{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "openl3-audio-api.ipynb", "provenance": [], "collapsed_sections": [], "machine_shape": "hm", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "metadata": { "id": "Z07UxtTm-kEn", "colab_type": "code", "colab": {} }, "source": [ "\"\"\"\n", "Description: The Openl3 based model for preprocessing, training, and predicting sentiment on EmotiW videos. \n", "Co-Authored by: Leila Abdelrhaman, Amil Khanzada, Tom Jin \n", "\n", "\"\"\"\n", "\n", "\n", "#TODO: There are some bugs here \n", "# Next Steps:\n", "# 1. Transfer learning\n", "# 2. Data Augmentation\n", "# 3. Look for other model architecture to use with openl3 features\n", "# 4. Look into fusing audio with video info (perhaps scene layer embeddings) through concat.\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "KPKtwAdgQzil", "colab_type": "text" }, "source": [ "# Audio API" ] }, { "cell_type": "code", "metadata": { "id": "nOSIfnb4PRuT", "colab_type": "code", "colab": {} }, "source": [ "class audio_model:\n", " def __init__(self):\n", " self.model = ()\n", " return\n", "\n", " def predict(self, mp4_filepath , X):\n", " soft_pred = self.model.predict(X)\n", " return soft_pred # ( M , n) where M is the number of samples and n is the number of features. \n", "\n", " def load_model(self, best_model_filepath):\n", " self.model = load_model(best_model_filepath)\n", " return \n", "\n", " def train(self, X_train, y_train, epochs=4000 , batch_size=32 , X_val=[None] , Y_val=[None] , val_split=0.1, save_path = None):\n", " \"\"\"\n", " Train function with the model architecture\n", "\n", " - Outputs\n", " 1. Trained model -- saves the model as a .h5 file to the specified path\n", "\n", " \"\"\"\n", "\n", " # ******** Keras Functional API ********\n", " inputs = keras.Input(shape=[None,6144])\n", "\n", " # CNN Portion\n", " x = tf.keras.layers.Conv1D(64, 2, activation='selu')(inputs) # This does convolves on the time domain. I.e. it is NOT time distributed. \n", " x = tf.keras.layers.BatchNormalization()(x)\n", " x = tf.keras.layers.MaxPooling1D(pool_size=2, strides=1, padding='valid')(x)\n", " x = tf.keras.layers.Dropout(0.4)(x)\n", " x = tf.keras.layers.Conv1D(512, 2, activation='selu')(x)\n", " x = tf.keras.layers.BatchNormalization()(x)\n", " x = tf.keras.layers.MaxPooling1D(pool_size=2, strides=1, padding='valid')(x)\n", " x = tf.keras.layers.Dropout(0.2)(x)\n", " x = tf.keras.layers.Conv1D(512, 2, activation='selu')(x)\n", " x = tf.keras.layers.BatchNormalization()(x)\n", " x = tf.keras.layers.MaxPooling1D(pool_size=2, strides=1, padding='valid')(x)\n", " x = tf.keras.layers.Dropout(0.2)(x)\n", " \n", " # Recurrent Portion\n", " x = tf.keras.layers.Bidirectional(keras.layers.LSTM(10, return_sequences=True, input_shape=[None, 6144] , dropout=0.2 , activation='selu'))(x)\n", " x = tf.keras.layers.Bidirectional(keras.layers.LSTM(5))(x)\n", " x = tf.keras.layers.Dense(32 , activation='selu')(x)\n", " x = tf.keras.layers.Dropout(0.4)(x) \n", " outputs = tf.keras.layers.Dense(3 , activation='softmax')(x)\n", "\n", " # Define Hyperparams and Compile\n", " lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n", " initial_learning_rate=1e-6,\n", " decay_steps=10000,\n", " decay_rate=0.9)\n", " rnn_ae = keras.Model(inputs=inputs, outputs=outputs)\n", " opt = keras.optimizers.Adam(learning_rate=lr_schedule)\n", " rnn_ae.compile(loss='sparse_categorical_crossentropy', optimizer=opt , metrics=['accuracy'])\n", "\n", " history = None \n", "\n", " if X_val.any == None or Y_val.any == None:\n", " es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=300)\n", " mc = ModelCheckpoint(save_path, monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)\n", " history = rnn_ae.fit(X_train , y_train , epochs=epochs , batch_size=batch_size, validation_split=val_split , callbacks=[es, mc])\n", " return rnn_ae , history\n", " \n", " es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=300)\n", " mc = ModelCheckpoint(save_path, monitor='val_acc', mode='max', verbose=1, save_best_only=True)\n", " history = rnn_ae.fit(X_train , y_train , epochs=epochs , batch_size=batch_size, validation_data=(X_val, Y_val), callbacks=[es, mc])\n", " return rnn_ae , history\n", "\n", " def data_generator(files, target_label_path, batch_size = 32):\n", " \"\"\"\n", " Can be used as input for the fit_generator or training_generator functions. \n", " \"\"\"\n", " \n", " while True:\n", " # Select files (paths/indices) for the batch\n", " batch_paths = np.random.choice(a = files, \n", " size = batch_size)\n", " batch_x , sr , batch_y = self.preprocess(batch_paths, target_label_path=target_label_path, hop_size=0.5 , max_vid_len = 5)\n", " \n", " \n", " \n", " # Return a tuple of (input, output) to feed the network\n", " batch_x = np.array(batch_x)\n", " batch_y = np.array(batch_y)\n", " \n", " yield( batch_x, batch_y )\n", "\n", "\n", " def fit_generator(self , training_generator , validation_generator):\n", "\n", " history = self.model.fit_generator(generator=training_generator,\n", " validation_data=validation_generator,\n", " use_multiprocessing=True,\n", " workers=6)\n", " \n", " return self.model, history\n", " \n", "\n", " def predict_generator(data_generator):\n", "\n", " # Other hyperparams are tuneable (i.e. num workers, etc.). \n", "\n", " return self.model.fit_generator(data_generator)\n", "\n", "\n", "\n", " def preprocess(self, mp4_file_dr, target_label_path=None, hop_size=0.5 , max_vid_len = 5):\n", " \"\"\"\n", " Inputs:\n", " - count_file_fp: contains a pos/ and neg/ folder\n", " - hop_size: The frame collection rate \n", "\n", " Outputs:\n", " - X_train: a numpy array with dimensions (m,n).\n", " - m is the units in time dependent on the audio splice rate.\n", " - n is the number of features from the audio library (openl3).\n", " - sr: sample rate\n", " - Y_train: labels\n", " \"\"\"\n", " \n", " # Strip the audio from video and store as .wav file\n", " video_files = sorted(glob.glob(mp4_file_dr + '/*.mp4'))\n", " targets = []\n", " target_labels = np.genfromtxt(target_label_path , delimiter = ' ' , dtype='str')\n", " \n", "\n", " for f in video_files: \n", " #print(f\"Processing file {f} ...\")\n", " newname = f.split(\"/\")[1]\n", " output_wav_file = newname + 'extracted_audio.wav'\n", " ffmpeg_extract_audio(f , output_wav_file)\n", " if target_label_path is not None: \n", " target_index = np.where(target_labels[: , 0] == newname[:-4])[0]\n", " target_index = int(target_index)\n", " target = int(target_labels[: , 1][target_index])\n", " targets.append(target)\n", " \n", " audio_files = sorted(glob.glob('*.wav'))\n", " audio_reads = []\n", " sr = 0\n", " for audio_file in audio_files:\n", " #print(f\"Reading file {audio_file} ...\")\n", " audio_read, sr = sf.read(audio_file)\n", " audio_reads.append(audio_read)\n", " targets = np.asarray(targets)\n", " targets -= 1\n", " X_arr, ts_list = openl3.get_audio_embedding(audio_reads, sr, batch_size=15 , hop_size=hop_size)\n", "\n", " subprocess.check_output(\"rm *.wav\", shell=True)\n", "\n", " maxlen = max_vid_len / hop_size + 1 \n", " X_train_padded = tf.keras.preprocessing.sequence.pad_sequences(X_arr , maxlen=maxlen)\n", " X_train_padded = np.asarray(X_train_padded , dtype='float32')\n", " \n", " return X_train_padded, sr, targets" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qY-WJbPaffR_", "colab_type": "text" }, "source": [ "# Helper Functions" ] }, { "cell_type": "code", "metadata": { "id": "G1FbruEBfenK", "colab_type": "code", "colab": {} }, "source": [ " def setup_raw_input_data():\n", " #Extract and preprocess X_train and Y_train from .mp4 file paths\n", " # You will have to change these paths depending on your dir structure in gdrive\n", " subprocess.check_output(\"cp '/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/datasets/emotiw/Train.zip' '/content/'\", shell=True)\n", " subprocess.check_output(\"unzip Train.zip\", shell=True)\n", " subprocess.check_output(\"mkdir to_zip\", shell = True)\n", "\n", " X_train_save_path = \"\" # Enter .pickle path\n", " Y_train_save_path = \"\" # Enter .pickle path\n", "\n", " subprocess.check_output(\"cp '/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/datasets/emotiw/Train_labels.txt' '/content/'\", shell=True)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "rjQNCO44fx_G", "colab_type": "code", "colab": {} }, "source": [ "def dump_x_y(X_train , X_train_save_path , Y_train , Y_train_save_path):\n", " with open(X_train_save_path, 'wb') as f:\n", " pickle.dump(X_train, f) \n", " with open(Y_train_save_path, 'wb') as f:\n", " pickle.dump(Y_train, f) " ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9KpdHhUwgqFE", "colab_type": "code", "colab": {} }, "source": [ "def load_example_data():\n", "\n", " # You will have to change these paths depending on your dir structure in gdrive\n", " X_train = np.load(\"/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/arrays/np_arrays/100ms-11-unit-seq-openl3.x_val-0.5-hop-ALL_TRAIN_SAMPLES.pickle\" , allow_pickle=True)\n", " Y_train = np.load(\"/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/arrays/np_arrays/100ms-11-unit-seq-openl3.y_train-0.5-hop-ALL_TRAIN_SAMPLES.pickle\", allow_pickle=True)\n", " X_val = np.load(\"/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/arrays/np_arrays/100ms-11-unit-seq-openl3.x_train-0.5-hop-ALL_VAL_SAMPLES.pickle\" , allow_pickle=True)\n", " Y_val = np.load(\"/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/arrays/np_arrays/100ms-11-unit-seq-openl3.y_val-0.5-hop-ALL_VAL_SAMPLES.pickle\", allow_pickle=True)\n", "\n", " print(X_train.shape)\n", " print(Y_train.shape)\n", " print(X_val.shape)\n", " print(Y_val.shape)\n", "\n", " return X_train , Y_train , X_val , Y_val" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "f2msWOkUhDSD", "colab_type": "code", "colab": {} }, "source": [ "def normalize(X_train , X_val):\n", " from sklearn.preprocessing import Normalizer\n", " X_train_copy = X_train\n", " X_val_copy = X_val\n", " scaler = Normalizer()\n", "\n", " for i in range(0,X_train_copy.shape[0]):\n", " X_train_copy[i] = scaler.fit_transform(X_train_copy[i])\n", "\n", " for i in range(0,X_val_copy.shape[0]):\n", " X_val_copy[i] = scaler.fit_transform(X_val_copy[i])\n", "\n", " return X_train_copy , X_val_copy" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "BCeIoJS2hktX", "colab_type": "code", "colab": {} }, "source": [ "def plot_history(history):\n", " hist = pd.DataFrame(history.history)\n", " hist['epoch'] = history.epoch\n", "\n", " plt.figure()\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Accuracy')\n", " plt.plot(hist['epoch'], hist['acc'],\n", " label='Train Accuracy')\n", " plt.plot(hist['epoch'], hist['val_acc'],\n", " label = 'Val Accuracy')\n", " plt.ylim([0,1])\n", " plt.legend()\n", " plt.show()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "C5lfxK8Nb5A8", "colab_type": "text" }, "source": [ "# Main Method" ] }, { "cell_type": "code", "metadata": { "id": "uBAbNurmQFAv", "colab_type": "code", "outputId": "bbe7ba8a-b0b6-47a0-b76b-56c99845111b", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "if __name__ == \"__main__\":\n", " %tensorflow_version 1\n", " import subprocess\n", "\n", " subprocess.check_output(\"pip install pydub\", shell=True)\n", " subprocess.check_output(\"pip install openl3\" , shell=True)\n", "\n", " from tensorflow.keras.models import load_model\n", " from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_audio\n", " import glob\n", " from sklearn.preprocessing import Normalizer\n", " import os\n", " import pickle\n", " import openl3\n", " import soundfile as sf\n", " import numpy as np\n", " import pandas as pd\n", " from matplotlib import pyplot as plt\n", " from pydub import AudioSegment\n", " import audioread\n", " import time\n", " import tensorflow as tf\n", " from tensorflow import keras\n", " from tensorflow.keras.callbacks import EarlyStopping\n", " from tensorflow.keras.callbacks import ModelCheckpoint\n", "\n", " # Mount Your Google Drive \n", " from google.colab import drive\n", " drive.mount('/content/gdrive' , force_remount=True)\n", "\n", "\n", " model_test = audio_model()\n", "\n", " # Preprocess --- NOTE: THIS REQUIRES A HIGH RAM RUNTIME... Even so, I recommend \n", " # breaking up your data into batches.... The runtime often crashes due to RAM \n", " # exhaustion. \n", "\n", " raw_mpr_dir = \"\" # The directory of all raw .mp4 files\n", " target_path = \"\" # The path to the target (y) .txt file\n", "\n", " # X_train , sr , Y_train = model_test.preprocess(raw_mpr_dir , target_path)\n", "\n", "\n", " # These .np arrays are already in preprocessed form at hop of 0.5.\n", "\n", " X_train , Y_train , X_val , Y_val = load_example_data()\n", "\n", " X_train_norm , X_val_norm = normalize(X_train, X_val)\n", "\n", " # MAKE SURE TO CHANGE THE SAVE PATH\n", "\n", " Model_path = \"/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\"\n", " model, history = model_test.train(X_train_norm, Y_train , X_val=X_val_norm , Y_val=Y_val , save_path=Model_path )\n", "\n", " plot_history(history)\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n", "You set: `1`. This will be interpreted as: `1.x`.\n", "\n", "\n", "TensorFlow is already loaded. Please restart the runtime to change versions.\n", "Mounted at /content/gdrive\n", "(2661, 11, 6144)\n", "(2661,)\n", "(766, 11, 6144)\n", "(766,)\n", "Train on 2661 samples, validate on 766 samples\n", "Epoch 1/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1528 - acc: 0.3268\n", "Epoch 00001: val_acc improved from -inf to 0.24282, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 9s 4ms/sample - loss: 1.1527 - acc: 0.3262 - val_loss: 1.1112 - val_acc: 0.2428\n", "Epoch 2/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1318 - acc: 0.3520\n", "Epoch 00002: val_acc did not improve from 0.24282\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1320 - acc: 0.3514 - val_loss: 1.1180 - val_acc: 0.2428\n", "Epoch 3/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1273 - acc: 0.3483\n", "Epoch 00003: val_acc did not improve from 0.24282\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1272 - acc: 0.3491 - val_loss: 1.1217 - val_acc: 0.2428\n", "Epoch 4/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1201 - acc: 0.3577\n", "Epoch 00004: val_acc did not improve from 0.24282\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1198 - acc: 0.3578 - val_loss: 1.1220 - val_acc: 0.2428\n", "Epoch 5/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1363 - acc: 0.3426\n", "Epoch 00005: val_acc improved from 0.24282 to 0.24935, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1363 - acc: 0.3424 - val_loss: 1.1194 - val_acc: 0.2493\n", "Epoch 6/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1208 - acc: 0.3494\n", "Epoch 00006: val_acc improved from 0.24935 to 0.25979, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1209 - acc: 0.3491 - val_loss: 1.1164 - val_acc: 0.2598\n", "Epoch 7/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1168 - acc: 0.3690\n", "Epoch 00007: val_acc improved from 0.25979 to 0.28460, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1169 - acc: 0.3690 - val_loss: 1.1125 - val_acc: 0.2846\n", "Epoch 8/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1093 - acc: 0.3701\n", "Epoch 00008: val_acc improved from 0.28460 to 0.30026, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1098 - acc: 0.3694 - val_loss: 1.1078 - val_acc: 0.3003\n", "Epoch 9/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1048 - acc: 0.3709\n", "Epoch 00009: val_acc improved from 0.30026 to 0.34726, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1049 - acc: 0.3709 - val_loss: 1.1029 - val_acc: 0.3473\n", "Epoch 10/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1188 - acc: 0.3697\n", "Epoch 00010: val_acc improved from 0.34726 to 0.36292, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1188 - acc: 0.3698 - val_loss: 1.0994 - val_acc: 0.3629\n", "Epoch 11/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1051 - acc: 0.3998\n", "Epoch 00011: val_acc did not improve from 0.36292\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1050 - acc: 0.4002 - val_loss: 1.0956 - val_acc: 0.3577\n", "Epoch 12/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0939 - acc: 0.3855\n", "Epoch 00012: val_acc did not improve from 0.36292\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0939 - acc: 0.3848 - val_loss: 1.0935 - val_acc: 0.3603\n", "Epoch 13/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1085 - acc: 0.3739\n", "Epoch 00013: val_acc improved from 0.36292 to 0.36554, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1086 - acc: 0.3743 - val_loss: 1.0900 - val_acc: 0.3655\n", "Epoch 14/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1034 - acc: 0.3844\n", "Epoch 00014: val_acc improved from 0.36554 to 0.38251, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1030 - acc: 0.3848 - val_loss: 1.0883 - val_acc: 0.3825\n", "Epoch 15/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1012 - acc: 0.3908\n", "Epoch 00015: val_acc improved from 0.38251 to 0.38903, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1011 - acc: 0.3912 - val_loss: 1.0854 - val_acc: 0.3890\n", "Epoch 16/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1025 - acc: 0.3904\n", "Epoch 00016: val_acc improved from 0.38903 to 0.39948, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.1028 - acc: 0.3901 - val_loss: 1.0835 - val_acc: 0.3995\n", "Epoch 17/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0905 - acc: 0.3980\n", "Epoch 00017: val_acc improved from 0.39948 to 0.40862, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0907 - acc: 0.3976 - val_loss: 1.0808 - val_acc: 0.4086\n", "Epoch 18/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0887 - acc: 0.4055\n", "Epoch 00018: val_acc improved from 0.40862 to 0.41253, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0892 - acc: 0.4055 - val_loss: 1.0801 - val_acc: 0.4125\n", "Epoch 19/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0951 - acc: 0.3897\n", "Epoch 00019: val_acc did not improve from 0.41253\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0955 - acc: 0.3890 - val_loss: 1.0787 - val_acc: 0.4086\n", "Epoch 20/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0973 - acc: 0.3931\n", "Epoch 00020: val_acc improved from 0.41253 to 0.41906, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0978 - acc: 0.3923 - val_loss: 1.0764 - val_acc: 0.4191\n", "Epoch 21/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0854 - acc: 0.4021\n", "Epoch 00021: val_acc did not improve from 0.41906\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0851 - acc: 0.4025 - val_loss: 1.0753 - val_acc: 0.4178\n", "Epoch 