{ "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": "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 , target_filepath ):\n", " X , y = self.get_feature_batch(mp4_filepath , target_filepath) #Preproccess \n", " X = np.asarray(X) #You may get a bug TODO: trimming \n", "\n", " soft_pred = self.model.predict(X)\n", "\n", "\n", " return soft_pred # (0.1 ,0.2 , 0.7)\n", "\n", "\n", " \n", "\n", " def load_model(self, best_model_filepath):\n", " self.model = load_model(best_model_filepath)\n", " \n", " return \n", "\n", " def train(self, X_train , y_train , epochs=500 , batch_size=32 , X_val=None , Y_val=None , val_split=0.1, save_path = None):\n", "\n", " \"\"\"\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", "\n", " #TODO: There are some bugs here \n", " # Next Steps:\n", " # 1. Transfer learning\n", " # 2. Try CNN-LSTM approach \n", " # 3. For deep networks, try residual blocks\n", " # 4. Look for other model architecture to use with openSmile/VggISH/mel-spec features \n", "\n", " \n", " inputs = keras.Input(shape=[11,6144])\n", "\n", " cnn1 = tf.keras.layers.Conv1D(\n", " 512, 3, activation='selu')\n", "\n", " maxpool_1 = tf.keras.layers.MaxPooling1D(pool_size=2,\n", " strides=1, padding='valid')\n", " \n", " dropout_1 = tf.keras.layers.Dropout(0.3)\n", "\n", " cnn2 = tf.keras.layers.Conv1D(\n", " 512, 3, activation='selu')\n", "\n", " maxpool_2 = tf.keras.layers.MaxPooling1D(pool_size=2,\n", " strides=1, padding='valid')\n", " \n", " dropout_2 = tf.keras.layers.Dropout(0.3)\n", "\n", " cnn3 = tf.keras.layers.Conv1D(\n", " 512, 3, activation='selu')\n", " \n", "\n", " maxpool_3 = tf.keras.layers.MaxPooling1D(pool_size=2,\n", " strides=1, padding='valid')\n", " \n", " dropout_3 = tf.keras.layers.Dropout(0.3)\n", "\n", " \n", "\n", "\n", "\n", "\n", " \n", " recurrent_1 = keras.layers.Bidirectional(keras.layers.LSTM(10, return_sequences=True, input_shape=[None, 6144] , dropout=0.2 , activation='selu')) #A sequence of any length with dimensions 512 (i.e. 512 columns\n", " recurrent_2 = keras.layers.Bidirectional(keras.layers.LSTM(5))\n", " dense_1 = keras.layers.Dense(32 , activation='selu')\n", " dropout_1 = keras.layers.Dropout(0.5)\n", " softmax = keras.layers.Dense(3 , activation='softmax')\n", " \n", " inputs = keras.Input(shape=[11,6144])\n", " x = cnn1(inputs)\n", " x = maxpool_1(x)\n", " x = dropout_1(x)\n", " x = cnn2(x)\n", " x = maxpool_2(x)\n", " x = dropout_2(x)\n", " x = cnn3(x)\n", " x = maxpool_3(x)\n", " x = dropout_3(x)\n", " x = recurrent_1(x)\n", " x = recurrent_2(x)\n", " x = dense_1(x)\n", " x = dropout_1(x)\n", " outputs = softmax(x)\n", "\n", "\n", " recurrent_1 = keras.layers.Bidirectional(keras.layers.LSTM(10, return_sequences=True, input_shape=[None, 6144] , dropout=0.2 , activation='selu')) #A sequence of any length with dimensions 512 (i.e. 512 columns\n", " recurrent_2 = keras.layers.Bidirectional(keras.layers.LSTM(5))\n", " dense_1 = keras.layers.Dense(32 , activation='selu')\n", " dropout_1 = keras.layers.Dropout(0.5)\n", " softmax = keras.layers.Dense(3 , activation='softmax')\n", "\n", " # x = recurrent_1(inputs)\n", " # x = recurrent_2(x)\n", " # x = dense_1(x)\n", " # x = dropout_1(x)\n", " # outputs = softmax(x)\n", "\n", " rnn_ae = keras.Model(inputs=inputs, outputs=outputs)\n", "\n", " lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n", " initial_learning_rate=1e-6,\n", " decay_steps=10000,\n", " decay_rate=0.9)\n", " opt = keras.optimizers.Adam(learning_rate=lr_schedule)\n", "\n", " rnn_ae.compile(loss='sparse_categorical_crossentropy' , optimizer=opt , metrics=['accuracy'])\n", "\n", " history = None \n", "\n", " # if X_val == None and Y_val == None:\n", " # es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200)\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=4000 , batch_size=batch_size, validation_split=val_split , callbacks=[es, mc])\n", "\n", " # else:\n", " es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200)\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=4000 , batch_size=batch_size, validation_data=(X_val, Y_val), callbacks=[es, mc])\n", "\n", "\n", " if save_path != None:\n", " assert save_path[-3:] == '.h5'\n", " rnn_ae.save(save_path)\n", " return rnn_ae , history\n", "\n", " def preprocess(self, mp4_file_dr , target_label_path=None , hop_int=1):\n", "\n", " \"\"\"\n", " Outputs:\n", " - 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 openSMILE library.\n", " \"\"\"\n", "\n", " \n", "\n", " \n", " \n", " # mp4_filename = os.path.basename(mp4_filepath)\n", " # audio_home_dir = os.path.dirname(mp4_filepath)\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", " counter = -1\n", "\n", " for f in video_files: \n", "\n", "\n", " counter += 1 \n", "\n", " if counter >= 1000:\n", " continue;\n", "\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", "\n", "\n", "\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=0.5)\n", "\n", " subprocess.check_output(\"rm *.wav\", shell=True)\n", "\n", " X_train_padded = tf.keras.preprocessing.sequence.pad_sequences(X_arr , maxlen=11)\n", " X_train_padded = np.asarray(X_train_padded , dtype='float32')\n", " print(X_train_padded.shape)\n", "\n", " return X_train_padded , sr , targets\n", "\n", "\n", " # Read in each video file and add the (m,n) feature matrix to a 3D array\n", "\n", " " ], "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": "2c84fb04-ae6b-45ef-a9cd-441db0b8ab02", "colab": { "base_uri": "https://localhost:8080/", "height": 118 } }, "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 google.colab import drive\n", "drive.mount('/content/gdrive' , force_remount=True)\n", "\n", "\n", "\n", "from tensorflow.keras.models import load_model\n", "from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_audio\n", "import glob\n", "import time\n", "import importlib\n", "from sklearn.preprocessing import StandardScaler , Normalizer\n", "from subprocess import Popen, PIPE, STDOUT\n", "import librosa\n", "import os\n", "import pickle\n", "import openl3\n", "import soundfile as sf\n", "import importlib\n", "importlib.reload(openl3)\n", "\n", "\n", "from pydub import AudioSegment\n", "import argparse\n", "import audioread\n", "import time\n", "import numpy as np\n", "import subprocess\n", "from tensorflow import keras\n", "from tensorflow.keras.callbacks import EarlyStopping\n", "from tensorflow.keras.callbacks import ModelCheckpoint\n", "from matplotlib import pyplot\n", "import tensorflow as tf\n", "\n", "from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor\n", "from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional\n", "\n", "\n", "\n", "#Instantiate audio_model object\n", "model_test = audio_model()\n", "\n", " #Extract and preprocess X_train and Y_train from .mp4 file paths\n", "\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", "\n", " # X_train_save_path = \"\" # Enter .pickle path\n", " # Y_train_save_path = \"\" # Enter .pickle path\n", "\n", "\n", "\n", " # subprocess.check_output(\"cp '/content/gdrive/My Drive/Machine-Learning-Projects/cs231n-project/datasets/emotiw/Train_labels.txt' '/content/'\", shell=True)\n", " # X_train, sr , Y_train = model_test.preprocess('Train/' , target_label_path='Train_labels.txt')\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) \n", "\n", " #Train the model\n", "\n", "\n", " # MAKE SURE TO CHANGE THE SAVE PATH\n", "\n", " # Model_path = \"\"\n", "\n", " # model_test = audio_model()\n", " # model, history = model_test.train(X_train, Y_train , save_path=Model_path )\n", "\n" ], "execution_count": 3, "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" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "keGm229-rlYW", "colab_type": "code", "outputId": "87188894-1513-42a1-f825-628fa67e7a38", "colab": { "base_uri": "https://localhost:8080/", "height": 84 } }, "source": [ "import numpy as np\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" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ "(2661, 11, 6144)\n", "(2661,)\n", "(766, 11, 6144)\n", "(766,)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "O4wBUo2IGTZa", "colab_type": "code", "colab": {} }, "source": [ " # Normalize\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", " " ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "JtgHGMFzJqcq", "colab_type": "code", "outputId": "0302c273-8402-4bbd-97b2-db264857448a", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "model_test = audio_model()\n", "Model_path = \"\" # enter your model path here\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.h5\"\n", "model, history = model_test.train(X_train_copy, Y_train , X_val=X_val_copy , Y_val=Y_val , save_path=Model_path )\n", "\n" ], "execution_count": 17, "outputs": [ { "output_type": "stream", "text": [ "Train on 2661 samples, validate on 766 samples\n", "Epoch 1/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.1004 - acc: 0.3245\n", "Epoch 00001: val_acc improved from -inf to 0.29765, 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.h5\n", "2661/2661 [==============================] - 10s 4ms/sample - loss: 1.1004 - acc: 0.3243 - val_loss: 1.0989 - val_acc: 0.2977\n", "Epoch 2/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0988 - acc: 0.3336\n", "Epoch 00002: val_acc improved from 0.29765 to 0.32768, 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0988 - acc: 0.3337 - val_loss: 1.0988 - val_acc: 0.3277\n", "Epoch 3/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0974 - acc: 0.3524\n", "Epoch 00003: val_acc improved from 0.32768 to 0.36945, 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0975 - acc: 0.3521 - val_loss: 1.0988 - val_acc: 0.3695\n", "Epoch 4/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0976 - acc: 0.3547\n", "Epoch 00004: val_acc improved from 0.36945 to 0.37076, 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0976 - acc: 0.3540 - val_loss: 1.0992 - val_acc: 0.3708\n", "Epoch 5/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 1.0961 - acc: 0.3754\n", "Epoch 00005: val_acc did not improve from 0.37076\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0961 - acc: 0.3747 - val_loss: 1.1000 - val_acc: 0.3055\n", "Epoch 6/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0967 - acc: 0.3648\n", "Epoch 00006: val_acc did not improve from 0.37076\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0967 - acc: 0.3653 - val_loss: 1.1001 - val_acc: 0.3681\n", "Epoch 7/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0956 - acc: 0.3709\n", "Epoch 00007: val_acc improved from 0.37076 to 0.37728, 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0957 - acc: 0.3709 - val_loss: 1.0999 - val_acc: 0.3773\n", "Epoch 8/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0961 - acc: 0.3626\n", "Epoch 00008: val_acc improved from 0.37728 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0961 - acc: 0.3630 - val_loss: 1.0989 - val_acc: 0.3995\n", "Epoch 9/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0947 - acc: 0.3795\n", "Epoch 00009: val_acc improved from 0.39948 to 0.40209, 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0946 - acc: 0.3796 - val_loss: 1.0985 - val_acc: 0.4021\n", "Epoch 10/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0945 - acc: 0.3799\n", "Epoch 00010: val_acc improved from 0.40209 to 0.40339, 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0945 - acc: 0.3799 - val_loss: 1.0983 - val_acc: 0.4034\n", "Epoch 11/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0937 - acc: 0.3901\n", "Epoch 00011: val_acc improved from 0.40339 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0936 - acc: 0.3905 - val_loss: 1.0984 - val_acc: 0.4086\n", "Epoch 12/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0933 - acc: 0.3852\n", "Epoch 00012: val_acc did not improve from 0.40862\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0933 - acc: 0.3848 - val_loss: 1.0975 - val_acc: 0.4060\n", "Epoch 13/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0930 - acc: 0.3968\n", "Epoch 00013: val_acc improved from 0.40862 to 0.41645, 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0931 - acc: 0.3965 - val_loss: 1.0970 - val_acc: 0.4164\n", "Epoch 14/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0902 - acc: 0.4138\n", "Epoch 00014: val_acc did not improve from 0.41645\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0902 - acc: 0.4134 - val_loss: 1.0969 - val_acc: 0.4164\n", "Epoch 15/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0912 - acc: 0.4006\n", "Epoch 00015: val_acc did not improve from 0.41645\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0912 - acc: 0.4002 - val_loss: 1.0952 - val_acc: 0.4112\n", "Epoch 16/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0907 - acc: 0.4104\n", "Epoch 00016: val_acc improved from 0.41645 to 0.42298, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 1.0906 - acc: 0.4111 - val_loss: 1.0943 - val_acc: 0.4230\n", "Epoch 17/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0891 - acc: 0.4070\n", "Epoch 00017: val_acc did not improve from 