22/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0775 - acc: 0.4029\n", "Epoch 00022: val_acc improved from 0.41906 to 0.42820, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0772 - acc: 0.4036 - val_loss: 1.0735 - val_acc: 0.4282\n", "Epoch 23/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0934 - acc: 0.4036\n", "Epoch 00023: val_acc improved from 0.42820 to 0.43473, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0936 - acc: 0.4036 - val_loss: 1.0721 - val_acc: 0.4347\n", "Epoch 24/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0960 - acc: 0.4032\n", "Epoch 00024: val_acc did not improve from 0.43473\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0963 - acc: 0.4029 - val_loss: 1.0716 - val_acc: 0.4282\n", "Epoch 25/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0749 - acc: 0.4191\n", "Epoch 00025: val_acc did not improve from 0.43473\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0750 - acc: 0.4190 - val_loss: 1.0699 - val_acc: 0.4295\n", "Epoch 26/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0799 - acc: 0.4145\n", "Epoch 00026: val_acc improved from 0.43473 to 0.43864, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0799 - acc: 0.4149 - val_loss: 1.0676 - val_acc: 0.4386\n", "Epoch 27/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0773 - acc: 0.4266\n", "Epoch 00027: val_acc did not improve from 0.43864\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0773 - acc: 0.4269 - val_loss: 1.0674 - val_acc: 0.4386\n", "Epoch 28/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0742 - acc: 0.4194\n", "Epoch 00028: val_acc did not improve from 0.43864\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0743 - acc: 0.4194 - val_loss: 1.0657 - val_acc: 0.4386\n", "Epoch 29/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0703 - acc: 0.4262\n", "Epoch 00029: val_acc improved from 0.43864 to 0.43995, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0703 - acc: 0.4262 - val_loss: 1.0647 - val_acc: 0.4399\n", "Epoch 30/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0700 - acc: 0.4277\n", "Epoch 00030: val_acc improved from 0.43995 to 0.45039, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0697 - acc: 0.4280 - val_loss: 1.0629 - val_acc: 0.4504\n", "Epoch 31/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0754 - acc: 0.4277\n", "Epoch 00031: val_acc did not improve from 0.45039\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0750 - acc: 0.4277 - val_loss: 1.0621 - val_acc: 0.4491\n", "Epoch 32/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0718 - acc: 0.4209\n", "Epoch 00032: val_acc did not improve from 0.45039\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0716 - acc: 0.4205 - val_loss: 1.0614 - val_acc: 0.4465\n", "Epoch 33/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0648 - acc: 0.4450\n", "Epoch 00033: val_acc improved from 0.45039 to 0.45561, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 1.0650 - acc: 0.4449 - val_loss: 1.0599 - val_acc: 0.4556\n", "Epoch 34/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0693 - acc: 0.4300\n", "Epoch 00034: val_acc improved from 0.45561 to 0.45692, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0694 - acc: 0.4295 - val_loss: 1.0599 - val_acc: 0.4569\n", "Epoch 35/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0774 - acc: 0.4232\n", "Epoch 00035: val_acc did not improve from 0.45692\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0773 - acc: 0.4231 - val_loss: 1.0585 - val_acc: 0.4569\n", "Epoch 36/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0684 - acc: 0.4315\n", "Epoch 00036: val_acc improved from 0.45692 to 0.45822, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0681 - acc: 0.4318 - val_loss: 1.0576 - val_acc: 0.4582\n", "Epoch 37/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0597 - acc: 0.4300\n", "Epoch 00037: val_acc improved from 0.45822 to 0.45953, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0598 - acc: 0.4299 - val_loss: 1.0573 - val_acc: 0.4595\n", "Epoch 38/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0615 - acc: 0.4379\n", "Epoch 00038: val_acc did not improve from 0.45953\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0617 - acc: 0.4378 - val_loss: 1.0559 - val_acc: 0.4582\n", "Epoch 39/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0626 - acc: 0.4383\n", "Epoch 00039: val_acc did not improve from 0.45953\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0630 - acc: 0.4378 - val_loss: 1.0550 - val_acc: 0.4530\n", "Epoch 40/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0602 - acc: 0.4255\n", "Epoch 00040: val_acc did not improve from 0.45953\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0603 - acc: 0.4254 - val_loss: 1.0533 - val_acc: 0.4569\n", "Epoch 41/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0669 - acc: 0.4221\n", "Epoch 00041: val_acc did not improve from 0.45953\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0672 - acc: 0.4220 - val_loss: 1.0526 - val_acc: 0.4582\n", "Epoch 42/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0641 - acc: 0.4465\n", "Epoch 00042: val_acc did not improve from 0.45953\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0638 - acc: 0.4468 - val_loss: 1.0522 - val_acc: 0.4569\n", "Epoch 43/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0570 - acc: 0.4526\n", "Epoch 00043: val_acc improved from 0.45953 to 0.46475, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0573 - acc: 0.4525 - val_loss: 1.0515 - val_acc: 0.4648\n", "Epoch 44/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0622 - acc: 0.4469\n", "Epoch 00044: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0626 - acc: 0.4461 - val_loss: 1.0512 - val_acc: 0.4582\n", "Epoch 45/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0522 - acc: 0.4499\n", "Epoch 00045: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0519 - acc: 0.4502 - val_loss: 1.0513 - val_acc: 0.4556\n", "Epoch 46/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0483 - acc: 0.4567\n", "Epoch 00046: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0487 - acc: 0.4566 - val_loss: 1.0511 - val_acc: 0.4517\n", "Epoch 47/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0523 - acc: 0.4612\n", "Epoch 00047: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0530 - acc: 0.4607 - val_loss: 1.0513 - val_acc: 0.4491\n", "Epoch 48/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0600 - acc: 0.4405\n", "Epoch 00048: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0600 - acc: 0.4401 - val_loss: 1.0496 - val_acc: 0.4648\n", "Epoch 49/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0508 - acc: 0.4556\n", "Epoch 00049: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0513 - acc: 0.4551 - val_loss: 1.0495 - val_acc: 0.4608\n", "Epoch 50/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0533 - acc: 0.4458\n", "Epoch 00050: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0535 - acc: 0.4453 - val_loss: 1.0490 - val_acc: 0.4569\n", "Epoch 51/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0579 - acc: 0.4477\n", "Epoch 00051: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0580 - acc: 0.4476 - val_loss: 1.0485 - val_acc: 0.4569\n", "Epoch 52/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0432 - acc: 0.4752\n", "Epoch 00052: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0429 - acc: 0.4758 - val_loss: 1.0475 - val_acc: 0.4634\n", "Epoch 53/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0471 - acc: 0.4571\n", "Epoch 00053: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0475 - acc: 0.4562 - val_loss: 1.0476 - val_acc: 0.4595\n", "Epoch 54/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0510 - acc: 0.4507\n", "Epoch 00054: val_acc did not improve from 0.46475\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0514 - acc: 0.4506 - val_loss: 1.0469 - val_acc: 0.4648\n", "Epoch 55/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0440 - acc: 0.4639\n", "Epoch 00055: val_acc improved from 0.46475 to 0.46867, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0442 - acc: 0.4637 - val_loss: 1.0458 - val_acc: 0.4687\n", "Epoch 56/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0404 - acc: 0.4691\n", "Epoch 00056: val_acc improved from 0.46867 to 0.47128, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0404 - acc: 0.4690 - val_loss: 1.0453 - val_acc: 0.4713\n", "Epoch 57/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0480 - acc: 0.4575\n", "Epoch 00057: val_acc did not improve from 0.47128\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0480 - acc: 0.4577 - val_loss: 1.0453 - val_acc: 0.4634\n", "Epoch 58/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0512 - acc: 0.4661\n", "Epoch 00058: val_acc improved from 0.47128 to 0.47258, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0511 - acc: 0.4667 - val_loss: 1.0448 - val_acc: 0.4726\n", "Epoch 59/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0407 - acc: 0.4729\n", "Epoch 00059: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0408 - acc: 0.4724 - val_loss: 1.0449 - val_acc: 0.4687\n", "Epoch 60/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0451 - acc: 0.4612\n", "Epoch 00060: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0448 - acc: 0.4615 - val_loss: 1.0438 - val_acc: 0.4700\n", "Epoch 61/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0380 - acc: 0.4642\n", "Epoch 00061: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0375 - acc: 0.4649 - val_loss: 1.0442 - val_acc: 0.4621\n", "Epoch 62/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0376 - acc: 0.4710\n", "Epoch 00062: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0375 - acc: 0.4713 - val_loss: 1.0445 - val_acc: 0.4582\n", "Epoch 63/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0370 - acc: 0.4748\n", "Epoch 00063: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0372 - acc: 0.4743 - val_loss: 1.0444 - val_acc: 0.4648\n", "Epoch 64/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0371 - acc: 0.4684\n", "Epoch 00064: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0376 - acc: 0.4682 - val_loss: 1.0436 - val_acc: 0.4621\n", "Epoch 65/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0365 - acc: 0.4827\n", "Epoch 00065: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0364 - acc: 0.4829 - val_loss: 1.0418 - val_acc: 0.4713\n", "Epoch 66/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0341 - acc: 0.4789\n", "Epoch 00066: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0343 - acc: 0.4788 - val_loss: 1.0422 - val_acc: 0.4687\n", "Epoch 67/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0376 - acc: 0.4744\n", "Epoch 00067: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0374 - acc: 0.4746 - val_loss: 1.0423 - val_acc: 0.4713\n", "Epoch 68/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0400 - acc: 0.4680\n", "Epoch 00068: val_acc improved from 0.47258 to 0.47389, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0401 - acc: 0.4679 - val_loss: 1.0415 - val_acc: 0.4739\n", "Epoch 69/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0416 - acc: 0.4680\n", "Epoch 00069: val_acc did not improve from 0.47389\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0418 - acc: 0.4679 - val_loss: 1.0409 - val_acc: 0.4739\n", "Epoch 70/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0307 - acc: 0.4793\n", "Epoch 00070: val_acc improved from 0.47389 to 0.48172, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0309 - acc: 0.4795 - val_loss: 1.0409 - val_acc: 0.4817\n", "Epoch 71/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0310 - acc: 0.4838\n", "Epoch 00071: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0311 - acc: 0.4840 - val_loss: 1.0427 - val_acc: 0.4713\n", "Epoch 72/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0412 - acc: 0.4646\n", "Epoch 00072: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0412 - acc: 0.4645 - val_loss: 1.0419 - val_acc: 0.4726\n", "Epoch 73/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0296 - acc: 0.4789\n", "Epoch 00073: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0297 - acc: 0.4788 - val_loss: 1.0409 - val_acc: 0.4778\n", "Epoch 74/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0334 - acc: 0.4770\n", "Epoch 00074: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0336 - acc: 0.4769 - val_loss: 1.0418 - val_acc: 0.4687\n", "Epoch 75/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0351 - acc: 0.4752\n", "Epoch 00075: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0351 - acc: 0.4750 - val_loss: 1.0404 - val_acc: 0.4752\n", "Epoch 76/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0329 - acc: 0.4857\n", "Epoch 00076: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0320 - acc: 0.4867 - val_loss: 1.0404 - val_acc: 0.4791\n", "Epoch 77/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0291 - acc: 0.4785\n", "Epoch 00077: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0289 - acc: 0.4791 - val_loss: 1.0394 - val_acc: 0.4817\n", "Epoch 78/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0246 - acc: 0.4895\n", "Epoch 00078: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0248 - acc: 0.4897 - val_loss: 1.0402 - val_acc: 0.4739\n", "Epoch 79/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0340 - acc: 0.4834\n", "Epoch 00079: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0339 - acc: 0.4837 - val_loss: 1.0396 - val_acc: 0.4778\n", "Epoch 80/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0300 - acc: 0.4710\n", "Epoch 00080: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0302 - acc: 0.4709 - val_loss: 1.0402 - val_acc: 0.4804\n", "Epoch 81/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0301 - acc: 0.4744\n", "Epoch 00081: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0300 - acc: 0.4746 - val_loss: 1.0393 - val_acc: 0.4804\n", "Epoch 82/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0276 - acc: 0.4733\n", "Epoch 00082: val_acc improved from 0.48172 to 0.48303, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0274 - acc: 0.4735 - val_loss: 1.0385 - val_acc: 0.4830\n", "Epoch 83/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0301 - acc: 0.4853\n", "Epoch 00083: val_acc did not improve from 0.48303\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0303 - acc: 0.4852 - val_loss: 1.0390 - val_acc: 0.4765\n", "Epoch 84/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0238 - acc: 0.4846\n", "Epoch 00084: val_acc did not improve from 0.48303\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0242 - acc: 0.4844 - val_loss: 1.0387 - val_acc: 0.4817\n", "Epoch 85/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0202 - acc: 0.4966\n", "Epoch 00085: val_acc improved from 0.48303 to 0.48433, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 1.0210 - acc: 0.4957 - val_loss: 1.0377 - val_acc: 0.4843\n", "Epoch 86/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0192 - acc: 0.4902\n", "Epoch 00086: val_acc improved from 0.48433 to 0.48564, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0193 - acc: 0.4904 - val_loss: 1.0364 - val_acc: 0.4856\n", "Epoch 87/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0235 - acc: 0.4883\n", "Epoch 00087: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0236 - acc: 0.4882 - val_loss: 1.0361 - val_acc: 0.4843\n", "Epoch 88/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0176 - acc: 0.4932\n", "Epoch 00088: val_acc improved from 0.48564 to 0.48695, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0177 - acc: 0.4934 - val_loss: 1.0352 - val_acc: 0.4869\n", "Epoch 89/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0217 - acc: 0.4985\n", "Epoch 00089: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0217 - acc: 0.4983 - val_loss: 1.0349 - val_acc: 0.4869\n", "Epoch 90/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0212 - acc: 0.4921\n", "Epoch 00090: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0209 - acc: 0.4927 - val_loss: 1.0354 - val_acc: 0.4830\n", "Epoch 91/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0236 - acc: 0.4996\n", "Epoch 00091: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0238 - acc: 0.4991 - val_loss: 1.0353 - val_acc: 0.4856\n", "Epoch 92/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0089 - acc: 0.5000\n", "Epoch 00092: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0086 - acc: 0.5002 - val_loss: 1.0343 - val_acc: 0.4817\n", "Epoch 93/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0247 - acc: 0.4910\n", "Epoch 00093: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0252 - acc: 0.4900 - val_loss: 1.0339 - val_acc: 0.4830\n", "Epoch 94/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0212 - acc: 0.4887\n", "Epoch 00094: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0214 - acc: 0.4882 - val_loss: 1.0333 - val_acc: 0.4830\n", "Epoch 95/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0107 - acc: 0.5041\n", "Epoch 00095: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0106 - acc: 0.5039 - val_loss: 1.0324 - val_acc: 0.4869\n", "Epoch 96/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0167 - acc: 0.4966\n", "Epoch 00096: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0166 - acc: 0.4968 - val_loss: 1.0333 - val_acc: 0.4843\n", "Epoch 97/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0116 - acc: 0.5030\n", "Epoch 00097: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0119 - acc: 0.5028 - val_loss: 1.0334 - val_acc: 0.4830\n", "Epoch 98/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0182 - acc: 0.4823\n", "Epoch 00098: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0181 - acc: 0.4821 - val_loss: 1.0328 - val_acc: 0.4869\n", "Epoch 99/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0161 - acc: 0.4977\n", "Epoch 00099: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0162 - acc: 0.4976 - val_loss: 1.0330 - val_acc: 0.4856\n", "Epoch 100/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0150 - acc: 0.4834\n", "Epoch 00100: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0149 - acc: 0.4833 - val_loss: 1.0336 - val_acc: 0.4869\n", "Epoch 101/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0121 - acc: 0.5090\n", "Epoch 00101: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0121 - acc: 0.5088 - val_loss: 1.0343 - val_acc: 0.4856\n", "Epoch 102/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0092 - acc: 0.5034\n", "Epoch 00102: val_acc improved from 0.48695 to 0.48825, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0093 - acc: 0.5036 - val_loss: 1.0333 - val_acc: 0.4883\n", "Epoch 103/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0092 - acc: 0.5087\n", "Epoch 00103: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0099 - acc: 0.5085 - val_loss: 1.0337 - val_acc: 0.4843\n", "Epoch 104/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0089 - acc: 0.4917\n", "Epoch 00104: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0087 - acc: 0.4923 - val_loss: 1.0329 - val_acc: 0.4856\n", "Epoch 105/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0137 - acc: 0.5041\n", "Epoch 00105: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0134 - acc: 0.5051 - val_loss: 1.0334 - val_acc: 0.4856\n", "Epoch 106/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0095 - acc: 0.5162\n", "Epoch 00106: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0100 - acc: 0.5156 - val_loss: 1.0336 - val_acc: 0.4856\n", "Epoch 107/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0109 - acc: 0.4992\n", "Epoch 00107: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0104 - acc: 0.4998 - val_loss: 1.0328 - val_acc: 0.4830\n", "Epoch 108/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0036 - acc: 0.5102\n", "Epoch 00108: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0035 - acc: 0.5103 - val_loss: 1.0327 - val_acc: 0.4817\n", "Epoch 109/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0038 - acc: 0.5094\n", "Epoch 00109: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0039 - acc: 0.5092 - val_loss: 1.0321 - val_acc: 0.4843\n", "Epoch 110/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0012 - acc: 0.5128\n", "Epoch 00110: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0014 - acc: 0.5126 - val_loss: 1.0320 - val_acc: 0.4856\n", "Epoch 111/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0041 - acc: 0.5045\n", "Epoch 00111: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0037 - acc: 0.5047 - val_loss: 1.0319 - val_acc: 0.4843\n", "Epoch 112/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0072 - acc: 0.5053\n", "Epoch 00112: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0073 - acc: 0.5051 - val_loss: 1.0319 - val_acc: 0.4883\n", "Epoch 113/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0035 - acc: 0.4959\n", "Epoch 00113: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0032 - acc: 0.4961 - val_loss: 1.0318 - val_acc: 0.4856\n", "Epoch 114/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0110 - acc: 0.5023\n", "Epoch 00114: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0107 - acc: 0.5024 - val_loss: 1.0309 - val_acc: 0.4869\n", "Epoch 115/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0017 - acc: 0.5128\n", "Epoch 00115: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0018 - acc: 0.5130 - val_loss: 1.0315 - val_acc: 0.4883\n", "Epoch 116/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0099 - acc: 0.5158\n", "Epoch 00116: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0095 - acc: 0.5163 - val_loss: 1.0326 - val_acc: 0.4883\n", "Epoch 117/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0035 - acc: 0.5098\n", "Epoch 00117: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0034 - acc: 0.5096 - val_loss: 1.0313 - val_acc: 0.4883\n", "Epoch 118/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0009 - acc: 0.5064\n", "Epoch 00118: val_acc improved from 0.48825 to 0.49086, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0012 - acc: 0.5058 - val_loss: 1.0312 - val_acc: 0.4909\n", "Epoch 119/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0006 - acc: 0.5128\n", "Epoch 00119: val_acc did not improve from 0.49086\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0009 - acc: 0.5130 - val_loss: 1.0320 - val_acc: 0.4896\n", "Epoch 120/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9979 - acc: 0.5136\n", "Epoch 00120: val_acc improved from 0.49086 to 0.49217, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9981 - acc: 0.5133 - val_loss: 1.0309 - val_acc: 0.4922\n", "Epoch 121/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0002 - acc: 0.5136\n", "Epoch 00121: val_acc improved from 0.49217 to 0.49347, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0000 - acc: 0.5133 - val_loss: 1.0308 - val_acc: 0.4935\n", "Epoch 122/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9996 - acc: 0.5132\n", "Epoch 00122: val_acc did not improve from 0.49347\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9996 - acc: 0.5137 - val_loss: 1.0306 - val_acc: 0.4935\n", "Epoch 123/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9890 - acc: 0.5312\n", "Epoch 00123: val_acc did not improve from 0.49347\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9885 - acc: 0.5318 - val_loss: 1.0309 - val_acc: 0.4909\n", "Epoch 124/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9980 - acc: 0.5136\n", "Epoch 00124: val_acc did not improve from 0.49347\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9989 - acc: 0.5133 - val_loss: 1.0304 - val_acc: 0.4909\n", "Epoch 125/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9945 - acc: 0.5233\n", "Epoch 00125: val_acc did not improve from 0.49347\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9943 - acc: 0.5239 - val_loss: 1.0314 - val_acc: 0.4909\n", "Epoch 126/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9998 - acc: 0.4989\n", "Epoch 00126: val_acc improved from 0.49347 to 0.50000, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0001 - acc: 0.4987 - val_loss: 1.0302 - val_acc: 0.5000\n", "Epoch 127/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9906 - acc: 0.5237\n", "Epoch 00127: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9906 - acc: 0.5239 - val_loss: 1.0302 - val_acc: 0.5000\n", "Epoch 128/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0023 - acc: 0.5162\n", "Epoch 00128: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0020 - acc: 0.5163 - val_loss: 1.0300 - val_acc: 0.4974\n", "Epoch 129/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9922 - acc: 0.5230\n", "Epoch 00129: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9924 - acc: 0.5227 - val_loss: 1.0298 - val_acc: 0.4987\n", "Epoch 130/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9863 - acc: 0.5305\n", "Epoch 00130: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9865 - acc: 0.5303 - val_loss: 1.0305 - val_acc: 0.4961\n", "Epoch 131/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9973 - acc: 0.5128\n", "Epoch 00131: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9978 - acc: 0.5122 - val_loss: 1.0305 - val_acc: 0.4961\n", "Epoch 132/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9926 - acc: 0.5147\n", "Epoch 00132: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9928 - acc: 0.5145 - val_loss: 1.0297 - val_acc: 0.4987\n", "Epoch 133/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9904 - acc: 0.5196\n", "Epoch 00133: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9909 - acc: 0.5190 - val_loss: 1.0300 - val_acc: 0.4987\n", "Epoch 134/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9953 - acc: 0.5230\n", "Epoch 00134: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9953 - acc: 0.5227 - val_loss: 1.0295 - val_acc: 0.4961\n", "Epoch 135/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0007 - acc: 0.5184\n", "Epoch 00135: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0005 - acc: 0.5186 - val_loss: 1.0302 - val_acc: 0.4974\n", "Epoch 136/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0018 - acc: 0.5207\n", "Epoch 00136: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 1.0022 - acc: 0.5205 - val_loss: 1.0286 - val_acc: 0.4987\n", "Epoch 137/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9827 - acc: 0.5354\n", "Epoch 00137: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9825 - acc: 0.5351 - val_loss: 1.0287 - val_acc: 0.5000\n", "Epoch 138/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9914 - acc: 0.5264\n", "Epoch 00138: val_acc improved from 0.50000 to 0.50131, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9912 - acc: 0.5269 - val_loss: 1.0288 - val_acc: 0.5013\n", "Epoch 139/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9904 - acc: 0.5339\n", "Epoch 00139: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9909 - acc: 0.5329 - val_loss: 1.0281 - val_acc: 0.5013\n", "Epoch 140/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9903 - acc: 0.5264\n", "Epoch 00140: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9907 - acc: 0.5265 - val_loss: 1.0278 - val_acc: 0.4987\n", "Epoch 141/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9944 - acc: 0.5335\n", "Epoch 00141: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9940 - acc: 0.5340 - val_loss: 1.0284 - val_acc: 0.4974\n", "Epoch 142/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9951 - acc: 0.5226\n", "Epoch 00142: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9957 - acc: 0.5224 - val_loss: 1.0282 - val_acc: 0.4987\n", "Epoch 143/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9820 - acc: 0.5399\n", "Epoch 00143: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9821 - acc: 0.5396 - val_loss: 1.0279 - val_acc: 0.5013\n", "Epoch 144/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9850 - acc: 0.5320\n", "Epoch 00144: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9852 - acc: 0.5318 - val_loss: 1.0287 - val_acc: 0.5000\n", "Epoch 145/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9867 - acc: 0.5350\n", "Epoch 00145: val_acc improved from 0.50131 to 0.50522, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9862 - acc: 0.5351 - val_loss: 1.0289 - val_acc: 0.5052\n", "Epoch 146/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9887 - acc: 0.5410\n", "Epoch 00146: val_acc did not improve from 0.50522\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9891 - acc: 0.5411 - val_loss: 1.0298 - val_acc: 0.4987\n", "Epoch 147/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9838 - acc: 0.5384\n", "Epoch 00147: val_acc did not improve from 0.50522\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9840 - acc: 0.5381 - val_loss: 1.0301 - val_acc: 0.5013\n", "Epoch 148/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9829 - acc: 0.5433\n", "Epoch 00148: val_acc did not improve from 0.50522\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9835 - acc: 0.5423 - val_loss: 1.0290 - val_acc: 0.5000\n", "Epoch 149/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9919 - acc: 0.5237\n", "Epoch 00149: val_acc did not improve from 0.50522\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9922 - acc: 0.5227 - val_loss: 1.0292 - val_acc: 0.5026\n", "Epoch 150/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9848 - acc: 0.5388\n", "Epoch 00150: val_acc did not improve from 0.50522\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9854 - acc: 0.5381 - val_loss: 1.0288 - val_acc: 0.4987\n", "Epoch 151/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9822 - acc: 0.5418\n", "Epoch 00151: val_acc did not improve from 0.50522\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9818 - acc: 0.5423 - val_loss: 1.0294 - val_acc: 0.5039\n", "Epoch 152/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9847 - acc: 0.5339\n", "Epoch 00152: val_acc improved from 0.50522 to 0.50653, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 0.9843 - acc: 0.5344 - val_loss: 1.0299 - val_acc: 0.5065\n", "Epoch 153/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9737 - acc: 0.5335\n", "Epoch 00153: val_acc did not improve from 0.50653\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9737 - acc: 0.5336 - val_loss: 1.0291 - val_acc: 0.5052\n", "Epoch 154/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9898 - acc: 0.5143\n", "Epoch 00154: val_acc improved from 0.50653 to 0.50783, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9897 - acc: 0.5148 - val_loss: 1.0280 - val_acc: 0.5078\n", "Epoch 155/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9832 - acc: 0.5410\n", "Epoch 00155: val_acc improved from 0.50783 to 0.51044, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9833 - acc: 0.5404 - val_loss: 1.0270 - val_acc: 0.5104\n", "Epoch 156/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9784 - acc: 0.5275\n", "Epoch 00156: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9783 - acc: 0.5276 - val_loss: 1.0274 - val_acc: 0.5065\n", "Epoch 157/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9817 - acc: 0.5377\n", "Epoch 00157: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9815 - acc: 0.5381 - val_loss: 1.0288 - val_acc: 0.5078\n", "Epoch 158/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9850 - acc: 0.5388\n", "Epoch 00158: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9855 - acc: 0.5381 - val_loss: 1.0286 - val_acc: 0.5104\n", "Epoch 159/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9775 - acc: 0.5369\n", "Epoch 00159: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9775 - acc: 0.5370 - val_loss: 1.0282 - val_acc: 0.5065\n", "Epoch 160/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9726 - acc: 0.5478\n", "Epoch 00160: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9725 - acc: 0.5479 - val_loss: 1.0283 - val_acc: 0.5039\n", "Epoch 161/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9725 - acc: 0.5493\n", "Epoch 00161: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9724 - acc: 0.5494 - val_loss: 1.0285 - val_acc: 0.5039\n", "Epoch 162/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9757 - acc: 0.5297\n", "Epoch 00162: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9753 - acc: 0.5306 - val_loss: 1.0278 - val_acc: 0.5078\n", "Epoch 163/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9771 - acc: 0.5365\n", "Epoch 00163: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9776 - acc: 0.5363 - val_loss: 1.0290 - val_acc: 0.5039\n", "Epoch 164/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9793 - acc: 0.5301\n", "Epoch 00164: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9791 - acc: 0.5299 - val_loss: 1.0294 - val_acc: 0.5052\n", "Epoch 165/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9805 - acc: 0.5361\n", "Epoch 00165: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9807 - acc: 0.5359 - val_loss: 1.0293 - val_acc: 0.5065\n", "Epoch 166/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9750 - acc: 0.5486\n", "Epoch 00166: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9752 - acc: 0.5483 - val_loss: 1.0284 - val_acc: 0.5091\n", "Epoch 167/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9821 - acc: 0.5437\n", "Epoch 00167: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9825 - acc: 0.5434 - val_loss: 1.0276 - val_acc: 0.5026\n", "Epoch 168/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9712 - acc: 0.5550\n", "Epoch 00168: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9712 - acc: 0.5551 - val_loss: 1.0274 - val_acc: 0.5026\n", "Epoch 169/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9680 - acc: 0.5546\n", "Epoch 00169: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9686 - acc: 0.5539 - val_loss: 1.0285 - val_acc: 0.5000\n", "Epoch 170/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9757 - acc: 0.5312\n", "Epoch 00170: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9757 - acc: 0.5310 - val_loss: 1.0287 - val_acc: 0.5013\n", "Epoch 171/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9733 - acc: 0.5343\n", "Epoch 00171: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9730 - acc: 0.5348 - val_loss: 1.0289 - val_acc: 0.5013\n", "Epoch 172/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9842 - acc: 0.5373\n", "Epoch 00172: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9841 - acc: 0.5374 - val_loss: 1.0282 - val_acc: 0.5039\n", "Epoch 173/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9826 - acc: 0.5425\n", "Epoch 00173: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9828 - acc: 0.5423 - val_loss: 1.0280 - val_acc: 0.5039\n", "Epoch 174/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9803 - acc: 0.5414\n", "Epoch 00174: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9800 - acc: 0.5419 - val_loss: 1.0280 - val_acc: 0.5065\n", "Epoch 175/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9574 - acc: 0.5640\n", "Epoch 00175: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9580 - acc: 0.5629 - val_loss: 1.0280 - val_acc: 0.5078\n", "Epoch 176/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9655 - acc: 0.5452\n", "Epoch 00176: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9657 - acc: 0.5449 - val_loss: 1.0290 - val_acc: 0.5065\n", "Epoch 177/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9756 - acc: 0.5489\n", "Epoch 00177: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9755 - acc: 0.5490 - val_loss: 1.0285 - val_acc: 0.5026\n", "Epoch 178/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9826 - acc: 0.5444\n", "Epoch 00178: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9825 - acc: 0.5449 - val_loss: 1.0279 - val_acc: 0.5078\n", "Epoch 179/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9563 - acc: 0.5595\n", "Epoch 00179: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9568 - acc: 0.5588 - val_loss: 1.0283 - val_acc: 0.5013\n", "Epoch 180/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9622 - acc: 0.5523\n", "Epoch 00180: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9616 - acc: 0.5532 - val_loss: 1.0281 - val_acc: 0.5065\n", "Epoch 181/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9799 - acc: 0.5328\n", "Epoch 00181: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9797 - acc: 0.5329 - val_loss: 1.0275 - val_acc: 0.5065\n", "Epoch 182/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9666 - acc: 0.5482\n", "Epoch 00182: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9668 - acc: 0.5483 - val_loss: 1.0269 - val_acc: 0.5013\n", "Epoch 183/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9657 - acc: 0.5452\n", "Epoch 00183: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9660 - acc: 0.5445 - val_loss: 1.0269 - val_acc: 0.5091\n", "Epoch 184/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9682 - acc: 0.5501\n", "Epoch 00184: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9683 - acc: 0.5498 - val_loss: 1.0271 - val_acc: 0.5091\n", "Epoch 185/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9626 - acc: 0.5599\n", "Epoch 00185: val_acc improved from 0.51044 to 0.51175, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 0.9629 - acc: 0.5596 - val_loss: 1.0259 - val_acc: 0.5117\n", "Epoch 186/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9618 - acc: 0.5505\n", "Epoch 00186: val_acc did not improve from 0.51175\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9616 - acc: 0.5502 - val_loss: 1.0257 - val_acc: 0.5104\n", "Epoch 187/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9717 - acc: 0.5474\n", "Epoch 