0.42298\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0891 - acc: 0.4062 - val_loss: 1.0940 - val_acc: 0.4138\n", "Epoch 18/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0882 - acc: 0.4232\n", "Epoch 00018: val_acc did not improve from 0.42298\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0882 - acc: 0.4231 - val_loss: 1.0940 - val_acc: 0.4191\n", "Epoch 19/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0865 - acc: 0.4315\n", "Epoch 00019: val_acc did not improve from 0.42298\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0865 - acc: 0.4314 - val_loss: 1.0928 - val_acc: 0.4191\n", "Epoch 20/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0868 - acc: 0.4134\n", "Epoch 00020: val_acc did not improve from 0.42298\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0868 - acc: 0.4138 - val_loss: 1.0911 - val_acc: 0.4099\n", "Epoch 21/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0862 - acc: 0.4273\n", "Epoch 00021: val_acc improved from 0.42298 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 1.0862 - acc: 0.4273 - val_loss: 1.0909 - val_acc: 0.4282\n", "Epoch 22/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0834 - acc: 0.4273\n", "Epoch 00022: val_acc did not improve from 0.42820\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0834 - acc: 0.4273 - val_loss: 1.0900 - val_acc: 0.4230\n", "Epoch 23/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0875 - acc: 0.4198\n", "Epoch 00023: val_acc did not improve from 0.42820\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0876 - acc: 0.4198 - val_loss: 1.0882 - val_acc: 0.4191\n", "Epoch 24/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0842 - acc: 0.4360\n", "Epoch 00024: val_acc did not improve from 0.42820\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0843 - acc: 0.4359 - val_loss: 1.0872 - val_acc: 0.4256\n", "Epoch 25/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 1.0825 - acc: 0.4425\n", "Epoch 00025: val_acc did not improve from 0.42820\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0824 - acc: 0.4419 - val_loss: 1.0858 - val_acc: 0.4256\n", "Epoch 26/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0813 - acc: 0.4341\n", "Epoch 00026: val_acc improved from 0.42820 to 0.43081, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 1.0813 - acc: 0.4340 - val_loss: 1.0859 - val_acc: 0.4308\n", "Epoch 27/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0809 - acc: 0.4477\n", "Epoch 00027: val_acc did not improve from 0.43081\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0809 - acc: 0.4476 - val_loss: 1.0844 - val_acc: 0.4282\n", "Epoch 28/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0789 - acc: 0.4631\n", "Epoch 00028: val_acc did not improve from 0.43081\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0789 - acc: 0.4634 - val_loss: 1.0826 - val_acc: 0.4282\n", "Epoch 29/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0781 - acc: 0.4450\n", "Epoch 00029: val_acc improved from 0.43081 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0780 - acc: 0.4457 - val_loss: 1.0814 - val_acc: 0.4347\n", "Epoch 30/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0782 - acc: 0.4477\n", "Epoch 00030: val_acc did not improve from 0.43473\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0781 - acc: 0.4483 - val_loss: 1.0804 - val_acc: 0.4334\n", "Epoch 31/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 1.0735 - acc: 0.4775\n", "Epoch 00031: val_acc improved from 0.43473 to 0.43734, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 1.0732 - acc: 0.4780 - val_loss: 1.0784 - val_acc: 0.4373\n", "Epoch 32/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0770 - acc: 0.4518\n", "Epoch 00032: val_acc did not improve from 0.43734\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0770 - acc: 0.4513 - val_loss: 1.0765 - val_acc: 0.4373\n", "Epoch 33/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0729 - acc: 0.4623\n", "Epoch 00033: val_acc improved from 0.43734 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0730 - acc: 0.4619 - val_loss: 1.0762 - val_acc: 0.4386\n", "Epoch 34/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0725 - acc: 0.4608\n", "Epoch 00034: val_acc improved from 0.43864 to 0.44256, 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0726 - acc: 0.4607 - val_loss: 1.0750 - val_acc: 0.4426\n", "Epoch 35/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0712 - acc: 0.4706\n", "Epoch 00035: val_acc improved from 0.44256 to 0.44909, 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0712 - acc: 0.4705 - val_loss: 1.0741 - val_acc: 0.4491\n", "Epoch 36/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0699 - acc: 0.4654\n", "Epoch 00036: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0697 - acc: 0.4660 - val_loss: 1.0737 - val_acc: 0.4439\n", "Epoch 37/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0683 - acc: 0.4699\n", "Epoch 00037: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0684 - acc: 0.4697 - val_loss: 1.0709 - val_acc: 0.4413\n", "Epoch 38/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 1.0673 - acc: 0.4809\n", "Epoch 00038: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0673 - acc: 0.4806 - val_loss: 1.0723 - val_acc: 0.4465\n", "Epoch 39/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0681 - acc: 0.4725\n", "Epoch 00039: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0679 - acc: 0.4731 - val_loss: 1.0704 - val_acc: 0.4465\n", "Epoch 40/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0691 - acc: 0.4593\n", "Epoch 00040: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0693 - acc: 0.4589 - val_loss: 1.0693 - val_acc: 0.4386\n", "Epoch 41/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0639 - acc: 0.4853\n", "Epoch 00041: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0640 - acc: 0.4844 - val_loss: 1.0682 - val_acc: 0.4452\n", "Epoch 42/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0632 - acc: 0.4736\n", "Epoch 00042: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0633 - acc: 0.4731 - val_loss: 1.0665 - val_acc: 0.4491\n", "Epoch 43/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0632 - acc: 0.4793\n", "Epoch 00043: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0631 - acc: 0.4795 - val_loss: 1.0651 - val_acc: 0.4426\n", "Epoch 44/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0619 - acc: 0.4657\n", "Epoch 00044: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0619 - acc: 0.4652 - val_loss: 1.0653 - val_acc: 0.4478\n", "Epoch 45/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0612 - acc: 0.4778\n", "Epoch 00045: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0611 - acc: 0.4776 - val_loss: 1.0633 - val_acc: 0.4465\n", "Epoch 46/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0575 - acc: 0.4838\n", "Epoch 00046: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0576 - acc: 0.4837 - val_loss: 1.0621 - val_acc: 0.4426\n", "Epoch 47/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0583 - acc: 0.4804\n", "Epoch 00047: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0582 - acc: 0.4810 - val_loss: 1.0615 - val_acc: 0.4491\n", "Epoch 48/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0559 - acc: 0.4793\n", "Epoch 00048: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0558 - acc: 0.4795 - val_loss: 1.0609 - val_acc: 0.4491\n", "Epoch 49/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0549 - acc: 0.4767\n", "Epoch 00049: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0551 - acc: 0.4765 - val_loss: 1.0603 - val_acc: 0.4491\n", "Epoch 50/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0527 - acc: 0.4872\n", "Epoch 00050: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0526 - acc: 0.4870 - val_loss: 1.0621 - val_acc: 0.4426\n", "Epoch 51/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0549 - acc: 0.4721\n", "Epoch 00051: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0549 - acc: 0.4724 - val_loss: 1.0601 - val_acc: 0.4491\n", "Epoch 52/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0514 - acc: 0.5049\n", "Epoch 00052: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0514 - acc: 0.5051 - val_loss: 1.0583 - val_acc: 0.4452\n", "Epoch 53/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0477 - acc: 0.4887\n", "Epoch 00053: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0478 - acc: 0.4885 - val_loss: 1.0563 - val_acc: 0.4465\n", "Epoch 54/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0496 - acc: 0.4921\n", "Epoch 00054: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0497 - acc: 0.4919 - val_loss: 1.0572 - val_acc: 0.4478\n", "Epoch 55/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0502 - acc: 0.4819\n", "Epoch 00055: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0501 - acc: 0.4829 - val_loss: 1.0561 - val_acc: 0.4439\n", "Epoch 56/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0467 - acc: 0.4932\n", "Epoch 00056: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0466 - acc: 0.4938 - val_loss: 1.0558 - val_acc: 0.4413\n", "Epoch 57/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0458 - acc: 0.4906\n", "Epoch 00057: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0457 - acc: 0.4908 - val_loss: 1.0554 - val_acc: 0.4439\n", "Epoch 58/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0445 - acc: 0.4921\n", "Epoch 00058: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0443 - acc: 0.4927 - val_loss: 1.0524 - val_acc: 0.4439\n", "Epoch 59/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0454 - acc: 0.4985\n", "Epoch 00059: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0454 - acc: 0.4983 - val_loss: 1.0532 - val_acc: 0.4426\n", "Epoch 60/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0450 - acc: 0.4944\n", "Epoch 00060: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0451 - acc: 0.4942 - val_loss: 1.0518 - val_acc: 0.4439\n", "Epoch 61/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0431 - acc: 0.4895\n", "Epoch 00061: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0433 - acc: 0.4885 - val_loss: 1.0506 - val_acc: 0.4439\n", "Epoch 62/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 1.0392 - acc: 0.5057\n", "Epoch 00062: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0392 - acc: 0.5058 - val_loss: 1.0497 - val_acc: 0.4426\n", "Epoch 63/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0388 - acc: 0.5008\n", "Epoch 00063: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0389 - acc: 0.5006 - val_loss: 1.0505 - val_acc: 0.4439\n", "Epoch 64/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0394 - acc: 0.5008\n", "Epoch 00064: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0395 - acc: 0.5009 - val_loss: 1.0495 - val_acc: 0.4439\n", "Epoch 65/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0359 - acc: 0.5105\n", "Epoch 00065: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0360 - acc: 0.5103 - val_loss: 1.0479 - val_acc: 0.4452\n", "Epoch 66/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0355 - acc: 0.5026\n", "Epoch 00066: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0354 - acc: 0.5028 - val_loss: 1.0478 - val_acc: 0.4413\n", "Epoch 67/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0359 - acc: 0.4944\n", "Epoch 00067: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0358 - acc: 0.4949 - val_loss: 1.0475 - val_acc: 0.4426\n", "Epoch 68/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0338 - acc: 0.5068\n", "Epoch 00068: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0340 - acc: 0.5062 - val_loss: 1.0458 - val_acc: 0.4452\n", "Epoch 69/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0339 - acc: 0.4955\n", "Epoch 00069: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0341 - acc: 0.4953 - val_loss: 1.0454 - val_acc: 0.4465\n", "Epoch 70/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0357 - acc: 0.4925\n", "Epoch 00070: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0359 - acc: 0.4919 - val_loss: 1.0456 - val_acc: 0.4452\n", "Epoch 71/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0299 - acc: 0.5075\n", "Epoch 00071: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0294 - acc: 0.5085 - val_loss: 1.0433 - val_acc: 0.4452\n", "Epoch 72/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0341 - acc: 0.4974\n", "Epoch 00072: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0339 - acc: 0.4979 - val_loss: 1.0446 - val_acc: 0.4452\n", "Epoch 73/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0317 - acc: 0.5045\n", "Epoch 00073: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0317 - acc: 0.5043 - val_loss: 1.0436 - val_acc: 0.4478\n", "Epoch 74/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0258 - acc: 0.5120\n", "Epoch 00074: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0256 - acc: 0.5130 - val_loss: 1.0433 - val_acc: 0.4452\n", "Epoch 75/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0251 - acc: 0.5045\n", "Epoch 00075: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0249 - acc: 0.5047 - val_loss: 1.0417 - val_acc: 0.4452\n", "Epoch 76/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0210 - acc: 0.5203\n", "Epoch 00076: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0212 - acc: 0.5201 - val_loss: 1.0407 - val_acc: 0.4465\n", "Epoch 77/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0275 - acc: 0.5113\n", "Epoch 00077: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0273 - acc: 0.5118 - val_loss: 1.0404 - val_acc: 0.4465\n", "Epoch 78/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0251 - acc: 0.4940\n", "Epoch 00078: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0253 - acc: 0.4930 - val_loss: 1.0394 - val_acc: 0.4478\n", "Epoch 79/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0256 - acc: 0.5056\n", "Epoch 00079: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0254 - acc: 0.5062 - val_loss: 1.0393 - val_acc: 0.4465\n", "Epoch 80/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0229 - acc: 0.5068\n", "Epoch 00080: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0226 - acc: 0.5070 - val_loss: 1.0379 - val_acc: 0.4465\n", "Epoch 81/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0262 - acc: 0.5098\n", "Epoch 00081: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0259 - acc: 0.5103 - val_loss: 1.0381 - val_acc: 0.4478\n", "Epoch 82/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 1.0198 - acc: 0.5191\n", "Epoch 00082: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0200 - acc: 0.5186 - val_loss: 1.0382 - val_acc: 0.4452\n", "Epoch 83/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0270 - acc: 0.4992\n", "Epoch 00083: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0270 - acc: 0.4994 - val_loss: 1.0380 - val_acc: 0.4452\n", "Epoch 84/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0228 - acc: 0.5038\n", "Epoch 00084: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0226 - acc: 0.5039 - val_loss: 1.0385 - val_acc: 0.4452\n", "Epoch 85/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0225 - acc: 0.5166\n", "Epoch 00085: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0224 - acc: 0.5167 - val_loss: 1.0389 - val_acc: 0.4452\n", "Epoch 86/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0149 - acc: 0.5211\n", "Epoch 00086: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0148 - acc: 0.5212 - val_loss: 1.0392 - val_acc: 0.4452\n", "Epoch 87/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0202 - acc: 0.5128\n", "Epoch 00087: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0202 - acc: 0.5126 - val_loss: 1.0377 - val_acc: 0.4465\n", "Epoch 88/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0183 - acc: 0.5072\n", "Epoch 00088: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0180 - acc: 0.5073 - val_loss: 1.0383 - val_acc: 0.4465\n", "Epoch 89/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0169 - acc: 0.5169\n", "Epoch 00089: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0167 - acc: 0.5171 - val_loss: 1.0356 - val_acc: 0.4439\n", "Epoch 90/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0195 - acc: 0.5026\n", "Epoch 00090: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0195 - acc: 0.5024 - val_loss: 1.0394 - val_acc: 0.4413\n", "Epoch 91/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0130 - acc: 0.5203\n", "Epoch 00091: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0131 - acc: 0.5201 - val_loss: 1.0346 - val_acc: 0.4452\n", "Epoch 92/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0157 - acc: 0.5132\n", "Epoch 00092: val_acc did not improve from 0.44909\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0156 - acc: 0.5133 - val_loss: 1.0346 - val_acc: 0.4452\n", "Epoch 93/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0075 - acc: 0.5207\n", "Epoch 00093: val_acc improved from 0.44909 to 0.45300, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 1.0074 - acc: 0.5209 - val_loss: 1.0326 - val_acc: 0.4530\n", "Epoch 94/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0155 - acc: 0.5113\n", "Epoch 00094: val_acc did not improve from 0.45300\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0155 - acc: 0.5107 - val_loss: 1.0333 - val_acc: 0.4478\n", "Epoch 95/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0094 - acc: 0.5215\n", "Epoch 00095: val_acc did not improve from 0.45300\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0094 - acc: 0.5216 - val_loss: 1.0328 - val_acc: 0.4491\n", "Epoch 96/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0141 - acc: 0.5169\n", "Epoch 00096: val_acc did not improve from 0.45300\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0140 - acc: 0.5167 - val_loss: 1.0323 - val_acc: 0.4491\n", "Epoch 97/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0118 - acc: 0.5102\n", "Epoch 00097: val_acc improved from 0.45300 to 0.45431, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 1.0119 - acc: 0.5100 - val_loss: 1.0311 - val_acc: 0.4543\n", "Epoch 98/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0074 - acc: 0.5169\n", "Epoch 00098: val_acc did not improve from 0.45431\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0075 - acc: 0.5163 - val_loss: 1.0319 - val_acc: 0.4530\n", "Epoch 99/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0081 - acc: 0.5211\n", "Epoch 00099: val_acc did not improve from 0.45431\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0082 - acc: 0.5216 - val_loss: 1.0321 - val_acc: 0.4504\n", "Epoch 100/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0131 - acc: 0.5053\n", "Epoch 00100: val_acc improved from 0.45431 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0130 - acc: 0.5054 - val_loss: 1.0304 - val_acc: 0.4556\n", "Epoch 101/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0046 - acc: 0.5248\n", "Epoch 00101: val_acc improved from 0.45561 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 1.0046 - acc: 0.5250 - val_loss: 1.0304 - val_acc: 0.4582\n", "Epoch 102/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0069 - acc: 0.5282\n", "Epoch 00102: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0069 - acc: 0.5280 - val_loss: 1.0306 - val_acc: 0.4543\n", "Epoch 103/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0098 - acc: 0.5083\n", "Epoch 00103: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0097 - acc: 0.5081 - val_loss: 1.0313 - val_acc: 0.4504\n", "Epoch 104/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0062 - acc: 0.5139\n", "Epoch 00104: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0061 - acc: 0.5133 - val_loss: 1.0303 - val_acc: 0.4530\n", "Epoch 105/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0004 - acc: 0.5237\n", "Epoch 00105: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0003 - acc: 0.5239 - val_loss: 1.0308 - val_acc: 0.4530\n", "Epoch 106/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0035 - acc: 0.5286\n", "Epoch 00106: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0036 - acc: 0.5287 - val_loss: 1.0297 - val_acc: 0.4582\n", "Epoch 107/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9987 - acc: 0.5279\n", "Epoch 00107: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9986 - acc: 0.5276 - val_loss: 1.0280 - val_acc: 0.4582\n", "Epoch 108/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0051 - acc: 0.5177\n", "Epoch 00108: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0052 - acc: 0.5175 - val_loss: 1.0307 - val_acc: 0.4478\n", "Epoch 109/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0037 - acc: 0.5184\n", "Epoch 00109: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0041 - acc: 0.5182 - val_loss: 1.0280 - val_acc: 0.4556\n", "Epoch 110/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9983 - acc: 0.5264\n", "Epoch 00110: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9983 - acc: 0.5265 - val_loss: 1.0291 - val_acc: 0.4530\n", "Epoch 111/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9971 - acc: 0.5173\n", "Epoch 00111: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9972 - acc: 0.5175 - val_loss: 1.0273 - val_acc: 0.4517\n", "Epoch 112/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0043 - acc: 0.5207\n", "Epoch 00112: val_acc did not improve from 0.45822\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0042 - acc: 0.5205 - val_loss: 1.0314 - val_acc: 0.4413\n", "Epoch 113/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9969 - acc: 0.5294\n", "Epoch 00113: val_acc improved from 0.45822 to 0.46214, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9965 - acc: 0.5299 - val_loss: 1.0268 - val_acc: 0.4621\n", "Epoch 114/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9965 - acc: 0.5324\n", "Epoch 00114: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9963 - acc: 0.5325 - val_loss: 1.0261 - val_acc: 0.4608\n", "Epoch 115/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9955 - acc: 0.5335\n", "Epoch 00115: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9956 - acc: 0.5336 - val_loss: 1.0252 - val_acc: 0.4543\n", "Epoch 116/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9918 - acc: 0.5377\n", "Epoch 00116: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9920 - acc: 0.5378 - val_loss: 1.0259 - val_acc: 0.4582\n", "Epoch 117/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9992 - acc: 0.5324\n", "Epoch 00117: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9992 - acc: 0.5321 - val_loss: 1.0238 - val_acc: 0.4582\n", "Epoch 118/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9993 - acc: 0.5267\n", "Epoch 00118: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9989 - acc: 0.5276 - val_loss: 1.0284 - val_acc: 0.4504\n", "Epoch 119/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0002 - acc: 0.5260\n", "Epoch 00119: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0002 - acc: 0.5254 - val_loss: 1.0245 - val_acc: 0.4569\n", "Epoch 120/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 1.0005 - acc: 0.5358\n", "Epoch 00120: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 1.0003 - acc: 0.5359 - val_loss: 1.0273 - val_acc: 0.4530\n", "Epoch 121/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9943 - acc: 0.5245\n", "Epoch 00121: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9946 - acc: 0.5239 - val_loss: 1.0273 - val_acc: 0.4504\n", "Epoch 122/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9912 - acc: 0.5256\n", "Epoch 00122: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9912 - acc: 0.5257 - val_loss: 1.0256 - val_acc: 0.4608\n", "Epoch 123/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9962 - acc: 0.5294\n", "Epoch 00123: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9959 - acc: 0.5299 - val_loss: 1.0244 - val_acc: 0.4582\n", "Epoch 124/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9956 - acc: 0.5350\n", "Epoch 00124: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9957 - acc: 0.5344 - val_loss: 1.0220 - val_acc: 0.4543\n", "Epoch 125/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9983 - acc: 0.5301\n", "Epoch 00125: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9985 - acc: 0.5295 - val_loss: 1.0202 - val_acc: 0.4582\n", "Epoch 126/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9934 - acc: 0.5350\n", "Epoch 00126: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9939 - acc: 0.5348 - val_loss: 1.0215 - val_acc: 0.4569\n", "Epoch 127/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9867 - acc: 0.5410\n", "Epoch 00127: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9866 - acc: 0.5415 - val_loss: 1.0233 - val_acc: 0.4569\n", "Epoch 128/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9855 - acc: 0.5463\n", "Epoch 00128: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9855 - acc: 0.5457 - val_loss: 1.0222 - val_acc: 0.4569\n", "Epoch 129/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9928 - acc: 0.5275\n", "Epoch 00129: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9928 - acc: 0.5272 - val_loss: 1.0217 - val_acc: 0.4595\n", "Epoch 130/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9882 - acc: 0.5343\n", "Epoch 00130: val_acc did not improve from 0.46214\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9880 - acc: 0.5348 - val_loss: 1.0230 - val_acc: 0.4608\n", "Epoch 131/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9903 - acc: 0.5316\n", "Epoch 00131: val_acc improved from 0.46214 to 0.46345, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9906 - acc: 0.5314 - val_loss: 1.0213 - val_acc: 0.4634\n", "Epoch 132/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9843 - acc: 0.5489\n", "Epoch 00132: val_acc did not improve from 0.46345\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9842 - acc: 0.5487 - val_loss: 1.0197 - val_acc: 0.4608\n", "Epoch 133/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9843 - acc: 0.5448\n", "Epoch 00133: val_acc did not improve from 0.46345\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9848 - acc: 0.5442 - val_loss: 1.0209 - val_acc: 0.4569\n", "Epoch 134/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9898 - acc: 0.5373\n", "Epoch 