00187: val_acc improved from 0.51175 to 0.51436, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9720 - acc: 0.5472 - val_loss: 1.0255 - val_acc: 0.5144\n", "Epoch 188/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9659 - acc: 0.5467\n", "Epoch 00188: val_acc did not improve from 0.51436\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9667 - acc: 0.5457 - val_loss: 1.0252 - val_acc: 0.5131\n", "Epoch 189/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9636 - acc: 0.5467\n", "Epoch 00189: val_acc did not improve from 0.51436\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9639 - acc: 0.5468 - val_loss: 1.0255 - val_acc: 0.5131\n", "Epoch 190/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9844 - acc: 0.5454\n", "Epoch 00190: val_acc did not improve from 0.51436\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9842 - acc: 0.5445 - val_loss: 1.0268 - val_acc: 0.5104\n", "Epoch 191/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9628 - acc: 0.5444\n", "Epoch 00191: val_acc did not improve from 0.51436\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9630 - acc: 0.5442 - val_loss: 1.0258 - val_acc: 0.5131\n", "Epoch 192/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9599 - acc: 0.5704\n", "Epoch 00192: val_acc improved from 0.51436 to 0.51828, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 0.9602 - acc: 0.5701 - val_loss: 1.0254 - val_acc: 0.5183\n", "Epoch 193/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9708 - acc: 0.5531\n", "Epoch 00193: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9709 - acc: 0.5532 - val_loss: 1.0256 - val_acc: 0.5157\n", "Epoch 194/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9687 - acc: 0.5422\n", "Epoch 00194: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9685 - acc: 0.5423 - val_loss: 1.0254 - val_acc: 0.5117\n", "Epoch 195/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9676 - acc: 0.5505\n", "Epoch 00195: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9680 - acc: 0.5502 - val_loss: 1.0257 - val_acc: 0.5117\n", "Epoch 196/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9526 - acc: 0.5700\n", "Epoch 00196: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9522 - acc: 0.5705 - val_loss: 1.0268 - val_acc: 0.5131\n", "Epoch 197/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9626 - acc: 0.5437\n", "Epoch 00197: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9624 - acc: 0.5438 - val_loss: 1.0273 - val_acc: 0.5170\n", "Epoch 198/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9672 - acc: 0.5520\n", "Epoch 00198: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9671 - acc: 0.5520 - val_loss: 1.0263 - val_acc: 0.5131\n", "Epoch 199/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9659 - acc: 0.5584\n", "Epoch 00199: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9667 - acc: 0.5577 - val_loss: 1.0263 - val_acc: 0.5131\n", "Epoch 200/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9559 - acc: 0.5659\n", "Epoch 00200: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9559 - acc: 0.5660 - val_loss: 1.0268 - val_acc: 0.5144\n", "Epoch 201/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9549 - acc: 0.5655\n", "Epoch 00201: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9549 - acc: 0.5652 - val_loss: 1.0266 - val_acc: 0.5183\n", "Epoch 202/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9669 - acc: 0.5569\n", "Epoch 00202: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9671 - acc: 0.5566 - val_loss: 1.0271 - val_acc: 0.5131\n", "Epoch 203/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9517 - acc: 0.5599\n", "Epoch 00203: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9527 - acc: 0.5588 - val_loss: 1.0275 - val_acc: 0.5117\n", "Epoch 204/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9535 - acc: 0.5730\n", "Epoch 00204: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9541 - acc: 0.5731 - val_loss: 1.0274 - val_acc: 0.5144\n", "Epoch 205/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9556 - acc: 0.5595\n", "Epoch 00205: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9556 - acc: 0.5596 - val_loss: 1.0268 - val_acc: 0.5170\n", "Epoch 206/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9583 - acc: 0.5572\n", "Epoch 00206: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9587 - acc: 0.5569 - val_loss: 1.0268 - val_acc: 0.5170\n", "Epoch 207/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9484 - acc: 0.5629\n", "Epoch 00207: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9486 - acc: 0.5626 - val_loss: 1.0265 - val_acc: 0.5183\n", "Epoch 208/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9591 - acc: 0.5572\n", "Epoch 00208: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9595 - acc: 0.5573 - val_loss: 1.0270 - val_acc: 0.5104\n", "Epoch 209/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9548 - acc: 0.5640\n", "Epoch 00209: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9554 - acc: 0.5633 - val_loss: 1.0282 - val_acc: 0.5104\n", "Epoch 210/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9490 - acc: 0.5621\n", "Epoch 00210: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9489 - acc: 0.5622 - val_loss: 1.0279 - val_acc: 0.5131\n", "Epoch 211/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9509 - acc: 0.5742\n", "Epoch 00211: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9514 - acc: 0.5742 - val_loss: 1.0281 - val_acc: 0.5144\n", "Epoch 212/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9477 - acc: 0.5610\n", "Epoch 00212: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9477 - acc: 0.5611 - val_loss: 1.0278 - val_acc: 0.5144\n", "Epoch 213/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9466 - acc: 0.5648\n", "Epoch 00213: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9464 - acc: 0.5648 - val_loss: 1.0278 - val_acc: 0.5157\n", "Epoch 214/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9436 - acc: 0.5727\n", "Epoch 00214: val_acc did not improve from 0.51828\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9440 - acc: 0.5720 - val_loss: 1.0275 - val_acc: 0.5183\n", "Epoch 215/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9575 - acc: 0.5580\n", "Epoch 00215: val_acc improved from 0.51828 to 0.51958, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 0.9572 - acc: 0.5584 - val_loss: 1.0266 - val_acc: 0.5196\n", "Epoch 216/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9513 - acc: 0.5614\n", "Epoch 00216: val_acc improved from 0.51958 to 0.52350, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9517 - acc: 0.5611 - val_loss: 1.0260 - val_acc: 0.5235\n", "Epoch 217/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9518 - acc: 0.5742\n", "Epoch 00217: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9519 - acc: 0.5742 - val_loss: 1.0267 - val_acc: 0.5144\n", "Epoch 218/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9517 - acc: 0.5621\n", "Epoch 00218: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9512 - acc: 0.5629 - val_loss: 1.0266 - val_acc: 0.5144\n", "Epoch 219/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9543 - acc: 0.5685\n", "Epoch 00219: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9549 - acc: 0.5675 - val_loss: 1.0264 - val_acc: 0.5209\n", "Epoch 220/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9398 - acc: 0.5727\n", "Epoch 00220: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9398 - acc: 0.5727 - val_loss: 1.0259 - val_acc: 0.5183\n", "Epoch 221/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9477 - acc: 0.5610\n", "Epoch 00221: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9478 - acc: 0.5607 - val_loss: 1.0259 - val_acc: 0.5209\n", "Epoch 222/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9438 - acc: 0.5809\n", "Epoch 00222: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9447 - acc: 0.5802 - val_loss: 1.0256 - val_acc: 0.5196\n", "Epoch 223/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9446 - acc: 0.5625\n", "Epoch 00223: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9454 - acc: 0.5618 - val_loss: 1.0267 - val_acc: 0.5235\n", "Epoch 224/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9470 - acc: 0.5606\n", "Epoch 00224: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9465 - acc: 0.5614 - val_loss: 1.0261 - val_acc: 0.5222\n", "Epoch 225/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9462 - acc: 0.5697\n", "Epoch 00225: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9462 - acc: 0.5693 - val_loss: 1.0263 - val_acc: 0.5222\n", "Epoch 226/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9510 - acc: 0.5640\n", "Epoch 00226: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9508 - acc: 0.5637 - val_loss: 1.0271 - val_acc: 0.5196\n", "Epoch 227/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9446 - acc: 0.5674\n", "Epoch 00227: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9446 - acc: 0.5675 - val_loss: 1.0271 - val_acc: 0.5170\n", "Epoch 228/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9406 - acc: 0.5802\n", "Epoch 00228: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9407 - acc: 0.5799 - val_loss: 1.0259 - val_acc: 0.5222\n", "Epoch 229/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9325 - acc: 0.5862\n", "Epoch 00229: val_acc did not improve from 0.52350\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9323 - acc: 0.5862 - val_loss: 1.0258 - val_acc: 0.5222\n", "Epoch 230/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9483 - acc: 0.5800\n", "Epoch 00230: val_acc improved from 0.52350 to 0.52480, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9480 - acc: 0.5814 - val_loss: 1.0245 - val_acc: 0.5248\n", "Epoch 231/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9440 - acc: 0.5772\n", "Epoch 00231: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9440 - acc: 0.5769 - val_loss: 1.0248 - val_acc: 0.5235\n", "Epoch 232/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9421 - acc: 0.5821\n", "Epoch 00232: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9418 - acc: 0.5825 - val_loss: 1.0250 - val_acc: 0.5196\n", "Epoch 233/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9430 - acc: 0.5629\n", "Epoch 00233: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9430 - acc: 0.5629 - val_loss: 1.0254 - val_acc: 0.5144\n", "Epoch 234/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9374 - acc: 0.5712\n", "Epoch 00234: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9376 - acc: 0.5708 - val_loss: 1.0256 - val_acc: 0.5131\n", "Epoch 235/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9378 - acc: 0.5798\n", "Epoch 00235: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9376 - acc: 0.5799 - val_loss: 1.0254 - val_acc: 0.5157\n", "Epoch 236/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9391 - acc: 0.5836\n", "Epoch 00236: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9391 - acc: 0.5840 - val_loss: 1.0250 - val_acc: 0.5170\n", "Epoch 237/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9376 - acc: 0.5749\n", "Epoch 00237: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9371 - acc: 0.5753 - val_loss: 1.0255 - val_acc: 0.5170\n", "Epoch 238/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9339 - acc: 0.5757\n", "Epoch 00238: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9343 - acc: 0.5753 - val_loss: 1.0249 - val_acc: 0.5196\n", "Epoch 239/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9323 - acc: 0.5821\n", "Epoch 00239: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9325 - acc: 0.5821 - val_loss: 1.0248 - val_acc: 0.5196\n", "Epoch 240/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9369 - acc: 0.5727\n", "Epoch 00240: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9364 - acc: 0.5735 - val_loss: 1.0251 - val_acc: 0.5183\n", "Epoch 241/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9361 - acc: 0.5719\n", "Epoch 00241: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9362 - acc: 0.5712 - val_loss: 1.0256 - val_acc: 0.5170\n", "Epoch 242/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9314 - acc: 0.5832\n", "Epoch 00242: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9310 - acc: 0.5836 - val_loss: 1.0250 - val_acc: 0.5157\n", "Epoch 243/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9307 - acc: 0.5900\n", "Epoch 00243: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9306 - acc: 0.5900 - val_loss: 1.0255 - val_acc: 0.5157\n", "Epoch 244/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9371 - acc: 0.5877\n", "Epoch 00244: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9373 - acc: 0.5877 - val_loss: 1.0246 - val_acc: 0.5144\n", "Epoch 245/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9298 - acc: 0.5926\n", "Epoch 00245: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9298 - acc: 0.5923 - val_loss: 1.0237 - val_acc: 0.5209\n", "Epoch 246/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9376 - acc: 0.5757\n", "Epoch 00246: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9383 - acc: 0.5750 - val_loss: 1.0243 - val_acc: 0.5235\n", "Epoch 247/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9391 - acc: 0.5685\n", "Epoch 00247: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9394 - acc: 0.5686 - val_loss: 1.0236 - val_acc: 0.5209\n", "Epoch 248/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9205 - acc: 0.5877\n", "Epoch 00248: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9201 - acc: 0.5885 - val_loss: 1.0240 - val_acc: 0.5157\n", "Epoch 249/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9311 - acc: 0.5749\n", "Epoch 00249: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9314 - acc: 0.5742 - val_loss: 1.0235 - val_acc: 0.5235\n", "Epoch 250/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9440 - acc: 0.5708\n", "Epoch 00250: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9444 - acc: 0.5697 - val_loss: 1.0242 - val_acc: 0.5170\n", "Epoch 251/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9313 - acc: 0.5813\n", "Epoch 00251: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9314 - acc: 0.5810 - val_loss: 1.0245 - val_acc: 0.5157\n", "Epoch 252/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9265 - acc: 0.5847\n", "Epoch 00252: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9267 - acc: 0.5847 - val_loss: 1.0252 - val_acc: 0.5157\n", "Epoch 253/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9348 - acc: 0.5776\n", "Epoch 00253: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9347 - acc: 0.5780 - val_loss: 1.0259 - val_acc: 0.5170\n", "Epoch 254/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9401 - acc: 0.5697\n", "Epoch 00254: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9406 - acc: 0.5693 - val_loss: 1.0250 - val_acc: 0.5170\n", "Epoch 255/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9241 - acc: 0.5870\n", "Epoch 00255: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9248 - acc: 0.5862 - val_loss: 1.0251 - val_acc: 0.5131\n", "Epoch 256/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9289 - acc: 0.5779\n", "Epoch 00256: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9289 - acc: 0.5784 - val_loss: 1.0247 - val_acc: 0.5104\n", "Epoch 257/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9322 - acc: 0.5802\n", "Epoch 00257: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9317 - acc: 0.5806 - val_loss: 1.0246 - val_acc: 0.5131\n", "Epoch 258/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9223 - acc: 0.5915\n", "Epoch 00258: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9222 - acc: 0.5915 - val_loss: 1.0237 - val_acc: 0.5209\n", "Epoch 259/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9230 - acc: 0.5900\n", "Epoch 00259: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9228 - acc: 0.5908 - val_loss: 1.0241 - val_acc: 0.5222\n", "Epoch 260/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9240 - acc: 0.5941\n", "Epoch 00260: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9239 - acc: 0.5945 - val_loss: 1.0249 - val_acc: 0.5196\n", "Epoch 261/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9262 - acc: 0.5941\n", "Epoch 00261: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9262 - acc: 0.5941 - val_loss: 1.0251 - val_acc: 0.5222\n", "Epoch 262/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9231 - acc: 0.5840\n", "Epoch 00262: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9227 - acc: 0.5840 - val_loss: 1.0255 - val_acc: 0.5222\n", "Epoch 263/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9208 - acc: 0.5881\n", "Epoch 00263: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9208 - acc: 0.5877 - val_loss: 1.0258 - val_acc: 0.5196\n", "Epoch 264/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9265 - acc: 0.5934\n", "Epoch 00264: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9269 - acc: 0.5930 - val_loss: 1.0247 - val_acc: 0.5209\n", "Epoch 265/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9245 - acc: 0.5983\n", "Epoch 00265: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9246 - acc: 0.5983 - val_loss: 1.0253 - val_acc: 0.5183\n", "Epoch 266/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9231 - acc: 0.5892\n", "Epoch 00266: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9233 - acc: 0.5885 - val_loss: 1.0264 - val_acc: 0.5209\n", "Epoch 267/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9249 - acc: 0.5896\n", "Epoch 00267: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9251 - acc: 0.5900 - val_loss: 1.0261 - val_acc: 0.5157\n", "Epoch 268/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9108 - acc: 0.5953\n", "Epoch 00268: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9108 - acc: 0.5953 - val_loss: 1.0244 - val_acc: 0.5209\n", "Epoch 269/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9119 - acc: 0.6047\n", "Epoch 00269: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9119 - acc: 0.6047 - val_loss: 1.0254 - val_acc: 0.5157\n", "Epoch 270/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9139 - acc: 0.6017\n", "Epoch 00270: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9135 - acc: 0.6024 - val_loss: 1.0268 - val_acc: 0.5144\n", "Epoch 271/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9132 - acc: 0.6009\n", "Epoch 00271: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9125 - acc: 0.6017 - val_loss: 1.0265 - val_acc: 0.5117\n", "Epoch 272/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9132 - acc: 0.6032\n", "Epoch 00272: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9139 - acc: 0.6028 - val_loss: 1.0265 - val_acc: 0.5183\n", "Epoch 273/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9229 - acc: 0.5975\n", "Epoch 00273: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9231 - acc: 0.5975 - val_loss: 1.0266 - val_acc: 0.5131\n", "Epoch 274/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9175 - acc: 0.6017\n", "Epoch 00274: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9171 - acc: 0.6020 - val_loss: 1.0265 - val_acc: 0.5117\n", "Epoch 275/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9191 - acc: 0.6084\n", "Epoch 00275: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9190 - acc: 0.6084 - val_loss: 1.0259 - val_acc: 0.5117\n", "Epoch 276/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9156 - acc: 0.6017\n", "Epoch 00276: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9152 - acc: 0.6024 - val_loss: 1.0262 - val_acc: 0.5144\n", "Epoch 277/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9253 - acc: 0.6017\n", "Epoch 00277: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9256 - acc: 0.6013 - val_loss: 1.0268 - val_acc: 0.5117\n", "Epoch 278/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9080 - acc: 0.6054\n", "Epoch 00278: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9082 - acc: 0.6054 - val_loss: 1.0273 - val_acc: 0.5170\n", "Epoch 279/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9140 - acc: 0.6009\n", "Epoch 00279: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9142 - acc: 0.6005 - val_loss: 1.0268 - val_acc: 0.5222\n", "Epoch 280/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9123 - acc: 0.5964\n", "Epoch 00280: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9129 - acc: 0.5956 - val_loss: 1.0265 - val_acc: 0.5222\n", "Epoch 281/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9072 - acc: 0.6024\n", "Epoch 00281: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9069 - acc: 0.6024 - val_loss: 1.0268 - val_acc: 0.5196\n", "Epoch 282/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9088 - acc: 0.6020\n", "Epoch 00282: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9090 - acc: 0.6013 - val_loss: 1.0263 - val_acc: 0.5222\n", "Epoch 283/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9039 - acc: 0.6092\n", "Epoch 00283: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9038 - acc: 0.6092 - val_loss: 1.0258 - val_acc: 0.5248\n", "Epoch 284/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9102 - acc: 0.5949\n", "Epoch 00284: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9101 - acc: 0.5949 - val_loss: 1.0264 - val_acc: 0.5209\n", "Epoch 285/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9085 - acc: 0.6058\n", "Epoch 00285: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9086 - acc: 0.6058 - val_loss: 1.0268 - val_acc: 0.5183\n", "Epoch 286/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9046 - acc: 0.6096\n", "Epoch 00286: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9039 - acc: 0.6099 - val_loss: 1.0268 - val_acc: 0.5183\n", "Epoch 287/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9082 - acc: 0.5986\n", "Epoch 00287: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9084 - acc: 0.5986 - val_loss: 1.0259 - val_acc: 0.5170\n", "Epoch 288/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9131 - acc: 0.6014\n", "Epoch 00288: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9133 - acc: 0.6024 - val_loss: 1.0262 - val_acc: 0.5144\n", "Epoch 289/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9094 - acc: 0.5915\n", "Epoch 00289: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9097 - acc: 0.5915 - val_loss: 1.0249 - val_acc: 0.5131\n", "Epoch 290/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9105 - acc: 0.6111\n", "Epoch 00290: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9110 - acc: 0.6103 - val_loss: 1.0253 - val_acc: 0.5170\n", "Epoch 291/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9121 - acc: 0.6002\n", "Epoch 00291: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9119 - acc: 0.5998 - val_loss: 1.0260 - val_acc: 0.5209\n", "Epoch 292/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9037 - acc: 0.6028\n", "Epoch 00292: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9037 - acc: 0.6028 - val_loss: 1.0242 - val_acc: 0.5235\n", "Epoch 293/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8988 - acc: 0.6160\n", "Epoch 00293: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8995 - acc: 0.6156 - val_loss: 1.0243 - val_acc: 0.5209\n", "Epoch 294/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9097 - acc: 0.6118\n", "Epoch 00294: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9096 - acc: 0.6118 - val_loss: 1.0248 - val_acc: 0.5170\n", "Epoch 295/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9054 - acc: 0.6039\n", "Epoch 00295: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9064 - acc: 0.6032 - val_loss: 1.0250 - val_acc: 0.5248\n", "Epoch 296/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9135 - acc: 0.5889\n", "Epoch 00296: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9136 - acc: 0.5889 - val_loss: 1.0243 - val_acc: 0.5196\n", "Epoch 297/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9064 - acc: 0.6054\n", "Epoch 00297: val_acc did not improve from 0.52480\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9059 - acc: 0.6054 - val_loss: 1.0249 - val_acc: 0.5196\n", "Epoch 298/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8882 - acc: 0.6194\n", "Epoch 00298: val_acc improved from 0.52480 to 0.52611, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8881 - acc: 0.6197 - val_loss: 1.0241 - val_acc: 0.5261\n", "Epoch 299/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9022 - acc: 0.6032\n", "Epoch 00299: val_acc did not improve from 0.52611\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9022 - acc: 0.6032 - val_loss: 1.0236 - val_acc: 0.5209\n", "Epoch 300/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9139 - acc: 0.6039\n", "Epoch 00300: val_acc did not improve from 0.52611\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9147 - acc: 0.6035 - val_loss: 1.0246 - val_acc: 0.5196\n", "Epoch 301/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9003 - acc: 0.6160\n", "Epoch 00301: val_acc did not improve from 0.52611\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9001 - acc: 0.6163 - val_loss: 1.0246 - val_acc: 0.5235\n", "Epoch 302/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8956 - acc: 0.6107\n", "Epoch 00302: val_acc improved from 0.52611 to 0.52872, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8962 - acc: 0.6103 - val_loss: 1.0225 - val_acc: 0.5287\n", "Epoch 303/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9075 - acc: 0.6126\n", "Epoch 00303: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9082 - acc: 0.6122 - val_loss: 1.0236 - val_acc: 0.5274\n", "Epoch 304/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8976 - acc: 0.6130\n", "Epoch 00304: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8974 - acc: 0.6129 - val_loss: 1.0242 - val_acc: 0.5222\n", "Epoch 305/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8961 - acc: 0.6126\n", "Epoch 00305: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8964 - acc: 0.6122 - val_loss: 1.0244 - val_acc: 0.5235\n", "Epoch 306/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9045 - acc: 0.6107\n", "Epoch 00306: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9041 - acc: 0.6107 - val_loss: 1.0234 - val_acc: 0.5222\n", "Epoch 307/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9034 - acc: 0.6066\n", "Epoch 00307: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9035 - acc: 0.6062 - val_loss: 1.0240 - val_acc: 0.5248\n", "Epoch 308/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9059 - acc: 0.6107\n", "Epoch 00308: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9058 - acc: 0.6107 - val_loss: 1.0231 - val_acc: 0.5248\n", "Epoch 309/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8971 - acc: 0.6111\n", "Epoch 00309: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8969 - acc: 0.6114 - val_loss: 1.0233 - val_acc: 0.5248\n", "Epoch 310/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8988 - acc: 0.6194\n", "Epoch 00310: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8991 - acc: 0.6193 - val_loss: 1.0234 - val_acc: 0.5261\n", "Epoch 311/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9053 - acc: 0.6152\n", "Epoch 00311: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9052 - acc: 0.6156 - val_loss: 1.0240 - val_acc: 0.5261\n", "Epoch 312/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8994 - acc: 0.6111\n", "Epoch 00312: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.9000 - acc: 0.6107 - val_loss: 1.0248 - val_acc: 0.5274\n", "Epoch 313/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8985 - acc: 0.6081\n", "Epoch 00313: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8981 - acc: 0.6084 - val_loss: 1.0258 - val_acc: 0.5261\n", "Epoch 314/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8969 - acc: 0.6220\n", "Epoch 00314: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8974 - acc: 0.6216 - val_loss: 1.0249 - val_acc: 0.5248\n", "Epoch 315/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8964 - acc: 0.6148\n", "Epoch 00315: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8963 - acc: 0.6148 - val_loss: 1.0251 - val_acc: 0.5235\n", "Epoch 316/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8848 - acc: 0.6370\n", "Epoch 00316: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8860 - acc: 0.6362 - val_loss: 1.0251 - val_acc: 0.5248\n", "Epoch 317/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8921 - acc: 0.6133\n", "Epoch 00317: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8931 - acc: 0.6122 - val_loss: 1.0251 - val_acc: 0.5261\n", "Epoch 318/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8930 - acc: 0.6148\n", "Epoch 00318: val_acc did not improve from 0.52872\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8928 - acc: 0.6152 - val_loss: 1.0234 - val_acc: 0.5261\n", "Epoch 319/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8929 - acc: 0.6242\n", "Epoch 00319: val_acc improved from 0.52872 to 0.53264, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 0.8933 - acc: 0.6242 - val_loss: 1.0222 - val_acc: 0.5326\n", "Epoch 320/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8858 - acc: 0.6145\n", "Epoch 00320: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8861 - acc: 0.6144 - val_loss: 1.0218 - val_acc: 0.5261\n", "Epoch 321/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8929 - acc: 0.6178\n", "Epoch 00321: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8933 - acc: 0.6171 - val_loss: 1.0228 - val_acc: 0.5300\n", "Epoch 322/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8845 - acc: 0.6152\n", "Epoch 00322: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8845 - acc: 0.6152 - val_loss: 1.0222 - val_acc: 0.5287\n", "Epoch 323/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8835 - acc: 0.6299\n", "Epoch 00323: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8835 - acc: 0.6295 - val_loss: 1.0219 - val_acc: 0.5300\n", "Epoch 324/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8935 - acc: 0.6190\n", "Epoch 00324: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8932 - acc: 0.6189 - val_loss: 1.0224 - val_acc: 0.5313\n", "Epoch 325/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8835 - acc: 0.6269\n", "Epoch 00325: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8831 - acc: 0.6272 - val_loss: 1.0227 - val_acc: 0.5300\n", "Epoch 326/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8920 - acc: 0.6156\n", "Epoch 00326: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8921 - acc: 0.6152 - val_loss: 1.0242 - val_acc: 0.5274\n", "Epoch 327/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8843 - acc: 0.6254\n", "Epoch 00327: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8841 - acc: 0.6257 - val_loss: 1.0225 - val_acc: 0.5300\n", "Epoch 328/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8893 - acc: 0.6160\n", "Epoch 00328: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8903 - acc: 0.6152 - val_loss: 1.0241 - val_acc: 0.5235\n", "Epoch 329/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8866 - acc: 0.6201\n", "Epoch 00329: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8866 - acc: 0.6204 - val_loss: 1.0236 - val_acc: 0.5261\n", "Epoch 330/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8858 - acc: 0.6171\n", "Epoch 00330: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8864 - acc: 0.6171 - val_loss: 1.0245 - val_acc: 0.5274\n", "Epoch 331/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8871 - acc: 0.6137\n", "Epoch 00331: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8868 - acc: 0.6141 - val_loss: 1.0233 - val_acc: 0.5300\n", "Epoch 332/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8909 - acc: 0.6227\n", "Epoch 00332: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8909 - acc: 0.6227 - val_loss: 1.0239 - val_acc: 0.5261\n", "Epoch 333/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8902 - acc: 0.6209\n", "Epoch 00333: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8910 - acc: 0.6204 - val_loss: 1.0234 - val_acc: 0.5261\n", "Epoch 334/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8882 - acc: 0.6197\n", "Epoch 00334: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8877 - acc: 0.6204 - val_loss: 1.0226 - val_acc: 0.5261\n", "Epoch 335/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8828 - acc: 0.6197\n", "Epoch 00335: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8833 - acc: 0.6189 - val_loss: 1.0219 - val_acc: 0.5287\n", "Epoch 336/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8801 - acc: 0.6303\n", "Epoch 00336: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8799 - acc: 0.6306 - val_loss: 1.0234 - val_acc: 0.5274\n", "Epoch 337/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8816 - acc: 0.6227\n", "Epoch 00337: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8814 - acc: 0.6227 - val_loss: 1.0244 - val_acc: 0.5235\n", "Epoch 338/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8638 - acc: 0.6423\n", "Epoch 00338: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8637 - acc: 0.6426 - val_loss: 1.0245 - val_acc: 0.5222\n", "Epoch 339/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8833 - acc: 0.6325\n", "Epoch 00339: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8835 - acc: 0.6317 - val_loss: 1.0267 - val_acc: 0.5261\n", "Epoch 340/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8819 - acc: 0.6235\n", "Epoch 00340: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8818 - acc: 0.6234 - val_loss: 1.0241 - val_acc: 0.5287\n", "Epoch 341/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8852 - acc: 0.6250\n", "Epoch 00341: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8852 - acc: 0.6253 - val_loss: 1.0243 - val_acc: 0.5261\n", "Epoch 342/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8826 - acc: 0.6299\n", "Epoch 00342: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8823 - acc: 0.6298 - val_loss: 1.0249 - val_acc: 0.5274\n", "Epoch 343/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8757 - acc: 0.6306\n", "Epoch 00343: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8754 - acc: 0.6310 - val_loss: 1.0245 - val_acc: 0.5274\n", "Epoch 344/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8749 - acc: 0.6246\n", "Epoch 00344: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8747 - acc: 0.6246 - val_loss: 1.0239 - val_acc: 0.5274\n", "Epoch 345/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8770 - acc: 0.6190\n", "Epoch 00345: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8771 - acc: 0.6186 - val_loss: 1.0242 - val_acc: 0.5235\n", "Epoch 346/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8771 - acc: 0.6276\n", "Epoch 00346: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8771 - acc: 0.6276 - val_loss: 1.0240 - val_acc: 0.5287\n", "Epoch 347/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8809 - acc: 0.6258\n", "Epoch 00347: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8808 - acc: 0.6257 - val_loss: 1.0242 - val_acc: 0.5287\n", "Epoch 348/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.8767 - acc: 0.6349\n", "Epoch 00348: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8768 - acc: 0.6343 - val_loss: 1.0251 - val_acc: 0.5274\n", "Epoch 349/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8701 - acc: 0.6288\n", "Epoch 00349: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8701 - acc: 0.6287 - val_loss: 1.0247 - val_acc: 0.5261\n", "Epoch 350/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8711 - acc: 0.6397\n", "Epoch 00350: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8707 - acc: 0.6400 - val_loss: 1.0252 - val_acc: 0.5287\n", "Epoch 351/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8806 - acc: 0.6276\n", "Epoch 00351: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8811 - acc: 0.6276 - val_loss: 1.0263 - val_acc: 0.5261\n", "Epoch 352/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8791 - acc: 0.6333\n", "Epoch 00352: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8797 - acc: 0.6328 - val_loss: 1.0281 - val_acc: 0.5235\n", "Epoch 353/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8708 - acc: 0.6325\n", "Epoch 00353: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8719 - acc: 0.6317 - val_loss: 1.0279 - val_acc: 0.5274\n", "Epoch 354/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8724 - acc: 0.6329\n", "Epoch 00354: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8731 - acc: 0.6325 - val_loss: 1.0268 - val_acc: 0.5235\n", "Epoch 355/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8820 - acc: 0.6205\n", "Epoch 00355: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8821 - acc: 0.6197 - val_loss: 1.0262 - val_acc: 0.5300\n", "Epoch 356/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8703 - acc: 0.6280\n", "Epoch 00356: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8703 - acc: 0.6280 - val_loss: 1.0255 - val_acc: 0.5248\n", "Epoch 357/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.8745 - acc: 0.6349\n", "Epoch 00357: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8747 - acc: 0.6336 - val_loss: 1.0258 - val_acc: 0.5222\n", "Epoch 358/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8722 - acc: 0.6269\n", "Epoch 00358: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8725 - acc: 0.6265 - val_loss: 1.0266 - val_acc: 0.5274\n", "Epoch 359/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8819 - acc: 0.6242\n", "Epoch 00359: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8819 - acc: 0.6242 - val_loss: 1.0262 - val_acc: 0.5274\n", "Epoch 