00134: val_acc did not improve from 0.46345\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9899 - acc: 0.5370 - val_loss: 1.0212 - val_acc: 0.4569\n", "Epoch 135/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9923 - acc: 0.5173\n", "Epoch 00135: val_acc improved from 0.46345 to 0.46606, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9924 - acc: 0.5171 - val_loss: 1.0197 - val_acc: 0.4661\n", "Epoch 136/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9873 - acc: 0.5414\n", "Epoch 00136: val_acc did not improve from 0.46606\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9875 - acc: 0.5411 - val_loss: 1.0214 - val_acc: 0.4543\n", "Epoch 137/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9854 - acc: 0.5478\n", "Epoch 00137: val_acc did not improve from 0.46606\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9851 - acc: 0.5483 - val_loss: 1.0203 - val_acc: 0.4608\n", "Epoch 138/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9909 - acc: 0.5422\n", "Epoch 00138: val_acc did not improve from 0.46606\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9909 - acc: 0.5419 - val_loss: 1.0196 - val_acc: 0.4634\n", "Epoch 139/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9847 - acc: 0.5410\n", "Epoch 00139: val_acc improved from 0.46606 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9852 - acc: 0.5408 - val_loss: 1.0167 - val_acc: 0.4726\n", "Epoch 140/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9820 - acc: 0.5365\n", "Epoch 00140: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9820 - acc: 0.5359 - val_loss: 1.0192 - val_acc: 0.4648\n", "Epoch 141/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9800 - acc: 0.5433\n", "Epoch 00141: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9804 - acc: 0.5427 - val_loss: 1.0176 - val_acc: 0.4661\n", "Epoch 142/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9866 - acc: 0.5282\n", "Epoch 00142: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9865 - acc: 0.5280 - val_loss: 1.0216 - val_acc: 0.4556\n", "Epoch 143/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9898 - acc: 0.5275\n", "Epoch 00143: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9899 - acc: 0.5276 - val_loss: 1.0156 - val_acc: 0.4674\n", "Epoch 144/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9808 - acc: 0.5523\n", "Epoch 00144: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9810 - acc: 0.5524 - val_loss: 1.0180 - val_acc: 0.4634\n", "Epoch 145/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9810 - acc: 0.5422\n", "Epoch 00145: val_acc did not improve from 0.47258\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9807 - acc: 0.5430 - val_loss: 1.0152 - val_acc: 0.4687\n", "Epoch 146/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9803 - acc: 0.5497\n", "Epoch 00146: val_acc improved from 0.47258 to 0.47650, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9803 - acc: 0.5494 - val_loss: 1.0144 - val_acc: 0.4765\n", "Epoch 147/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9809 - acc: 0.5471\n", "Epoch 00147: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9809 - acc: 0.5468 - val_loss: 1.0142 - val_acc: 0.4713\n", "Epoch 148/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9772 - acc: 0.5591\n", "Epoch 00148: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9775 - acc: 0.5588 - val_loss: 1.0158 - val_acc: 0.4700\n", "Epoch 149/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9764 - acc: 0.5433\n", "Epoch 00149: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9763 - acc: 0.5438 - val_loss: 1.0163 - val_acc: 0.4687\n", "Epoch 150/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9807 - acc: 0.5434\n", "Epoch 00150: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9805 - acc: 0.5438 - val_loss: 1.0159 - val_acc: 0.4621\n", "Epoch 151/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9779 - acc: 0.5395\n", "Epoch 00151: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9782 - acc: 0.5396 - val_loss: 1.0137 - val_acc: 0.4713\n", "Epoch 152/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9837 - acc: 0.5380\n", "Epoch 00152: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9838 - acc: 0.5374 - val_loss: 1.0148 - val_acc: 0.4687\n", "Epoch 153/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9732 - acc: 0.5569\n", "Epoch 00153: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9730 - acc: 0.5569 - val_loss: 1.0144 - val_acc: 0.4713\n", "Epoch 154/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9780 - acc: 0.5459\n", "Epoch 00154: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9778 - acc: 0.5460 - val_loss: 1.0119 - val_acc: 0.4765\n", "Epoch 155/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9794 - acc: 0.5467\n", "Epoch 00155: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9790 - acc: 0.5472 - val_loss: 1.0133 - val_acc: 0.4674\n", "Epoch 156/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9764 - acc: 0.5467\n", "Epoch 00156: val_acc did not improve from 0.47650\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9764 - acc: 0.5464 - val_loss: 1.0129 - val_acc: 0.4752\n", "Epoch 157/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9695 - acc: 0.5606\n", "Epoch 00157: val_acc improved from 0.47650 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9696 - acc: 0.5607 - val_loss: 1.0117 - val_acc: 0.4817\n", "Epoch 158/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9724 - acc: 0.5463\n", "Epoch 00158: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9721 - acc: 0.5468 - val_loss: 1.0160 - val_acc: 0.4621\n", "Epoch 159/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9792 - acc: 0.5463\n", "Epoch 00159: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9789 - acc: 0.5468 - val_loss: 1.0158 - val_acc: 0.4595\n", "Epoch 160/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9752 - acc: 0.5527\n", "Epoch 00160: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9754 - acc: 0.5524 - val_loss: 1.0113 - val_acc: 0.4713\n", "Epoch 161/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9760 - acc: 0.5456\n", "Epoch 00161: val_acc did not improve from 0.48172\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9755 - acc: 0.5460 - val_loss: 1.0118 - val_acc: 0.4661\n", "Epoch 162/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9764 - acc: 0.5474\n", "Epoch 00162: 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9763 - acc: 0.5475 - val_loss: 1.0101 - val_acc: 0.4830\n", "Epoch 163/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9750 - acc: 0.5527\n", "Epoch 00163: val_acc improved from 0.48303 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.h5\n", "2661/2661 [==============================] - 4s 2ms/sample - loss: 0.9749 - acc: 0.5528 - val_loss: 1.0093 - val_acc: 0.4856\n", "Epoch 164/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9727 - acc: 0.5471\n", "Epoch 00164: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9728 - acc: 0.5472 - val_loss: 1.0172 - val_acc: 0.4595\n", "Epoch 165/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9733 - acc: 0.5448\n", "Epoch 00165: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9734 - acc: 0.5449 - val_loss: 1.0113 - val_acc: 0.4726\n", "Epoch 166/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9589 - acc: 0.5693\n", "Epoch 00166: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9590 - acc: 0.5690 - val_loss: 1.0108 - val_acc: 0.4739\n", "Epoch 167/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9679 - acc: 0.5614\n", "Epoch 00167: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9679 - acc: 0.5618 - val_loss: 1.0086 - val_acc: 0.4752\n", "Epoch 168/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9719 - acc: 0.5557\n", "Epoch 00168: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9716 - acc: 0.5558 - val_loss: 1.0122 - val_acc: 0.4634\n", "Epoch 169/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9714 - acc: 0.5497\n", "Epoch 00169: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9713 - acc: 0.5494 - val_loss: 1.0119 - val_acc: 0.4648\n", "Epoch 170/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9726 - acc: 0.5591\n", "Epoch 00170: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9727 - acc: 0.5584 - val_loss: 1.0100 - val_acc: 0.4713\n", "Epoch 171/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9741 - acc: 0.5542\n", "Epoch 00171: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9738 - acc: 0.5547 - val_loss: 1.0088 - val_acc: 0.4739\n", "Epoch 172/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9656 - acc: 0.5610\n", "Epoch 00172: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9655 - acc: 0.5611 - val_loss: 1.0090 - val_acc: 0.4778\n", "Epoch 173/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9729 - acc: 0.5561\n", "Epoch 00173: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9733 - acc: 0.5562 - val_loss: 1.0100 - val_acc: 0.4674\n", "Epoch 174/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9738 - acc: 0.5478\n", "Epoch 00174: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9741 - acc: 0.5475 - val_loss: 1.0119 - val_acc: 0.4634\n", "Epoch 175/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9735 - acc: 0.5595\n", "Epoch 00175: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9734 - acc: 0.5599 - val_loss: 1.0110 - val_acc: 0.4648\n", "Epoch 176/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9672 - acc: 0.5697\n", "Epoch 00176: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9670 - acc: 0.5697 - val_loss: 1.0057 - val_acc: 0.4765\n", "Epoch 177/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9639 - acc: 0.5569\n", "Epoch 00177: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9640 - acc: 0.5569 - val_loss: 1.0070 - val_acc: 0.4778\n", "Epoch 178/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9669 - acc: 0.5681\n", "Epoch 00178: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9676 - acc: 0.5678 - val_loss: 1.0098 - val_acc: 0.4713\n", "Epoch 179/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9685 - acc: 0.5625\n", "Epoch 00179: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9679 - acc: 0.5629 - val_loss: 1.0087 - val_acc: 0.4726\n", "Epoch 180/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9722 - acc: 0.5538\n", "Epoch 00180: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9724 - acc: 0.5539 - val_loss: 1.0081 - val_acc: 0.4726\n", "Epoch 181/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9632 - acc: 0.5708\n", "Epoch 00181: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9633 - acc: 0.5705 - val_loss: 1.0073 - val_acc: 0.4765\n", "Epoch 182/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9667 - acc: 0.5629\n", "Epoch 00182: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9668 - acc: 0.5626 - val_loss: 1.0118 - val_acc: 0.4661\n", "Epoch 183/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9686 - acc: 0.5512\n", "Epoch 00183: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9684 - acc: 0.5517 - val_loss: 1.0094 - val_acc: 0.4648\n", "Epoch 184/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9684 - acc: 0.5561\n", "Epoch 00184: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9684 - acc: 0.5562 - val_loss: 1.0090 - val_acc: 0.4687\n", "Epoch 185/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9649 - acc: 0.5591\n", "Epoch 00185: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9652 - acc: 0.5584 - val_loss: 1.0080 - val_acc: 0.4726\n", "Epoch 186/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9656 - acc: 0.5599\n", "Epoch 00186: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9648 - acc: 0.5607 - val_loss: 1.0058 - val_acc: 0.4726\n", "Epoch 187/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9569 - acc: 0.5651\n", "Epoch 00187: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9567 - acc: 0.5656 - val_loss: 1.0070 - val_acc: 0.4752\n", "Epoch 188/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9610 - acc: 0.5666\n", "Epoch 00188: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9606 - acc: 0.5671 - val_loss: 1.0061 - val_acc: 0.4765\n", "Epoch 189/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9580 - acc: 0.5617\n", "Epoch 00189: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9578 - acc: 0.5614 - val_loss: 1.0039 - val_acc: 0.4752\n", "Epoch 190/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9613 - acc: 0.5685\n", "Epoch 00190: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9615 - acc: 0.5682 - val_loss: 1.0043 - val_acc: 0.4752\n", "Epoch 191/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9539 - acc: 0.5670\n", "Epoch 00191: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9539 - acc: 0.5667 - val_loss: 1.0042 - val_acc: 0.4739\n", "Epoch 192/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9625 - acc: 0.5757\n", "Epoch 00192: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9622 - acc: 0.5757 - val_loss: 1.0085 - val_acc: 0.4713\n", "Epoch 193/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9644 - acc: 0.5678\n", "Epoch 00193: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9640 - acc: 0.5682 - val_loss: 1.0071 - val_acc: 0.4687\n", "Epoch 194/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9546 - acc: 0.5712\n", "Epoch 00194: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9542 - acc: 0.5716 - val_loss: 1.0066 - val_acc: 0.4752\n", "Epoch 195/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9543 - acc: 0.5708\n", "Epoch 00195: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9540 - acc: 0.5712 - val_loss: 1.0036 - val_acc: 0.4752\n", "Epoch 196/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9617 - acc: 0.5610\n", "Epoch 00196: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9620 - acc: 0.5607 - val_loss: 1.0049 - val_acc: 0.4726\n", "Epoch 197/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9466 - acc: 0.5787\n", "Epoch 00197: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9463 - acc: 0.5791 - val_loss: 1.0061 - val_acc: 0.4726\n", "Epoch 198/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9555 - acc: 0.5685\n", "Epoch 00198: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9564 - acc: 0.5678 - val_loss: 1.0045 - val_acc: 0.4726\n", "Epoch 199/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9503 - acc: 0.5757\n", "Epoch 00199: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9503 - acc: 0.5757 - val_loss: 1.0029 - val_acc: 0.4739\n", "Epoch 200/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9528 - acc: 0.5742\n", "Epoch 00200: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9531 - acc: 0.5742 - val_loss: 1.0030 - val_acc: 0.4752\n", "Epoch 201/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9563 - acc: 0.5772\n", "Epoch 00201: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9562 - acc: 0.5769 - val_loss: 1.0041 - val_acc: 0.4739\n", "Epoch 202/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9547 - acc: 0.5787\n", "Epoch 00202: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9549 - acc: 0.5784 - val_loss: 1.0045 - val_acc: 0.4765\n", "Epoch 203/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9465 - acc: 0.5809\n", "Epoch 00203: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9474 - acc: 0.5799 - val_loss: 1.0026 - val_acc: 0.4739\n", "Epoch 204/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9555 - acc: 0.5633\n", "Epoch 00204: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9554 - acc: 0.5637 - val_loss: 1.0013 - val_acc: 0.4765\n", "Epoch 205/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9544 - acc: 0.5866\n", "Epoch 00205: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9546 - acc: 0.5866 - val_loss: 1.0008 - val_acc: 0.4778\n", "Epoch 206/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9548 - acc: 0.5693\n", "Epoch 00206: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9549 - acc: 0.5693 - val_loss: 1.0017 - val_acc: 0.4817\n", "Epoch 207/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9560 - acc: 0.5798\n", "Epoch 00207: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9565 - acc: 0.5795 - val_loss: 1.0011 - val_acc: 0.4739\n", "Epoch 208/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9511 - acc: 0.5640\n", "Epoch 00208: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9513 - acc: 0.5644 - val_loss: 1.0033 - val_acc: 0.4752\n", "Epoch 209/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9634 - acc: 0.5602\n", "Epoch 00209: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9632 - acc: 0.5607 - val_loss: 0.9994 - val_acc: 0.4726\n", "Epoch 210/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9523 - acc: 0.5681\n", "Epoch 00210: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9520 - acc: 0.5682 - val_loss: 1.0056 - val_acc: 0.4739\n", "Epoch 211/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9487 - acc: 0.5633\n", "Epoch 00211: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9489 - acc: 0.5629 - val_loss: 1.0030 - val_acc: 0.4778\n", "Epoch 212/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9468 - acc: 0.5817\n", "Epoch 00212: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9466 - acc: 0.5821 - val_loss: 1.0008 - val_acc: 0.4726\n", "Epoch 213/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9492 - acc: 0.5761\n", "Epoch 00213: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9491 - acc: 0.5761 - val_loss: 1.0002 - val_acc: 0.4765\n", "Epoch 214/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9433 - acc: 0.5697\n", "Epoch 00214: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9431 - acc: 0.5697 - val_loss: 1.0009 - val_acc: 0.4726\n", "Epoch 215/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9509 - acc: 0.5761\n", "Epoch 00215: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9509 - acc: 0.5761 - val_loss: 1.0010 - val_acc: 0.4713\n", "Epoch 216/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9446 - acc: 0.5757\n", "Epoch 00216: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9451 - acc: 0.5750 - val_loss: 0.9999 - val_acc: 0.4726\n", "Epoch 217/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9564 - acc: 0.5681\n", "Epoch 00217: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9566 - acc: 0.5678 - val_loss: 1.0010 - val_acc: 0.4713\n", "Epoch 218/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9459 - acc: 0.5809\n", "Epoch 00218: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9456 - acc: 0.5814 - val_loss: 1.0028 - val_acc: 0.4726\n", "Epoch 219/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9437 - acc: 0.5794\n", "Epoch 00219: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9434 - acc: 0.5799 - val_loss: 1.0014 - val_acc: 0.4726\n", "Epoch 220/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9461 - acc: 0.5855\n", "Epoch 00220: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9461 - acc: 0.5855 - val_loss: 1.0009 - val_acc: 0.4752\n", "Epoch 221/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9506 - acc: 0.5727\n", "Epoch 00221: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9502 - acc: 0.5735 - val_loss: 0.9993 - val_acc: 0.4713\n", "Epoch 222/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9385 - acc: 0.5877\n", "Epoch 00222: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9380 - acc: 0.5881 - val_loss: 0.9977 - val_acc: 0.4765\n", "Epoch 223/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9370 - acc: 0.5911\n", "Epoch 00223: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9369 - acc: 0.5911 - val_loss: 1.0009 - val_acc: 0.4765\n", "Epoch 224/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9486 - acc: 0.5700\n", "Epoch 00224: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9490 - acc: 0.5697 - val_loss: 1.0020 - val_acc: 0.4765\n", "Epoch 225/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9395 - acc: 0.5866\n", "Epoch 00225: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9392 - acc: 0.5866 - val_loss: 0.9986 - val_acc: 0.4856\n", "Epoch 226/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9470 - acc: 0.5836\n", "Epoch 00226: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9469 - acc: 0.5832 - val_loss: 0.9968 - val_acc: 0.4765\n", "Epoch 227/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9528 - acc: 0.5636\n", "Epoch 00227: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9530 - acc: 0.5637 - val_loss: 0.9981 - val_acc: 0.4778\n", "Epoch 228/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9403 - acc: 0.5806\n", "Epoch 00228: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9400 - acc: 0.5810 - val_loss: 0.9966 - val_acc: 0.4726\n", "Epoch 229/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9406 - acc: 0.5873\n", "Epoch 00229: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9407 - acc: 0.5870 - val_loss: 0.9989 - val_acc: 0.4726\n", "Epoch 230/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9468 - acc: 0.5772\n", "Epoch 00230: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9470 - acc: 0.5772 - val_loss: 0.9995 - val_acc: 0.4713\n", "Epoch 231/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9358 - acc: 0.5896\n", "Epoch 00231: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9363 - acc: 0.5889 - val_loss: 1.0016 - val_acc: 0.4752\n", "Epoch 232/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9395 - acc: 0.5730\n", "Epoch 00232: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9396 - acc: 0.5731 - val_loss: 1.0004 - val_acc: 0.4752\n", "Epoch 233/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9367 - acc: 0.5813\n", "Epoch 00233: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9367 - acc: 0.5817 - val_loss: 1.0018 - val_acc: 0.4739\n", "Epoch 234/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9387 - acc: 0.5855\n", "Epoch 00234: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9392 - acc: 0.5855 - val_loss: 1.0006 - val_acc: 0.4765\n", "Epoch 235/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9442 - acc: 0.5779\n", "Epoch 00235: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9443 - acc: 0.5780 - val_loss: 0.9969 - val_acc: 0.4752\n", "Epoch 236/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9354 - acc: 0.5885\n", "Epoch 00236: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9355 - acc: 0.5885 - val_loss: 0.9967 - val_acc: 0.4791\n", "Epoch 237/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9359 - acc: 0.5953\n", "Epoch 00237: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9357 - acc: 0.5956 - val_loss: 0.9956 - val_acc: 0.4778\n", "Epoch 238/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9362 - acc: 0.5817\n", "Epoch 00238: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9359 - acc: 0.5817 - val_loss: 0.9961 - val_acc: 0.4804\n", "Epoch 239/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9323 - acc: 0.5885\n", "Epoch 00239: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9324 - acc: 0.5885 - val_loss: 0.9928 - val_acc: 0.4765\n", "Epoch 240/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9496 - acc: 0.5745\n", "Epoch 00240: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9492 - acc: 0.5750 - val_loss: 0.9965 - val_acc: 0.4817\n", "Epoch 241/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9444 - acc: 0.5768\n", "Epoch 00241: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9445 - acc: 0.5765 - val_loss: 0.9943 - val_acc: 0.4791\n", "Epoch 242/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9350 - acc: 0.5945\n", "Epoch 00242: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9348 - acc: 0.5949 - val_loss: 0.9938 - val_acc: 0.4856\n", "Epoch 243/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9443 - acc: 0.5851\n", "Epoch 00243: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9442 - acc: 0.5851 - val_loss: 0.9962 - val_acc: 0.4817\n", "Epoch 244/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9369 - acc: 0.5889\n", "Epoch 00244: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9369 - acc: 0.5885 - val_loss: 0.9936 - val_acc: 0.4778\n", "Epoch 245/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9365 - acc: 0.5881\n", "Epoch 00245: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9364 - acc: 0.5881 - val_loss: 0.9947 - val_acc: 0.4817\n", "Epoch 246/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9281 - acc: 0.6002\n", "Epoch 00246: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9281 - acc: 0.5998 - val_loss: 0.9941 - val_acc: 0.4804\n", "Epoch 247/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9375 - acc: 0.5794\n", "Epoch 00247: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9376 - acc: 0.5795 - val_loss: 0.9942 - val_acc: 0.4804\n", "Epoch 248/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9346 - acc: 0.5945\n", "Epoch 00248: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9354 - acc: 0.5938 - val_loss: 0.9966 - val_acc: 0.4752\n", "Epoch 249/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9361 - acc: 0.5975\n", "Epoch 00249: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9360 - acc: 0.5975 - val_loss: 0.9971 - val_acc: 0.4739\n", "Epoch 250/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9350 - acc: 0.5847\n", "Epoch 00250: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9353 - acc: 0.5844 - val_loss: 0.9948 - val_acc: 0.4830\n", "Epoch 251/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9364 - acc: 0.5919\n", "Epoch 00251: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9368 - acc: 0.5919 - val_loss: 0.9972 - val_acc: 0.4817\n", "Epoch 252/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9335 - acc: 0.5892\n", "Epoch 00252: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9337 - acc: 0.5889 - val_loss: 0.9970 - val_acc: 0.4830\n", "Epoch 253/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9279 - acc: 0.6013\n", "Epoch 00253: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9276 - acc: 0.6013 - val_loss: 0.9945 - val_acc: 0.4817\n", "Epoch 254/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9366 - acc: 0.5922\n", "Epoch 00254: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9367 - acc: 0.5923 - val_loss: 0.9968 - val_acc: 0.4843\n", "Epoch 255/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9321 - acc: 0.5843\n", "Epoch 00255: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9322 - acc: 0.5844 - val_loss: 0.9964 - val_acc: 0.4765\n", "Epoch 256/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9301 - acc: 0.5960\n", "Epoch 00256: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9296 - acc: 0.5964 - val_loss: 0.9946 - val_acc: 0.4804\n", "Epoch 257/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9336 - acc: 0.5873\n", "Epoch 00257: val_acc did not improve from 0.48564\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9336 - acc: 0.5877 - val_loss: 0.9950 - val_acc: 0.4752\n", "Epoch 258/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9289 - acc: 0.5964\n", "Epoch 00258: 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9291 - acc: 0.5964 - val_loss: 0.9936 - val_acc: 0.4869\n", "Epoch 259/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9270 - acc: 0.5949\n", "Epoch 00259: val_acc did not improve from 0.48695\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9268 - acc: 0.5949 - val_loss: 0.9917 - val_acc: 0.4830\n", "Epoch 260/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9210 - acc: 0.6073\n", "Epoch 00260: 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9218 - acc: 0.6069 - val_loss: 0.9969 - val_acc: 0.4883\n", "Epoch 261/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9255 - acc: 0.5866\n", "Epoch 00261: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9256 - acc: 0.5862 - val_loss: 0.9944 - val_acc: 0.4830\n", "Epoch 262/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9260 - acc: 0.5968\n", "Epoch 00262: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9257 - acc: 0.5968 - val_loss: 0.9933 - val_acc: 0.4856\n", "Epoch 263/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9247 - acc: 0.5828\n", "Epoch 00263: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9250 - acc: 0.5825 - val_loss: 0.9924 - val_acc: 0.4765\n", "Epoch 264/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9277 - acc: 0.6020\n", "Epoch 00264: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9278 - acc: 0.6017 - val_loss: 0.9916 - val_acc: 0.4791\n", "Epoch 265/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9200 - acc: 0.6107\n", "Epoch 00265: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9204 - acc: 0.6099 - val_loss: 0.9941 - val_acc: 0.4856\n", "Epoch 266/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9339 - acc: 0.5911\n", "Epoch 00266: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9340 - acc: 0.5904 - val_loss: 0.9906 - val_acc: 0.4830\n", "Epoch 267/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9220 - acc: 0.6002\n", "Epoch 00267: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9216 - acc: 0.6009 - val_loss: 0.9896 - val_acc: 0.4830\n", "Epoch 268/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9278 - acc: 0.6005\n", "Epoch 00268: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9274 - acc: 0.6013 - val_loss: 0.9921 - val_acc: 0.4883\n", "Epoch 269/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9214 - acc: 0.6043\n", "Epoch 00269: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9211 - acc: 0.6047 - val_loss: 0.9921 - val_acc: 0.4843\n", "Epoch 270/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9254 - acc: 0.6050\n", "Epoch 00270: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9252 - acc: 0.6050 - val_loss: 0.9904 - val_acc: 0.4817\n", "Epoch 271/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9186 - acc: 0.6073\n", "Epoch 00271: val_acc did not improve from 0.48825\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9194 - acc: 0.6069 - val_loss: 0.9922 - val_acc: 0.4869\n", "Epoch 272/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9196 - acc: 0.5990\n", "Epoch 00272: val_acc improved from 0.48825 to 0.48956, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9194 - acc: 0.5994 - val_loss: 0.9921 - val_acc: 0.4896\n", "Epoch 273/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9213 - acc: 0.5956\n", "Epoch 00273: val_acc did not improve from 0.48956\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9211 - acc: 0.5960 - val_loss: 0.9946 - val_acc: 0.4830\n", "Epoch 274/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9180 - acc: 0.6025\n", "Epoch 00274: val_acc did not improve from 0.48956\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9187 - acc: 0.6017 - val_loss: 0.9920 - val_acc: 0.4752\n", "Epoch 275/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9191 - acc: 0.6017\n", "Epoch 00275: val_acc did not improve from 0.48956\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9190 - acc: 0.6017 - val_loss: 0.9949 - val_acc: 0.4856\n", "Epoch 276/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9154 - acc: 0.6050\n", "Epoch 00276: val_acc improved from 0.48956 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9149 - acc: 0.6054 - val_loss: 0.9922 - val_acc: 0.4922\n", "Epoch 277/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9255 - acc: 0.5968\n", "Epoch 00277: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9246 - acc: 0.5975 - val_loss: 0.9945 - val_acc: 0.4817\n", "Epoch 278/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9323 - acc: 0.5960\n", "Epoch 00278: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9322 - acc: 0.5964 - val_loss: 0.9940 - val_acc: 0.4856\n", "Epoch 279/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9183 - acc: 0.5983\n", "Epoch 00279: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9181 - acc: 0.5986 - val_loss: 0.9930 - val_acc: 0.4843\n", "Epoch 280/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9171 - acc: 0.6018\n", "Epoch 00280: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9172 - acc: 0.6013 - val_loss: 0.9944 - val_acc: 0.4856\n", "Epoch 281/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9139 - acc: 0.6062\n", "Epoch 00281: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9137 - acc: 0.6065 - val_loss: 0.9905 - val_acc: 0.4869\n", "Epoch 282/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9175 - acc: 0.6133\n", "Epoch 00282: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9175 - acc: 0.6133 - val_loss: 0.9905 - val_acc: 0.4830\n", "Epoch 283/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9171 - acc: 0.6163\n", "Epoch 00283: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9173 - acc: 0.6163 - val_loss: 0.9914 - val_acc: 0.4869\n", "Epoch 284/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9195 - acc: 0.5990\n", "Epoch 00284: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9191 - acc: 0.5994 - val_loss: 0.9922 - val_acc: 0.4856\n", "Epoch 285/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9189 - acc: 0.6054\n", "Epoch 00285: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9189 - acc: 0.6058 - val_loss: 0.9922 - val_acc: 0.4896\n", "Epoch 286/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9193 - acc: 0.6024\n", "Epoch 00286: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9190 - acc: 0.6028 - val_loss: 0.9916 - val_acc: 0.4869\n", "Epoch 287/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9170 - acc: 0.6081\n", "Epoch 00287: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9173 - acc: 0.6077 - val_loss: 0.9891 - val_acc: 0.4856\n", "Epoch 288/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9117 - acc: 0.6122\n", "Epoch 00288: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9115 - acc: 0.6122 - val_loss: 0.9903 - val_acc: 0.4843\n", "Epoch 289/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9176 - acc: 0.6103\n", "Epoch 00289: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9170 - acc: 0.6107 - val_loss: 0.9924 - val_acc: 0.4856\n", "Epoch 290/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9076 - acc: 0.6145\n", "Epoch 00290: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9078 - acc: 0.6148 - val_loss: 0.9900 - val_acc: 0.4856\n", "Epoch 291/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9110 - acc: 0.6062\n", "Epoch 00291: val_acc did not improve from 0.49217\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9115 - acc: 0.6058 - val_loss: 0.9910 - val_acc: 0.4922\n", "Epoch 292/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9167 - acc: 0.6035\n", "Epoch 00292: 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9170 - acc: 0.6032 - val_loss: 0.9930 - val_acc: 0.4935\n", "Epoch 293/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9153 - acc: 0.6013\n", "Epoch 00293: val_acc did not improve from 0.49347\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9150 - acc: 0.6013 - val_loss: 0.9948 - val_acc: 0.4843\n", "Epoch 294/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9134 - acc: 0.6081\n", "Epoch 00294: val_acc improved from 0.49347 to 0.49739, 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9135 - acc: 0.6077 - val_loss: 0.9890 - val_acc: 0.4974\n", "Epoch 295/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9119 - acc: 0.6062\n", "Epoch 00295: val_acc did not improve from 0.49739\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9122 - acc: 0.6058 - val_loss: 0.9893 - val_acc: 0.4896\n", "Epoch 296/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9057 - acc: 0.6066\n", "Epoch 00296: val_acc improved from 0.49739 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.9057 - acc: 0.6065 - val_loss: 0.9896 - val_acc: 0.5000\n", "Epoch 297/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9078 - acc: 0.5986\n", "Epoch 00297: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9079 - acc: 0.5986 - val_loss: 0.9922 - val_acc: 0.4869\n", "Epoch 298/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9115 - acc: 0.6099\n", "Epoch 00298: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9111 - acc: 0.6103 - val_loss: 0.9917 - val_acc: 0.4856\n", "Epoch 299/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9045 - acc: 0.6101\n", "Epoch 00299: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9050 - acc: 0.6088 - val_loss: 0.9892 - val_acc: 0.4869\n", "Epoch 300/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9050 - acc: 0.6171\n", "Epoch 00300: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9048 - acc: 0.6171 - val_loss: 0.9897 - val_acc: 0.4804\n", "Epoch 301/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9068 - acc: 0.6175\n", "Epoch 00301: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9068 - acc: 0.6174 - val_loss: 0.9895 - val_acc: 0.4935\n", "Epoch 302/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9061 - acc: 0.6107\n", "Epoch 00302: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9071 - acc: 0.6099 - val_loss: 0.9900 - val_acc: 0.4856\n", "Epoch 303/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9024 - acc: 0.6163\n", "Epoch 00303: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9022 - acc: 0.6167 - val_loss: 0.9902 - val_acc: 0.4869\n", "Epoch 304/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9037 - acc: 0.6160\n", "Epoch 00304: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9039 - acc: 0.6156 - val_loss: 0.9913 - val_acc: 0.4843\n", "Epoch 305/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8981 - acc: 0.6152\n", "Epoch 00305: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8986 - acc: 0.6148 - val_loss: 0.9927 - val_acc: 0.4830\n", "Epoch 306/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9063 - acc: 0.6160\n", "Epoch 00306: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9065 - acc: 0.6156 - val_loss: 0.9880 - val_acc: 0.4922\n", "Epoch 307/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9060 - acc: 0.6175\n", "Epoch 00307: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9060 - acc: 0.6174 - val_loss: 0.9873 - val_acc: 0.4922\n", "Epoch 308/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9048 - acc: 0.6156\n", "Epoch 00308: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9045 - acc: 0.6159 - val_loss: 0.9888 - val_acc: 0.4961\n", "Epoch 309/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9030 - acc: 0.6126\n", "Epoch 00309: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9027 - acc: 0.6133 - val_loss: 0.9912 - val_acc: 0.4856\n", "Epoch 310/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9006 - acc: 0.6111\n", "Epoch 00310: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9006 - acc: 0.6110 - val_loss: 0.9885 - val_acc: 0.4909\n", "Epoch 311/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9031 - acc: 0.6167\n", "Epoch 00311: val_acc did not improve from 0.50000\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9029 - acc: 0.6171 - val_loss: 0.9889 - val_acc: 0.4896\n", "Epoch 312/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8992 - acc: 0.6224\n", "Epoch 00312: 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.8994 - acc: 0.6219 - val_loss: 0.9869 - val_acc: 0.5013\n", "Epoch 313/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9002 - acc: 0.6163\n", "Epoch 