360/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8693 - acc: 0.6378\n", "Epoch 00360: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8693 - acc: 0.6377 - val_loss: 1.0251 - val_acc: 0.5261\n", "Epoch 361/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8623 - acc: 0.6382\n", "Epoch 00361: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8621 - acc: 0.6385 - val_loss: 1.0268 - val_acc: 0.5287\n", "Epoch 362/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8785 - acc: 0.6295\n", "Epoch 00362: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8789 - acc: 0.6295 - val_loss: 1.0264 - val_acc: 0.5287\n", "Epoch 363/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8720 - acc: 0.6269\n", "Epoch 00363: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8725 - acc: 0.6265 - val_loss: 1.0261 - val_acc: 0.5300\n", "Epoch 364/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8669 - acc: 0.6363\n", "Epoch 00364: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8676 - acc: 0.6359 - val_loss: 1.0255 - val_acc: 0.5287\n", "Epoch 365/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8727 - acc: 0.6314\n", "Epoch 00365: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8729 - acc: 0.6313 - val_loss: 1.0262 - val_acc: 0.5300\n", "Epoch 366/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8709 - acc: 0.6295\n", "Epoch 00366: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8709 - acc: 0.6295 - val_loss: 1.0266 - val_acc: 0.5287\n", "Epoch 367/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8657 - acc: 0.6386\n", "Epoch 00367: val_acc did not improve from 0.53264\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8660 - acc: 0.6381 - val_loss: 1.0265 - val_acc: 0.5261\n", "Epoch 368/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8596 - acc: 0.6419\n", "Epoch 00368: val_acc improved from 0.53264 to 0.53394, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8598 - acc: 0.6419 - val_loss: 1.0264 - val_acc: 0.5339\n", "Epoch 369/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8674 - acc: 0.6348\n", "Epoch 00369: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8675 - acc: 0.6347 - val_loss: 1.0254 - val_acc: 0.5326\n", "Epoch 370/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8586 - acc: 0.6521\n", "Epoch 00370: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8587 - acc: 0.6524 - val_loss: 1.0247 - val_acc: 0.5313\n", "Epoch 371/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8542 - acc: 0.6532\n", "Epoch 00371: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8539 - acc: 0.6535 - val_loss: 1.0254 - val_acc: 0.5313\n", "Epoch 372/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8598 - acc: 0.6461\n", "Epoch 00372: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8595 - acc: 0.6464 - val_loss: 1.0264 - val_acc: 0.5287\n", "Epoch 373/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8672 - acc: 0.6412\n", "Epoch 00373: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8669 - acc: 0.6415 - val_loss: 1.0259 - val_acc: 0.5287\n", "Epoch 374/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8545 - acc: 0.6434\n", "Epoch 00374: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8542 - acc: 0.6441 - val_loss: 1.0261 - val_acc: 0.5248\n", "Epoch 375/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8667 - acc: 0.6333\n", "Epoch 00375: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8669 - acc: 0.6332 - val_loss: 1.0256 - val_acc: 0.5261\n", "Epoch 376/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8667 - acc: 0.6461\n", "Epoch 00376: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8665 - acc: 0.6460 - val_loss: 1.0271 - val_acc: 0.5300\n", "Epoch 377/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8593 - acc: 0.6393\n", "Epoch 00377: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8599 - acc: 0.6392 - val_loss: 1.0272 - val_acc: 0.5274\n", "Epoch 378/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8578 - acc: 0.6412\n", "Epoch 00378: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8582 - acc: 0.6407 - val_loss: 1.0277 - val_acc: 0.5248\n", "Epoch 379/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8602 - acc: 0.6476\n", "Epoch 00379: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8606 - acc: 0.6471 - val_loss: 1.0277 - val_acc: 0.5235\n", "Epoch 380/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8599 - acc: 0.6434\n", "Epoch 00380: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8606 - acc: 0.6430 - val_loss: 1.0279 - val_acc: 0.5222\n", "Epoch 381/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8532 - acc: 0.6623\n", "Epoch 00381: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8529 - acc: 0.6622 - val_loss: 1.0280 - val_acc: 0.5235\n", "Epoch 382/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8557 - acc: 0.6453\n", "Epoch 00382: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8557 - acc: 0.6456 - val_loss: 1.0272 - val_acc: 0.5248\n", "Epoch 383/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8545 - acc: 0.6412\n", "Epoch 00383: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8540 - acc: 0.6415 - val_loss: 1.0273 - val_acc: 0.5274\n", "Epoch 384/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8539 - acc: 0.6536\n", "Epoch 00384: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8533 - acc: 0.6543 - val_loss: 1.0278 - val_acc: 0.5313\n", "Epoch 385/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8556 - acc: 0.6457\n", "Epoch 00385: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8556 - acc: 0.6460 - val_loss: 1.0284 - val_acc: 0.5287\n", "Epoch 386/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8689 - acc: 0.6370\n", "Epoch 00386: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8693 - acc: 0.6362 - val_loss: 1.0269 - val_acc: 0.5287\n", "Epoch 387/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8561 - acc: 0.6344\n", "Epoch 00387: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8556 - acc: 0.6347 - val_loss: 1.0264 - val_acc: 0.5287\n", "Epoch 388/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8505 - acc: 0.6555\n", "Epoch 00388: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8500 - acc: 0.6561 - val_loss: 1.0272 - val_acc: 0.5287\n", "Epoch 389/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8526 - acc: 0.6525\n", "Epoch 00389: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8533 - acc: 0.6520 - val_loss: 1.0252 - val_acc: 0.5235\n", "Epoch 390/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8462 - acc: 0.6611\n", "Epoch 00390: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8465 - acc: 0.6614 - val_loss: 1.0257 - val_acc: 0.5287\n", "Epoch 391/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8529 - acc: 0.6483\n", "Epoch 00391: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8525 - acc: 0.6486 - val_loss: 1.0244 - val_acc: 0.5248\n", "Epoch 392/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8565 - acc: 0.6468\n", "Epoch 00392: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8560 - acc: 0.6475 - val_loss: 1.0234 - val_acc: 0.5248\n", "Epoch 393/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8606 - acc: 0.6427\n", "Epoch 00393: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8602 - acc: 0.6426 - val_loss: 1.0232 - val_acc: 0.5235\n", "Epoch 394/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8566 - acc: 0.6434\n", "Epoch 00394: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8564 - acc: 0.6434 - val_loss: 1.0233 - val_acc: 0.5287\n", "Epoch 395/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8494 - acc: 0.6525\n", "Epoch 00395: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8499 - acc: 0.6524 - val_loss: 1.0241 - val_acc: 0.5287\n", "Epoch 396/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8531 - acc: 0.6434\n", "Epoch 00396: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8532 - acc: 0.6437 - val_loss: 1.0242 - val_acc: 0.5261\n", "Epoch 397/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8483 - acc: 0.6544\n", "Epoch 00397: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8481 - acc: 0.6546 - val_loss: 1.0244 - val_acc: 0.5274\n", "Epoch 398/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8520 - acc: 0.6593\n", "Epoch 00398: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8518 - acc: 0.6595 - val_loss: 1.0248 - val_acc: 0.5274\n", "Epoch 399/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8492 - acc: 0.6529\n", "Epoch 00399: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8498 - acc: 0.6520 - val_loss: 1.0253 - val_acc: 0.5209\n", "Epoch 400/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8451 - acc: 0.6562\n", "Epoch 00400: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8444 - acc: 0.6569 - val_loss: 1.0256 - val_acc: 0.5222\n", "Epoch 401/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8397 - acc: 0.6649\n", "Epoch 00401: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8398 - acc: 0.6644 - val_loss: 1.0243 - val_acc: 0.5209\n", "Epoch 402/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8381 - acc: 0.6589\n", "Epoch 00402: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8379 - acc: 0.6588 - val_loss: 1.0256 - val_acc: 0.5196\n", "Epoch 403/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8490 - acc: 0.6514\n", "Epoch 00403: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8489 - acc: 0.6513 - val_loss: 1.0256 - val_acc: 0.5248\n", "Epoch 404/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8508 - acc: 0.6510\n", "Epoch 00404: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8508 - acc: 0.6505 - val_loss: 1.0257 - val_acc: 0.5235\n", "Epoch 405/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8392 - acc: 0.6574\n", "Epoch 00405: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8393 - acc: 0.6569 - val_loss: 1.0256 - val_acc: 0.5209\n", "Epoch 406/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8357 - acc: 0.6675\n", "Epoch 00406: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8359 - acc: 0.6670 - val_loss: 1.0260 - val_acc: 0.5183\n", "Epoch 407/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8472 - acc: 0.6480\n", "Epoch 00407: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8469 - acc: 0.6479 - val_loss: 1.0265 - val_acc: 0.5183\n", "Epoch 408/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8473 - acc: 0.6517\n", "Epoch 00408: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8475 - acc: 0.6516 - val_loss: 1.0261 - val_acc: 0.5183\n", "Epoch 409/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8447 - acc: 0.6638\n", "Epoch 00409: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8441 - acc: 0.6644 - val_loss: 1.0288 - val_acc: 0.5196\n", "Epoch 410/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8494 - acc: 0.6476\n", "Epoch 00410: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8488 - acc: 0.6479 - val_loss: 1.0269 - val_acc: 0.5183\n", "Epoch 411/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8413 - acc: 0.6593\n", "Epoch 00411: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8411 - acc: 0.6595 - val_loss: 1.0270 - val_acc: 0.5196\n", "Epoch 412/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8462 - acc: 0.6578\n", "Epoch 00412: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8457 - acc: 0.6584 - val_loss: 1.0258 - val_acc: 0.5157\n", "Epoch 413/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8484 - acc: 0.6593\n", "Epoch 00413: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8492 - acc: 0.6584 - val_loss: 1.0252 - val_acc: 0.5170\n", "Epoch 414/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8401 - acc: 0.6529\n", "Epoch 00414: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8395 - acc: 0.6531 - val_loss: 1.0266 - val_acc: 0.5196\n", "Epoch 415/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8363 - acc: 0.6596\n", "Epoch 00415: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8365 - acc: 0.6595 - val_loss: 1.0272 - val_acc: 0.5196\n", "Epoch 416/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8356 - acc: 0.6608\n", "Epoch 00416: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8362 - acc: 0.6607 - val_loss: 1.0257 - val_acc: 0.5209\n", "Epoch 417/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8342 - acc: 0.6514\n", "Epoch 00417: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8337 - acc: 0.6516 - val_loss: 1.0254 - val_acc: 0.5222\n", "Epoch 418/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8367 - acc: 0.6589\n", "Epoch 00418: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8373 - acc: 0.6584 - val_loss: 1.0256 - val_acc: 0.5261\n", "Epoch 419/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8405 - acc: 0.6645\n", "Epoch 00419: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8404 - acc: 0.6644 - val_loss: 1.0253 - val_acc: 0.5196\n", "Epoch 420/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8384 - acc: 0.6566\n", "Epoch 00420: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8386 - acc: 0.6561 - val_loss: 1.0253 - val_acc: 0.5183\n", "Epoch 421/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8362 - acc: 0.6664\n", "Epoch 00421: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8360 - acc: 0.6667 - val_loss: 1.0253 - val_acc: 0.5196\n", "Epoch 422/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8321 - acc: 0.6698\n", "Epoch 00422: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8326 - acc: 0.6693 - val_loss: 1.0265 - val_acc: 0.5209\n", "Epoch 423/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8373 - acc: 0.6638\n", "Epoch 00423: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8364 - acc: 0.6644 - val_loss: 1.0271 - val_acc: 0.5222\n", "Epoch 424/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8263 - acc: 0.6653\n", "Epoch 00424: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8269 - acc: 0.6652 - val_loss: 1.0273 - val_acc: 0.5209\n", "Epoch 425/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8329 - acc: 0.6540\n", "Epoch 00425: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8330 - acc: 0.6539 - val_loss: 1.0274 - val_acc: 0.5183\n", "Epoch 426/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8256 - acc: 0.6743\n", "Epoch 00426: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8259 - acc: 0.6742 - val_loss: 1.0274 - val_acc: 0.5300\n", "Epoch 427/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8340 - acc: 0.6574\n", "Epoch 00427: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8342 - acc: 0.6573 - val_loss: 1.0273 - val_acc: 0.5196\n", "Epoch 428/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8353 - acc: 0.6532\n", "Epoch 00428: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8349 - acc: 0.6539 - val_loss: 1.0286 - val_acc: 0.5222\n", "Epoch 429/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8311 - acc: 0.6649\n", "Epoch 00429: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8311 - acc: 0.6648 - val_loss: 1.0281 - val_acc: 0.5222\n", "Epoch 430/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8264 - acc: 0.6683\n", "Epoch 00430: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8265 - acc: 0.6678 - val_loss: 1.0304 - val_acc: 0.5209\n", "Epoch 431/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8322 - acc: 0.6675\n", "Epoch 00431: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8319 - acc: 0.6678 - val_loss: 1.0302 - val_acc: 0.5183\n", "Epoch 432/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8317 - acc: 0.6694\n", "Epoch 00432: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8313 - acc: 0.6697 - val_loss: 1.0298 - val_acc: 0.5196\n", "Epoch 433/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8395 - acc: 0.6608\n", "Epoch 00433: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8395 - acc: 0.6610 - val_loss: 1.0305 - val_acc: 0.5170\n", "Epoch 434/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8362 - acc: 0.6627\n", "Epoch 00434: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8369 - acc: 0.6622 - val_loss: 1.0320 - val_acc: 0.5209\n", "Epoch 435/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8267 - acc: 0.6691\n", "Epoch 00435: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8266 - acc: 0.6689 - val_loss: 1.0312 - val_acc: 0.5196\n", "Epoch 436/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8318 - acc: 0.6532\n", "Epoch 00436: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8321 - acc: 0.6531 - val_loss: 1.0308 - val_acc: 0.5209\n", "Epoch 437/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8303 - acc: 0.6687\n", "Epoch 00437: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8305 - acc: 0.6682 - val_loss: 1.0288 - val_acc: 0.5157\n", "Epoch 438/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8292 - acc: 0.6645\n", "Epoch 00438: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8291 - acc: 0.6644 - val_loss: 1.0274 - val_acc: 0.5170\n", "Epoch 439/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8215 - acc: 0.6837\n", "Epoch 00439: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8212 - acc: 0.6836 - val_loss: 1.0279 - val_acc: 0.5170\n", "Epoch 440/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8271 - acc: 0.6664\n", "Epoch 00440: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8274 - acc: 0.6663 - val_loss: 1.0279 - val_acc: 0.5157\n", "Epoch 441/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8184 - acc: 0.6803\n", "Epoch 00441: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8183 - acc: 0.6806 - val_loss: 1.0281 - val_acc: 0.5157\n", "Epoch 442/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8229 - acc: 0.6728\n", "Epoch 00442: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8226 - acc: 0.6731 - val_loss: 1.0290 - val_acc: 0.5170\n", "Epoch 443/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8195 - acc: 0.6785\n", "Epoch 00443: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8195 - acc: 0.6783 - val_loss: 1.0293 - val_acc: 0.5235\n", "Epoch 444/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8383 - acc: 0.6615\n", "Epoch 00444: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8374 - acc: 0.6622 - val_loss: 1.0282 - val_acc: 0.5209\n", "Epoch 445/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8228 - acc: 0.6660\n", "Epoch 00445: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8234 - acc: 0.6655 - val_loss: 1.0280 - val_acc: 0.5235\n", "Epoch 446/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8204 - acc: 0.6679\n", "Epoch 00446: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8201 - acc: 0.6685 - val_loss: 1.0296 - val_acc: 0.5248\n", "Epoch 447/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8159 - acc: 0.6766\n", "Epoch 00447: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8163 - acc: 0.6761 - val_loss: 1.0301 - val_acc: 0.5196\n", "Epoch 448/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8262 - acc: 0.6796\n", "Epoch 00448: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8260 - acc: 0.6794 - val_loss: 1.0309 - val_acc: 0.5248\n", "Epoch 449/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8237 - acc: 0.6777\n", "Epoch 00449: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8236 - acc: 0.6779 - val_loss: 1.0309 - val_acc: 0.5235\n", "Epoch 450/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8210 - acc: 0.6717\n", "Epoch 00450: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8208 - acc: 0.6719 - val_loss: 1.0305 - val_acc: 0.5235\n", "Epoch 451/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8266 - acc: 0.6691\n", "Epoch 00451: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8268 - acc: 0.6693 - val_loss: 1.0286 - val_acc: 0.5222\n", "Epoch 452/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8321 - acc: 0.6657\n", "Epoch 00452: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8314 - acc: 0.6663 - val_loss: 1.0298 - val_acc: 0.5235\n", "Epoch 453/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8152 - acc: 0.6758\n", "Epoch 00453: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8154 - acc: 0.6757 - val_loss: 1.0282 - val_acc: 0.5235\n", "Epoch 454/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8209 - acc: 0.6717\n", "Epoch 00454: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8210 - acc: 0.6716 - val_loss: 1.0294 - val_acc: 0.5235\n", "Epoch 455/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8102 - acc: 0.6826\n", "Epoch 00455: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8107 - acc: 0.6821 - val_loss: 1.0279 - val_acc: 0.5261\n", "Epoch 456/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8107 - acc: 0.6852\n", "Epoch 00456: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8107 - acc: 0.6851 - val_loss: 1.0318 - val_acc: 0.5209\n", "Epoch 457/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8120 - acc: 0.6781\n", "Epoch 00457: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8122 - acc: 0.6779 - val_loss: 1.0295 - val_acc: 0.5248\n", "Epoch 458/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8163 - acc: 0.6766\n", "Epoch 00458: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8160 - acc: 0.6768 - val_loss: 1.0279 - val_acc: 0.5274\n", "Epoch 459/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8213 - acc: 0.6706\n", "Epoch 00459: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8214 - acc: 0.6708 - val_loss: 1.0300 - val_acc: 0.5222\n", "Epoch 460/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8093 - acc: 0.6732\n", "Epoch 00460: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8098 - acc: 0.6727 - val_loss: 1.0305 - val_acc: 0.5248\n", "Epoch 461/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8142 - acc: 0.6773\n", "Epoch 00461: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8141 - acc: 0.6776 - val_loss: 1.0318 - val_acc: 0.5248\n", "Epoch 462/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8094 - acc: 0.6856\n", "Epoch 00462: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8088 - acc: 0.6862 - val_loss: 1.0288 - val_acc: 0.5274\n", "Epoch 463/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8160 - acc: 0.6800\n", "Epoch 00463: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8165 - acc: 0.6794 - val_loss: 1.0298 - val_acc: 0.5261\n", "Epoch 464/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8062 - acc: 0.6886\n", "Epoch 00464: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8060 - acc: 0.6888 - val_loss: 1.0309 - val_acc: 0.5339\n", "Epoch 465/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8240 - acc: 0.6653\n", "Epoch 00465: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8234 - acc: 0.6655 - val_loss: 1.0281 - val_acc: 0.5300\n", "Epoch 466/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8153 - acc: 0.6702\n", "Epoch 00466: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8152 - acc: 0.6700 - val_loss: 1.0275 - val_acc: 0.5300\n", "Epoch 467/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8154 - acc: 0.6732\n", "Epoch 00467: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8155 - acc: 0.6731 - val_loss: 1.0293 - val_acc: 0.5300\n", "Epoch 468/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8008 - acc: 0.6837\n", "Epoch 00468: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8007 - acc: 0.6840 - val_loss: 1.0311 - val_acc: 0.5326\n", "Epoch 469/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8104 - acc: 0.6867\n", "Epoch 00469: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8104 - acc: 0.6866 - val_loss: 1.0308 - val_acc: 0.5300\n", "Epoch 470/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8080 - acc: 0.6822\n", "Epoch 00470: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8085 - acc: 0.6817 - val_loss: 1.0304 - val_acc: 0.5287\n", "Epoch 471/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8012 - acc: 0.6860\n", "Epoch 00471: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8013 - acc: 0.6855 - val_loss: 1.0317 - val_acc: 0.5300\n", "Epoch 472/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8067 - acc: 0.6920\n", "Epoch 00472: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8062 - acc: 0.6922 - val_loss: 1.0294 - val_acc: 0.5287\n", "Epoch 473/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8092 - acc: 0.6728\n", "Epoch 00473: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8094 - acc: 0.6727 - val_loss: 1.0294 - val_acc: 0.5300\n", "Epoch 474/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7877 - acc: 0.6965\n", "Epoch 00474: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7878 - acc: 0.6967 - val_loss: 1.0295 - val_acc: 0.5287\n", "Epoch 475/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8062 - acc: 0.6845\n", "Epoch 00475: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8060 - acc: 0.6847 - val_loss: 1.0328 - val_acc: 0.5300\n", "Epoch 476/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8042 - acc: 0.6807\n", "Epoch 00476: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8046 - acc: 0.6806 - val_loss: 1.0317 - val_acc: 0.5287\n", "Epoch 477/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7988 - acc: 0.6867\n", "Epoch 00477: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7994 - acc: 0.6858 - val_loss: 1.0319 - val_acc: 0.5313\n", "Epoch 478/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7967 - acc: 0.6962\n", "Epoch 00478: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7969 - acc: 0.6960 - val_loss: 1.0313 - val_acc: 0.5287\n", "Epoch 479/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7993 - acc: 0.6871\n", "Epoch 00479: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7988 - acc: 0.6873 - val_loss: 1.0325 - val_acc: 0.5300\n", "Epoch 480/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8007 - acc: 0.6894\n", "Epoch 00480: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8014 - acc: 0.6888 - val_loss: 1.0332 - val_acc: 0.5287\n", "Epoch 481/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.8046 - acc: 0.6879\n", "Epoch 00481: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8040 - acc: 0.6885 - val_loss: 1.0343 - val_acc: 0.5287\n", "Epoch 482/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8060 - acc: 0.6796\n", "Epoch 00482: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8062 - acc: 0.6794 - val_loss: 1.0352 - val_acc: 0.5274\n", "Epoch 483/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8036 - acc: 0.6879\n", "Epoch 00483: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8036 - acc: 0.6881 - val_loss: 1.0350 - val_acc: 0.5261\n", "Epoch 484/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7936 - acc: 0.6871\n", "Epoch 00484: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7942 - acc: 0.6866 - val_loss: 1.0342 - val_acc: 0.5248\n", "Epoch 485/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8103 - acc: 0.6875\n", "Epoch 00485: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8101 - acc: 0.6873 - val_loss: 1.0317 - val_acc: 0.5235\n", "Epoch 486/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7984 - acc: 0.6819\n", "Epoch 00486: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7982 - acc: 0.6825 - val_loss: 1.0319 - val_acc: 0.5248\n", "Epoch 487/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8019 - acc: 0.6822\n", "Epoch 00487: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8013 - acc: 0.6825 - val_loss: 1.0325 - val_acc: 0.5248\n", "Epoch 488/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7936 - acc: 0.6841\n", "Epoch 00488: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7939 - acc: 0.6836 - val_loss: 1.0320 - val_acc: 0.5261\n", "Epoch 489/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7826 - acc: 0.6992\n", "Epoch 00489: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7822 - acc: 0.6994 - val_loss: 1.0330 - val_acc: 0.5300\n", "Epoch 490/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7942 - acc: 0.6909\n", "Epoch 00490: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7945 - acc: 0.6911 - val_loss: 1.0327 - val_acc: 0.5339\n", "Epoch 491/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8023 - acc: 0.6811\n", "Epoch 00491: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8021 - acc: 0.6809 - val_loss: 1.0325 - val_acc: 0.5274\n", "Epoch 492/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8015 - acc: 0.6788\n", "Epoch 00492: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.8013 - acc: 0.6791 - val_loss: 1.0297 - val_acc: 0.5248\n", "Epoch 493/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7959 - acc: 0.6924\n", "Epoch 00493: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7955 - acc: 0.6930 - val_loss: 1.0313 - val_acc: 0.5287\n", "Epoch 494/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7785 - acc: 0.7108\n", "Epoch 00494: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7784 - acc: 0.7110 - val_loss: 1.0328 - val_acc: 0.5274\n", "Epoch 495/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7843 - acc: 0.7003\n", "Epoch 00495: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7837 - acc: 0.7005 - val_loss: 1.0319 - val_acc: 0.5287\n", "Epoch 496/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7992 - acc: 0.6928\n", "Epoch 00496: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7995 - acc: 0.6926 - val_loss: 1.0331 - val_acc: 0.5261\n", "Epoch 497/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7974 - acc: 0.6894\n", "Epoch 00497: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7977 - acc: 0.6892 - val_loss: 1.0348 - val_acc: 0.5274\n", "Epoch 498/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7958 - acc: 0.6883\n", "Epoch 00498: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7961 - acc: 0.6881 - val_loss: 1.0332 - val_acc: 0.5313\n", "Epoch 499/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7901 - acc: 0.6973\n", "Epoch 00499: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7905 - acc: 0.6971 - val_loss: 1.0314 - val_acc: 0.5300\n", "Epoch 500/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7867 - acc: 0.6995\n", "Epoch 00500: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7866 - acc: 0.7001 - val_loss: 1.0318 - val_acc: 0.5300\n", "Epoch 501/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7757 - acc: 0.7033\n", "Epoch 00501: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7766 - acc: 0.7027 - val_loss: 1.0315 - val_acc: 0.5248\n", "Epoch 502/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7875 - acc: 0.6939\n", "Epoch 00502: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7871 - acc: 0.6941 - val_loss: 1.0316 - val_acc: 0.5313\n", "Epoch 503/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7841 - acc: 0.6943\n", "Epoch 00503: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7841 - acc: 0.6945 - val_loss: 1.0334 - val_acc: 0.5300\n", "Epoch 504/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7960 - acc: 0.6867\n", "Epoch 00504: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7958 - acc: 0.6870 - val_loss: 1.0322 - val_acc: 0.5274\n", "Epoch 505/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7886 - acc: 0.7022\n", "Epoch 00505: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7888 - acc: 0.7016 - val_loss: 1.0322 - val_acc: 0.5261\n", "Epoch 506/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7905 - acc: 0.6950\n", "Epoch 00506: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7905 - acc: 0.6952 - val_loss: 1.0326 - val_acc: 0.5313\n", "Epoch 507/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.7970 - acc: 0.6814\n", "Epoch 00507: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7957 - acc: 0.6821 - val_loss: 1.0322 - val_acc: 0.5287\n", "Epoch 508/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7897 - acc: 0.7011\n", "Epoch 00508: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7899 - acc: 0.7005 - val_loss: 1.0316 - val_acc: 0.5326\n", "Epoch 509/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7894 - acc: 0.6924\n", "Epoch 00509: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7890 - acc: 0.6926 - val_loss: 1.0319 - val_acc: 0.5313\n", "Epoch 510/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7887 - acc: 0.7011\n", "Epoch 00510: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7889 - acc: 0.7005 - val_loss: 1.0319 - val_acc: 0.5339\n", "Epoch 511/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7776 - acc: 0.7078\n", "Epoch 00511: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7783 - acc: 0.7073 - val_loss: 1.0311 - val_acc: 0.5300\n", "Epoch 512/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7817 - acc: 0.7063\n", "Epoch 00512: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7816 - acc: 0.7065 - val_loss: 1.0319 - val_acc: 0.5300\n", "Epoch 513/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7806 - acc: 0.6935\n", "Epoch 00513: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7804 - acc: 0.6937 - val_loss: 1.0323 - val_acc: 0.5287\n", "Epoch 514/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7872 - acc: 0.6943\n", "Epoch 00514: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7869 - acc: 0.6945 - val_loss: 1.0303 - val_acc: 0.5274\n", "Epoch 515/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7770 - acc: 0.7097\n", "Epoch 00515: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7772 - acc: 0.7099 - val_loss: 1.0314 - val_acc: 0.5248\n", "Epoch 516/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7779 - acc: 0.7059\n", "Epoch 00516: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7777 - acc: 0.7061 - val_loss: 1.0345 - val_acc: 0.5235\n", "Epoch 517/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.7835 - acc: 0.6993\n", "Epoch 00517: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7844 - acc: 0.6986 - val_loss: 1.0340 - val_acc: 0.5287\n", "Epoch 518/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7779 - acc: 0.7014\n", "Epoch 00518: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7773 - acc: 0.7020 - val_loss: 1.0331 - val_acc: 0.5287\n", "Epoch 519/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7687 - acc: 0.7029\n", "Epoch 00519: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7684 - acc: 0.7035 - val_loss: 1.0334 - val_acc: 0.5274\n", "Epoch 520/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7735 - acc: 0.7063\n", "Epoch 00520: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7730 - acc: 0.7069 - val_loss: 1.0335 - val_acc: 0.5287\n", "Epoch 521/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7818 - acc: 0.6973\n", "Epoch 00521: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7824 - acc: 0.6967 - val_loss: 1.0331 - val_acc: 0.5287\n", "Epoch 522/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7896 - acc: 0.6973\n", "Epoch 00522: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7895 - acc: 0.6975 - val_loss: 1.0333 - val_acc: 0.5300\n", "Epoch 523/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7840 - acc: 0.7014\n", "Epoch 00523: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7835 - acc: 0.7020 - val_loss: 1.0325 - val_acc: 0.5300\n", "Epoch 524/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7777 - acc: 0.7052\n", "Epoch 00524: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7775 - acc: 0.7057 - val_loss: 1.0328 - val_acc: 0.5300\n", "Epoch 525/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7786 - acc: 0.6995\n", "Epoch 00525: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7784 - acc: 0.6997 - val_loss: 1.0334 - val_acc: 0.5248\n", "Epoch 526/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7713 - acc: 0.7150\n", "Epoch 00526: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7706 - acc: 0.7155 - val_loss: 1.0326 - val_acc: 0.5287\n", "Epoch 527/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7745 - acc: 0.7052\n", "Epoch 00527: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7748 - acc: 0.7046 - val_loss: 1.0341 - val_acc: 0.5274\n", "Epoch 528/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7695 - acc: 0.7071\n", "Epoch 00528: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7692 - acc: 0.7076 - val_loss: 1.0330 - val_acc: 0.5261\n", "Epoch 529/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7802 - acc: 0.6977\n", "Epoch 00529: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7808 - acc: 0.6971 - val_loss: 1.0340 - val_acc: 0.5261\n", "Epoch 530/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7736 - acc: 0.6973\n", "Epoch 00530: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7735 - acc: 0.6975 - val_loss: 1.0343 - val_acc: 0.5300\n", "Epoch 531/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7804 - acc: 0.7011\n", "Epoch 00531: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7809 - acc: 0.7009 - val_loss: 1.0357 - val_acc: 0.5287\n", "Epoch 532/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7623 - acc: 0.7188\n", "Epoch 00532: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7629 - acc: 0.7185 - val_loss: 1.0338 - val_acc: 0.5300\n", "Epoch 533/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7736 - acc: 0.7037\n", "Epoch 00533: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7739 - acc: 0.7031 - val_loss: 1.0363 - val_acc: 0.5287\n", "Epoch 534/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7647 - acc: 0.7112\n", "Epoch 00534: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7646 - acc: 0.7114 - val_loss: 1.0361 - val_acc: 0.5300\n", "Epoch 535/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7689 - acc: 0.6965\n", "Epoch 00535: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7694 - acc: 0.6960 - val_loss: 1.0348 - val_acc: 0.5274\n", "Epoch 536/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7708 - acc: 0.7108\n", "Epoch 00536: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7703 - acc: 0.7114 - val_loss: 1.0337 - val_acc: 0.5300\n", "Epoch 537/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7754 - acc: 0.7033\n", "Epoch 00537: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7758 - acc: 0.7027 - val_loss: 1.0347 - val_acc: 0.5300\n", "Epoch 538/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.7698 - acc: 0.7088\n", "Epoch 00538: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7702 - acc: 0.7080 - val_loss: 1.0344 - val_acc: 0.5313\n", "Epoch 539/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7610 - acc: 0.7203\n", "Epoch 00539: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7611 - acc: 0.7200 - val_loss: 1.0342 - val_acc: 0.5274\n", "Epoch 540/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7703 - acc: 0.7154\n", "Epoch 00540: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7704 - acc: 0.7151 - val_loss: 1.0357 - val_acc: 0.5300\n", "Epoch 541/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7678 - acc: 0.7090\n", "Epoch 00541: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7679 - acc: 0.7088 - val_loss: 1.0367 - val_acc: 0.5287\n", "Epoch 542/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7556 - acc: 0.7097\n", "Epoch 00542: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7559 - acc: 0.7091 - val_loss: 1.0366 - val_acc: 0.5300\n", "Epoch 543/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7759 - acc: 0.7003\n", "Epoch 00543: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7753 - acc: 0.7009 - val_loss: 1.0370 - val_acc: 0.5274\n", "Epoch 544/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7615 - acc: 0.7191\n", "Epoch 00544: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7618 - acc: 0.7193 - val_loss: 1.0376 - val_acc: 0.5274\n", "Epoch 545/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7706 - acc: 0.7071\n", "Epoch 00545: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7703 - acc: 0.7073 - val_loss: 1.0367 - val_acc: 0.5261\n", "Epoch 546/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7583 - acc: 0.7131\n", "Epoch 00546: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7587 - acc: 0.7125 - val_loss: 1.0383 - val_acc: 0.5287\n", "Epoch 547/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7665 - acc: 0.7105\n", "Epoch 00547: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7665 - acc: 0.7106 - val_loss: 1.0373 - val_acc: 0.5274\n", "Epoch 548/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7646 - acc: 0.7154\n", "Epoch 00548: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7647 - acc: 0.7151 - val_loss: 1.0380 - val_acc: 0.5300\n", "Epoch 549/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7558 - acc: 0.7206\n", "Epoch 00549: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7560 - acc: 0.7204 - val_loss: 1.0371 - val_acc: 0.5274\n", "Epoch 550/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7672 - acc: 0.7108\n", "Epoch 00550: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7669 - acc: 0.7110 - val_loss: 1.0381 - val_acc: 0.5287\n", "Epoch 551/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7736 - acc: 0.7086\n", "Epoch 00551: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7727 - acc: 0.7091 - val_loss: 1.0383 - val_acc: 0.5287\n", "Epoch 552/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7683 - acc: 0.7146\n", "Epoch 00552: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7689 - acc: 0.7144 - val_loss: 1.0398 - val_acc: 0.5300\n", "Epoch 553/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7585 - acc: 0.7056\n", "Epoch 00553: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7585 - acc: 0.7054 - val_loss: 1.0405 - val_acc: 0.5287\n", "Epoch 554/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7717 - acc: 0.7116\n", "Epoch 00554: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7720 - acc: 0.7114 - val_loss: 1.0432 - val_acc: 0.5300\n", "Epoch 555/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7555 - acc: 0.7165\n", "Epoch 00555: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7554 - acc: 0.7166 - val_loss: 1.0413 - val_acc: 0.5261\n", "Epoch 556/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7675 - acc: 0.7056\n", "Epoch 00556: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7678 - acc: 0.7054 - val_loss: 1.0391 - val_acc: 0.5300\n", "Epoch 557/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7512 - acc: 0.7278\n", "Epoch 00557: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7509 - acc: 0.7279 - val_loss: 1.0397 - val_acc: 0.5287\n", "Epoch 558/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7583 - acc: 0.7116\n", "Epoch 00558: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7588 - acc: 0.7114 - val_loss: 1.0411 - val_acc: 0.5274\n", "Epoch 559/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7702 - acc: 0.6999\n", "Epoch 00559: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7704 - acc: 0.7001 - val_loss: 1.0418 - val_acc: 0.5274\n", "Epoch 560/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7591 - acc: 0.7154\n", "Epoch 00560: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7584 - acc: 0.7159 - val_loss: 1.0416 - val_acc: 0.5274\n", "Epoch 561/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7588 - acc: 0.7135\n", "Epoch 00561: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7584 - acc: 0.7136 - val_loss: 1.0430 - val_acc: 0.5261\n", "Epoch 562/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7473 - acc: 0.7127\n", "Epoch 00562: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7476 - acc: 0.7125 - val_loss: 1.0447 - val_acc: 0.5248\n", "Epoch 563/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7512 - acc: 0.7139\n", "Epoch 00563: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7512 - acc: 0.7133 - val_loss: 1.0431 - val_acc: 0.5248\n", "Epoch 564/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7547 - acc: 0.7214\n", "Epoch 00564: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7549 - acc: 0.7215 - val_loss: 1.0419 - val_acc: 0.5287\n", "Epoch 565/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7583 - acc: 0.7056\n", "Epoch 00565: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7586 - acc: 0.7054 - val_loss: 1.0422 - val_acc: 0.5287\n", "Epoch 566/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7594 - acc: 0.7037\n", "Epoch 00566: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7603 - acc: 0.7031 - val_loss: 1.0430 - val_acc: 0.5274\n", "Epoch 567/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7648 - acc: 0.7097\n", "Epoch 00567: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7644 - acc: 0.7099 - val_loss: 1.0439 - val_acc: 0.5248\n", "Epoch 568/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7554 - acc: 0.7199\n", "Epoch 00568: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7553 - acc: 0.7200 - val_loss: 1.0448 - val_acc: 0.5261\n", "Epoch 569/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.7580 - acc: 0.7039\n", "Epoch 00569: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7584 - acc: 0.7042 - val_loss: 1.0432 - val_acc: 0.5326\n", "Epoch 570/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7498 - acc: 0.7146\n", "Epoch 00570: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7503 - acc: 0.7144 - val_loss: 1.0414 - val_acc: 0.5313\n", "Epoch 571/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7557 - acc: 0.7184\n", "Epoch 00571: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7556 - acc: 0.7185 - val_loss: 1.0411 - val_acc: 0.5287\n", "Epoch 572/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7571 - acc: 0.7169\n", "Epoch 00572: val_acc did not improve from 0.53394\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7574 - acc: 0.7166 - val_loss: 1.0424 - val_acc: 0.5313\n", "Epoch 573/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7593 - acc: 0.7221\n", "Epoch 00573: val_acc improved from 0.53394 to 0.53525, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 0.7595 - acc: 0.7219 - val_loss: 1.0407 - val_acc: 0.5352\n", "Epoch 574/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7467 - acc: 0.7206\n", "Epoch 00574: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7466 - acc: 0.7208 - val_loss: 1.0393 - val_acc: 0.5313\n", "Epoch 575/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7587 - acc: 0.7097\n", "Epoch 00575: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7588 - acc: 0.7099 - val_loss: 1.0410 - val_acc: 0.5313\n", "Epoch 576/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7440 - acc: 0.7364\n", "Epoch 00576: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7449 - acc: 0.7358 - val_loss: 1.0431 - val_acc: 0.5313\n", "Epoch 577/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7456 - acc: 0.7259\n", "Epoch 00577: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7458 - acc: 0.7257 - val_loss: 1.0454 - val_acc: 0.5339\n", "Epoch 578/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7405 - acc: 0.7353\n", "Epoch 00578: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7402 - acc: 0.7358 - val_loss: 1.0428 - val_acc: 0.5326\n", "Epoch 579/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7543 - acc: 0.7255\n", "Epoch 00579: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7542 - acc: 0.7257 - val_loss: 1.0432 - val_acc: 0.5326\n", "Epoch 580/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7447 - acc: 0.7278\n", "Epoch 00580: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7447 - acc: 0.7275 - val_loss: 1.0428 - val_acc: 0.5352\n", "Epoch 581/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7370 - acc: 0.7304\n", "Epoch 00581: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7375 - acc: 0.7302 - val_loss: 1.0440 - val_acc: 0.5313\n", "Epoch 582/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7430 - acc: 0.7236\n", "Epoch 00582: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7423 - acc: 0.7242 - val_loss: 1.0438 - val_acc: 0.5313\n", "Epoch 583/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7292 - acc: 0.7455\n", "Epoch 00583: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7293 - acc: 0.7456 - val_loss: 1.0439 - val_acc: 0.5300\n", "Epoch 584/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7464 - acc: 0.7154\n", "Epoch 00584: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7463 - acc: 0.7155 - val_loss: 1.0451 - val_acc: 0.5274\n", "Epoch 585/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7541 - acc: 0.7165\n", "Epoch 00585: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7542 - acc: 0.7166 - val_loss: 1.0462 - val_acc: 0.5300\n", "Epoch 586/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7376 - acc: 0.7285\n", "Epoch 00586: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7373 - acc: 0.7287 - val_loss: 1.0452 - val_acc: 0.5339\n", "Epoch 587/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7408 - acc: 0.7188\n", "Epoch 00587: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7409 - acc: 0.7185 - val_loss: 1.0472 - val_acc: 0.5326\n", "Epoch 588/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7452 - acc: 0.7150\n", "Epoch 00588: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7459 - acc: 0.7148 - val_loss: 1.0475 - val_acc: 0.5287\n", "Epoch 589/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7361 - acc: 0.7244\n", "Epoch 00589: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7364 - acc: 0.7242 - val_loss: 1.0464 - val_acc: 0.5326\n", "Epoch 590/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7343 - acc: 0.7327\n", "Epoch 00590: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7341 - acc: 0.7328 - val_loss: 1.0460 - val_acc: 0.5274\n", "Epoch 591/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7445 - acc: 0.7240\n", "Epoch 00591: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7453 - acc: 0.7230 - val_loss: 1.0452 - val_acc: 0.5287\n", "Epoch 592/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7364 - acc: 0.7248\n", "Epoch 00592: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7359 - acc: 0.7253 - val_loss: 1.0466 - val_acc: 0.5287\n", "Epoch 593/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7315 - acc: 0.7308\n", "Epoch 00593: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7316 - acc: 0.7306 - val_loss: 1.0484 - val_acc: 0.5326\n", "Epoch 594/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7353 - acc: 0.7203\n", "Epoch 00594: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7358 - acc: 0.7200 - val_loss: 1.0456 - val_acc: 0.5313\n", "Epoch 595/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7412 - acc: 0.7248\n", "Epoch 00595: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7415 - acc: 0.7245 - val_loss: 1.0440 - val_acc: 0.5313\n", "Epoch 596/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7402 - acc: 0.7308\n", "Epoch 00596: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7401 - acc: 0.7306 - val_loss: 1.0465 - val_acc: 0.5326\n", "Epoch 597/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7335 - acc: 0.7259\n", "Epoch 00597: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7340 - acc: 0.7253 - val_loss: 1.0447 - val_acc: 0.5300\n", "Epoch 598/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7458 - acc: 0.7123\n", "Epoch 00598: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7464 - acc: 0.7121 - val_loss: 1.0461 - val_acc: 0.5300\n", "Epoch 599/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7275 - acc: 0.7285\n", "Epoch 00599: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7285 - acc: 0.7283 - val_loss: 1.0473 - val_acc: 0.5339\n", "Epoch 600/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7275 - acc: 0.7417\n", "Epoch 00600: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7274 - acc: 0.7418 - val_loss: 1.0452 - val_acc: 0.5300\n", "Epoch 601/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7331 - acc: 0.7308\n", "Epoch 00601: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7333 - acc: 0.7306 - val_loss: 1.0452 - val_acc: 0.5313\n", "Epoch 602/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7388 - acc: 0.7221\n", "Epoch 00602: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7384 - acc: 0.7223 - val_loss: 1.0446 - val_acc: 0.5287\n", "Epoch 603/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7412 - acc: 0.7285\n", "Epoch 00603: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7409 - acc: 0.7290 - val_loss: 1.0444 - val_acc: 0.5274\n", "Epoch 604/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7348 - acc: 0.7267\n", "Epoch 00604: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7348 - acc: 0.7268 - val_loss: 1.0472 - val_acc: 0.5313\n", "Epoch 605/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7250 - acc: 0.7413\n", "Epoch 00605: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7257 - acc: 0.7407 - val_loss: 1.0473 - val_acc: 0.5326\n", "Epoch 606/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7303 - acc: 0.7334\n", "Epoch 00606: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7307 - acc: 0.7336 - val_loss: 1.0494 - val_acc: 0.5339\n", "Epoch 607/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7413 - acc: 0.7267\n", "Epoch 00607: val_acc did not improve from 0.53525\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7413 - acc: 0.7264 - val_loss: 1.0477 - val_acc: 0.5339\n", "Epoch 608/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7397 - acc: 0.7236\n", "Epoch 00608: val_acc improved from 0.53525 to 0.53655, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 6s 2ms/sample - loss: 0.7401 - acc: 0.7230 - val_loss: 1.0476 - val_acc: 0.5366\n", "Epoch 609/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7295 - acc: 0.7304\n", "Epoch 00609: val_acc did not improve from 0.53655\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7294 - acc: 0.7306 - val_loss: 1.0474 - val_acc: 0.5326\n", "Epoch 610/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7330 - acc: 0.7361\n", "Epoch 00610: val_acc did not improve from 0.53655\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7327 - acc: 0.7362 - val_loss: 1.0477 - val_acc: 0.5339\n", "Epoch 611/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7330 - acc: 0.7248\n", "Epoch 00611: val_acc improved from 0.53655 to 0.53786, saving model to /content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/notebooks/audio-new/models/OPENL3_audio_api_train_test-1-500-epochs-0.5_hop--BEST_MODEL-w-VAL--1e-6-lr-0.2-dropout-512-feat-map-batch-norm-3-cnn-layers.h5\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7336 - acc: 0.7242 - val_loss: 1.0461 - val_acc: 0.5379\n", "Epoch 612/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7263 - acc: 0.7346\n", "Epoch 00612: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7260 - acc: 0.7347 - val_loss: 1.0444 - val_acc: 0.5366\n", "Epoch 613/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7204 - acc: 0.7395\n", "Epoch 00613: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7207 - acc: 0.7396 - val_loss: 1.0458 - val_acc: 0.5352\n", "Epoch 614/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7300 - acc: 0.7285\n", "Epoch 00614: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7305 - acc: 0.7283 - val_loss: 1.0450 - val_acc: 0.5339\n", "Epoch 615/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7265 - acc: 0.7319\n", "Epoch 00615: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7270 - acc: 0.7317 - val_loss: 1.0455 - val_acc: 0.5326\n", "Epoch 616/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7291 - acc: 0.7282\n", "Epoch 00616: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7292 - acc: 0.7275 - val_loss: 1.0431 - val_acc: 0.5300\n", "Epoch 617/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7297 - acc: 0.7331\n", "Epoch 00617: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7295 - acc: 0.7332 - val_loss: 1.0439 - val_acc: 0.5339\n", "Epoch 618/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7162 - acc: 0.7372\n", "Epoch 00618: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7161 - acc: 0.7373 - val_loss: 1.0452 - val_acc: 0.5352\n", "Epoch 619/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7245 - acc: 0.7368\n", "Epoch 00619: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7255 - acc: 0.7362 - val_loss: 1.0454 - val_acc: 0.5313\n", "Epoch 620/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7108 - acc: 0.7395\n", "Epoch 00620: val_acc did not improve from 0.53786\n", "2661/2661 [==============================] - 5s 2ms/sample - loss: 0.7127 - acc: 0.7388 - val_loss: 1.0478 - val_acc: 0.5313\n", "Epoch 00620: early stopping\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] } ] }