00313: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9004 - acc: 0.6159 - val_loss: 0.9875 - val_acc: 0.4843\n", "Epoch 314/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8942 - acc: 0.6171\n", "Epoch 00314: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8946 - acc: 0.6171 - val_loss: 0.9871 - val_acc: 0.4856\n", "Epoch 315/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9004 - acc: 0.6194\n", "Epoch 00315: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9002 - acc: 0.6193 - val_loss: 0.9874 - val_acc: 0.4896\n", "Epoch 316/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.9034 - acc: 0.6204\n", "Epoch 00316: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9053 - acc: 0.6201 - val_loss: 0.9871 - val_acc: 0.4922\n", "Epoch 317/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9042 - acc: 0.6137\n", "Epoch 00317: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9037 - acc: 0.6141 - val_loss: 0.9890 - val_acc: 0.4948\n", "Epoch 318/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.9099 - acc: 0.6141\n", "Epoch 00318: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.9098 - acc: 0.6141 - val_loss: 0.9873 - val_acc: 0.4883\n", "Epoch 319/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8929 - acc: 0.6258\n", "Epoch 00319: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8933 - acc: 0.6253 - val_loss: 0.9889 - val_acc: 0.4883\n", "Epoch 320/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8954 - acc: 0.6227\n", "Epoch 00320: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8947 - acc: 0.6231 - val_loss: 0.9880 - val_acc: 0.4896\n", "Epoch 321/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8888 - acc: 0.6197\n", "Epoch 00321: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8888 - acc: 0.6193 - val_loss: 0.9862 - val_acc: 0.4883\n", "Epoch 322/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8998 - acc: 0.6126\n", "Epoch 00322: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8997 - acc: 0.6126 - val_loss: 0.9892 - val_acc: 0.4856\n", "Epoch 323/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8980 - acc: 0.6250\n", "Epoch 00323: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8979 - acc: 0.6246 - val_loss: 0.9894 - val_acc: 0.4869\n", "Epoch 324/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8903 - acc: 0.6273\n", "Epoch 00324: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8906 - acc: 0.6276 - val_loss: 0.9867 - val_acc: 0.4883\n", "Epoch 325/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8907 - acc: 0.6273\n", "Epoch 00325: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8908 - acc: 0.6268 - val_loss: 0.9901 - val_acc: 0.4817\n", "Epoch 326/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8979 - acc: 0.6171\n", "Epoch 00326: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8979 - acc: 0.6171 - val_loss: 0.9872 - val_acc: 0.4974\n", "Epoch 327/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8881 - acc: 0.6261\n", "Epoch 00327: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8885 - acc: 0.6257 - val_loss: 0.9865 - val_acc: 0.4974\n", "Epoch 328/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8914 - acc: 0.6250\n", "Epoch 00328: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8912 - acc: 0.6250 - val_loss: 0.9859 - val_acc: 0.4974\n", "Epoch 329/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8930 - acc: 0.6156\n", "Epoch 00329: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8928 - acc: 0.6156 - val_loss: 0.9865 - val_acc: 0.4948\n", "Epoch 330/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8882 - acc: 0.6197\n", "Epoch 00330: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8893 - acc: 0.6189 - val_loss: 0.9872 - val_acc: 0.4961\n", "Epoch 331/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8858 - acc: 0.6227\n", "Epoch 00331: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8862 - acc: 0.6227 - val_loss: 0.9864 - val_acc: 0.4935\n", "Epoch 332/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8884 - acc: 0.6242\n", "Epoch 00332: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8883 - acc: 0.6242 - val_loss: 0.9892 - val_acc: 0.4856\n", "Epoch 333/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8924 - acc: 0.6209\n", "Epoch 00333: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8927 - acc: 0.6208 - val_loss: 0.9884 - val_acc: 0.4909\n", "Epoch 334/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8882 - acc: 0.6303\n", "Epoch 00334: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8883 - acc: 0.6298 - val_loss: 0.9879 - val_acc: 0.4922\n", "Epoch 335/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8962 - acc: 0.6242\n", "Epoch 00335: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8959 - acc: 0.6242 - val_loss: 0.9890 - val_acc: 0.4948\n", "Epoch 336/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8943 - acc: 0.6190\n", "Epoch 00336: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8943 - acc: 0.6189 - val_loss: 0.9861 - val_acc: 0.4935\n", "Epoch 337/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8932 - acc: 0.6216\n", "Epoch 00337: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8931 - acc: 0.6216 - val_loss: 0.9882 - val_acc: 0.4974\n", "Epoch 338/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8812 - acc: 0.6280\n", "Epoch 00338: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8819 - acc: 0.6272 - val_loss: 0.9870 - val_acc: 0.4922\n", "Epoch 339/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8849 - acc: 0.6284\n", "Epoch 00339: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8851 - acc: 0.6283 - val_loss: 0.9887 - val_acc: 0.4935\n", "Epoch 340/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8849 - acc: 0.6273\n", "Epoch 00340: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8846 - acc: 0.6272 - val_loss: 0.9870 - val_acc: 0.5000\n", "Epoch 341/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8861 - acc: 0.6276\n", "Epoch 00341: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8861 - acc: 0.6272 - val_loss: 0.9879 - val_acc: 0.4948\n", "Epoch 342/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8795 - acc: 0.6408\n", "Epoch 00342: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8792 - acc: 0.6411 - val_loss: 0.9884 - val_acc: 0.4948\n", "Epoch 343/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8914 - acc: 0.6220\n", "Epoch 00343: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8909 - acc: 0.6223 - val_loss: 0.9929 - val_acc: 0.4791\n", "Epoch 344/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8821 - acc: 0.6284\n", "Epoch 00344: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8818 - acc: 0.6283 - val_loss: 0.9884 - val_acc: 0.4948\n", "Epoch 345/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8859 - acc: 0.6291\n", "Epoch 00345: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8862 - acc: 0.6287 - val_loss: 0.9900 - val_acc: 0.5000\n", "Epoch 346/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8694 - acc: 0.6374\n", "Epoch 00346: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8698 - acc: 0.6370 - val_loss: 0.9885 - val_acc: 0.4935\n", "Epoch 347/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8819 - acc: 0.6344\n", "Epoch 00347: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8817 - acc: 0.6347 - val_loss: 0.9905 - val_acc: 0.4830\n", "Epoch 348/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8907 - acc: 0.6212\n", "Epoch 00348: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8901 - acc: 0.6219 - val_loss: 0.9881 - val_acc: 0.4935\n", "Epoch 349/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8802 - acc: 0.6340\n", "Epoch 00349: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8798 - acc: 0.6347 - val_loss: 0.9921 - val_acc: 0.4817\n", "Epoch 350/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8817 - acc: 0.6344\n", "Epoch 00350: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8817 - acc: 0.6347 - val_loss: 0.9912 - val_acc: 0.4830\n", "Epoch 351/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8792 - acc: 0.6306\n", "Epoch 00351: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8797 - acc: 0.6306 - val_loss: 0.9861 - val_acc: 0.4948\n", "Epoch 352/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8802 - acc: 0.6254\n", "Epoch 00352: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8809 - acc: 0.6246 - val_loss: 0.9865 - val_acc: 0.5013\n", "Epoch 353/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8798 - acc: 0.6374\n", "Epoch 00353: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8810 - acc: 0.6366 - val_loss: 0.9903 - val_acc: 0.4843\n", "Epoch 354/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8798 - acc: 0.6363\n", "Epoch 00354: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8799 - acc: 0.6362 - val_loss: 0.9876 - val_acc: 0.4909\n", "Epoch 355/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8820 - acc: 0.6273\n", "Epoch 00355: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8813 - acc: 0.6280 - val_loss: 0.9887 - val_acc: 0.4935\n", "Epoch 356/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8786 - acc: 0.6359\n", "Epoch 00356: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8787 - acc: 0.6359 - val_loss: 0.9862 - val_acc: 0.4935\n", "Epoch 357/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8742 - acc: 0.6386\n", "Epoch 00357: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8739 - acc: 0.6392 - val_loss: 0.9943 - val_acc: 0.4896\n", "Epoch 358/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8757 - acc: 0.6412\n", "Epoch 00358: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8755 - acc: 0.6411 - val_loss: 0.9903 - val_acc: 0.4909\n", "Epoch 359/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8719 - acc: 0.6389\n", "Epoch 00359: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8718 - acc: 0.6389 - val_loss: 0.9887 - val_acc: 0.4922\n", "Epoch 360/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.8838 - acc: 0.6338\n", "Epoch 00360: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8835 - acc: 0.6332 - val_loss: 0.9858 - val_acc: 0.4935\n", "Epoch 361/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8754 - acc: 0.6393\n", "Epoch 00361: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8753 - acc: 0.6392 - val_loss: 0.9880 - val_acc: 0.4935\n", "Epoch 362/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8746 - acc: 0.6389\n", "Epoch 00362: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8745 - acc: 0.6392 - val_loss: 0.9864 - val_acc: 0.4909\n", "Epoch 363/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8712 - acc: 0.6457\n", "Epoch 00363: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8715 - acc: 0.6449 - val_loss: 0.9846 - val_acc: 0.4948\n", "Epoch 364/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8688 - acc: 0.6457\n", "Epoch 00364: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8689 - acc: 0.6456 - val_loss: 0.9864 - val_acc: 0.4883\n", "Epoch 365/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8656 - acc: 0.6502\n", "Epoch 00365: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8657 - acc: 0.6501 - val_loss: 0.9878 - val_acc: 0.4961\n", "Epoch 366/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8641 - acc: 0.6472\n", "Epoch 00366: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8638 - acc: 0.6475 - val_loss: 0.9872 - val_acc: 0.4935\n", "Epoch 367/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8668 - acc: 0.6431\n", "Epoch 00367: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8669 - acc: 0.6430 - val_loss: 0.9856 - val_acc: 0.4896\n", "Epoch 368/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8712 - acc: 0.6450\n", "Epoch 00368: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8716 - acc: 0.6449 - val_loss: 0.9865 - val_acc: 0.4869\n", "Epoch 369/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8674 - acc: 0.6562\n", "Epoch 00369: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8672 - acc: 0.6561 - val_loss: 0.9874 - val_acc: 0.4948\n", "Epoch 370/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8673 - acc: 0.6352\n", "Epoch 00370: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8669 - acc: 0.6359 - val_loss: 0.9885 - val_acc: 0.4909\n", "Epoch 371/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8651 - acc: 0.6521\n", "Epoch 00371: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8649 - acc: 0.6520 - val_loss: 0.9859 - val_acc: 0.4922\n", "Epoch 372/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8659 - acc: 0.6370\n", "Epoch 00372: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8664 - acc: 0.6366 - val_loss: 0.9892 - val_acc: 0.4974\n", "Epoch 373/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8627 - acc: 0.6355\n", "Epoch 00373: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8631 - acc: 0.6355 - val_loss: 0.9908 - val_acc: 0.4935\n", "Epoch 374/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8565 - acc: 0.6423\n", "Epoch 00374: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8560 - acc: 0.6430 - val_loss: 0.9877 - val_acc: 0.4974\n", "Epoch 375/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8657 - acc: 0.6476\n", "Epoch 00375: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8660 - acc: 0.6475 - val_loss: 0.9867 - val_acc: 0.4987\n", "Epoch 376/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8663 - acc: 0.6514\n", "Epoch 00376: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8667 - acc: 0.6509 - val_loss: 0.9862 - val_acc: 0.4961\n", "Epoch 377/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8642 - acc: 0.6461\n", "Epoch 00377: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8637 - acc: 0.6467 - val_loss: 0.9861 - val_acc: 0.4974\n", "Epoch 378/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8679 - acc: 0.6325\n", "Epoch 00378: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8684 - acc: 0.6321 - val_loss: 0.9881 - val_acc: 0.4935\n", "Epoch 379/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8689 - acc: 0.6419\n", "Epoch 00379: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8693 - acc: 0.6419 - val_loss: 0.9884 - val_acc: 0.4909\n", "Epoch 380/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8628 - acc: 0.6578\n", "Epoch 00380: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8628 - acc: 0.6576 - val_loss: 0.9899 - val_acc: 0.4922\n", "Epoch 381/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8572 - acc: 0.6416\n", "Epoch 00381: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8576 - acc: 0.6411 - val_loss: 0.9916 - val_acc: 0.4922\n", "Epoch 382/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8539 - acc: 0.6529\n", "Epoch 00382: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8537 - acc: 0.6528 - val_loss: 0.9874 - val_acc: 0.4935\n", "Epoch 383/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8651 - acc: 0.6423\n", "Epoch 00383: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8650 - acc: 0.6422 - val_loss: 0.9877 - val_acc: 0.4922\n", "Epoch 384/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8591 - acc: 0.6551\n", "Epoch 00384: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8601 - acc: 0.6546 - val_loss: 0.9907 - val_acc: 0.4922\n", "Epoch 385/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8533 - acc: 0.6491\n", "Epoch 00385: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8533 - acc: 0.6490 - val_loss: 0.9871 - val_acc: 0.4922\n", "Epoch 386/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8636 - acc: 0.6457\n", "Epoch 00386: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8633 - acc: 0.6464 - val_loss: 0.9891 - val_acc: 0.4961\n", "Epoch 387/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8636 - acc: 0.6476\n", "Epoch 00387: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8628 - acc: 0.6483 - val_loss: 0.9878 - val_acc: 0.4948\n", "Epoch 388/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8660 - acc: 0.6495\n", "Epoch 00388: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8663 - acc: 0.6494 - val_loss: 0.9883 - val_acc: 0.4935\n", "Epoch 389/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8617 - acc: 0.6547\n", "Epoch 00389: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8617 - acc: 0.6546 - val_loss: 0.9884 - val_acc: 0.4987\n", "Epoch 390/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8520 - acc: 0.6596\n", "Epoch 00390: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8524 - acc: 0.6595 - val_loss: 0.9902 - val_acc: 0.4935\n", "Epoch 391/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8461 - acc: 0.6736\n", "Epoch 00391: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8456 - acc: 0.6742 - val_loss: 0.9878 - val_acc: 0.4961\n", "Epoch 392/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8536 - acc: 0.6498\n", "Epoch 00392: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8533 - acc: 0.6501 - val_loss: 0.9874 - val_acc: 0.4987\n", "Epoch 393/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8541 - acc: 0.6510\n", "Epoch 00393: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8536 - acc: 0.6516 - val_loss: 0.9910 - val_acc: 0.4948\n", "Epoch 394/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8490 - acc: 0.6547\n", "Epoch 00394: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8491 - acc: 0.6546 - val_loss: 0.9921 - val_acc: 0.4869\n", "Epoch 395/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8533 - acc: 0.6495\n", "Epoch 00395: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8529 - acc: 0.6501 - val_loss: 0.9933 - val_acc: 0.4896\n", "Epoch 396/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8548 - acc: 0.6547\n", "Epoch 00396: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8546 - acc: 0.6546 - val_loss: 0.9891 - val_acc: 0.4961\n", "Epoch 397/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8491 - acc: 0.6555\n", "Epoch 00397: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8491 - acc: 0.6554 - val_loss: 0.9908 - val_acc: 0.4922\n", "Epoch 398/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8567 - acc: 0.6589\n", "Epoch 00398: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8565 - acc: 0.6592 - val_loss: 0.9907 - val_acc: 0.5013\n", "Epoch 399/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8500 - acc: 0.6566\n", "Epoch 00399: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8494 - acc: 0.6569 - val_loss: 0.9934 - val_acc: 0.4961\n", "Epoch 400/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8524 - acc: 0.6562\n", "Epoch 00400: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8519 - acc: 0.6561 - val_loss: 0.9902 - val_acc: 0.4922\n", "Epoch 401/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8474 - acc: 0.6611\n", "Epoch 00401: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8469 - acc: 0.6618 - val_loss: 0.9880 - val_acc: 0.4948\n", "Epoch 402/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8414 - acc: 0.6668\n", "Epoch 00402: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8410 - acc: 0.6670 - val_loss: 0.9910 - val_acc: 0.4883\n", "Epoch 403/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8502 - acc: 0.6645\n", "Epoch 00403: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8498 - acc: 0.6648 - val_loss: 0.9888 - val_acc: 0.4948\n", "Epoch 404/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8411 - acc: 0.6461\n", "Epoch 00404: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8411 - acc: 0.6460 - val_loss: 0.9899 - val_acc: 0.4974\n", "Epoch 405/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8498 - acc: 0.6540\n", "Epoch 00405: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8503 - acc: 0.6539 - val_loss: 0.9916 - val_acc: 0.4922\n", "Epoch 406/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8406 - acc: 0.6611\n", "Epoch 00406: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8403 - acc: 0.6618 - val_loss: 0.9912 - val_acc: 0.4948\n", "Epoch 407/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8401 - acc: 0.6551\n", "Epoch 00407: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8400 - acc: 0.6554 - val_loss: 0.9934 - val_acc: 0.4961\n", "Epoch 408/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8485 - acc: 0.6536\n", "Epoch 00408: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8483 - acc: 0.6543 - val_loss: 0.9924 - val_acc: 0.4948\n", "Epoch 409/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8415 - acc: 0.6559\n", "Epoch 00409: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8414 - acc: 0.6561 - val_loss: 0.9960 - val_acc: 0.4935\n", "Epoch 410/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8493 - acc: 0.6559\n", "Epoch 00410: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8492 - acc: 0.6558 - val_loss: 0.9917 - val_acc: 0.4948\n", "Epoch 411/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8434 - acc: 0.6660\n", "Epoch 00411: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8439 - acc: 0.6655 - val_loss: 0.9906 - val_acc: 0.4909\n", "Epoch 412/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8462 - acc: 0.6589\n", "Epoch 00412: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8460 - acc: 0.6588 - val_loss: 0.9895 - val_acc: 0.5013\n", "Epoch 413/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8461 - acc: 0.6649\n", "Epoch 00413: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8458 - acc: 0.6652 - val_loss: 0.9892 - val_acc: 0.4948\n", "Epoch 414/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8397 - acc: 0.6589\n", "Epoch 00414: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8390 - acc: 0.6592 - val_loss: 0.9973 - val_acc: 0.5013\n", "Epoch 415/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8351 - acc: 0.6570\n", "Epoch 00415: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8351 - acc: 0.6569 - val_loss: 0.9924 - val_acc: 0.4987\n", "Epoch 416/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8436 - acc: 0.6487\n", "Epoch 00416: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8437 - acc: 0.6490 - val_loss: 0.9910 - val_acc: 0.4948\n", "Epoch 417/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8397 - acc: 0.6578\n", "Epoch 00417: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8393 - acc: 0.6580 - val_loss: 0.9922 - val_acc: 0.4922\n", "Epoch 418/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8395 - acc: 0.6536\n", "Epoch 00418: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8389 - acc: 0.6543 - val_loss: 0.9906 - val_acc: 0.4974\n", "Epoch 419/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8434 - acc: 0.6645\n", "Epoch 00419: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8428 - acc: 0.6648 - val_loss: 0.9912 - val_acc: 0.4948\n", "Epoch 420/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8372 - acc: 0.6694\n", "Epoch 00420: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8368 - acc: 0.6697 - val_loss: 0.9914 - val_acc: 0.4961\n", "Epoch 421/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8346 - acc: 0.6664\n", "Epoch 00421: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8341 - acc: 0.6670 - val_loss: 0.9931 - val_acc: 0.4922\n", "Epoch 422/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8406 - acc: 0.6627\n", "Epoch 00422: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8408 - acc: 0.6622 - val_loss: 0.9928 - val_acc: 0.5000\n", "Epoch 423/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8246 - acc: 0.6721\n", "Epoch 00423: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8259 - acc: 0.6719 - val_loss: 0.9908 - val_acc: 0.4974\n", "Epoch 424/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8331 - acc: 0.6627\n", "Epoch 00424: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8331 - acc: 0.6625 - val_loss: 0.9953 - val_acc: 0.5013\n", "Epoch 425/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8376 - acc: 0.6706\n", "Epoch 00425: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8378 - acc: 0.6704 - val_loss: 0.9923 - val_acc: 0.4948\n", "Epoch 426/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8294 - acc: 0.6743\n", "Epoch 00426: val_acc did not improve from 0.50131\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8294 - acc: 0.6742 - val_loss: 0.9992 - val_acc: 0.4974\n", "Epoch 427/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8348 - acc: 0.6649\n", "Epoch 00427: val_acc improved from 0.50131 to 0.50392, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.8348 - acc: 0.6652 - val_loss: 0.9879 - val_acc: 0.5039\n", "Epoch 428/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8262 - acc: 0.6762\n", "Epoch 00428: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8265 - acc: 0.6761 - val_loss: 0.9903 - val_acc: 0.4974\n", "Epoch 429/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8234 - acc: 0.6691\n", "Epoch 00429: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8232 - acc: 0.6697 - val_loss: 0.9932 - val_acc: 0.4961\n", "Epoch 430/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8280 - acc: 0.6724\n", "Epoch 00430: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8273 - acc: 0.6731 - val_loss: 0.9917 - val_acc: 0.4974\n", "Epoch 431/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.8314 - acc: 0.6711\n", "Epoch 00431: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8324 - acc: 0.6708 - val_loss: 0.9998 - val_acc: 0.4948\n", "Epoch 432/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8299 - acc: 0.6687\n", "Epoch 00432: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8299 - acc: 0.6685 - val_loss: 0.9934 - val_acc: 0.5026\n", "Epoch 433/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8315 - acc: 0.6627\n", "Epoch 00433: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8313 - acc: 0.6625 - val_loss: 0.9929 - val_acc: 0.4974\n", "Epoch 434/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8188 - acc: 0.6819\n", "Epoch 00434: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8186 - acc: 0.6821 - val_loss: 0.9919 - val_acc: 0.5000\n", "Epoch 435/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8257 - acc: 0.6691\n", "Epoch 00435: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8255 - acc: 0.6693 - val_loss: 0.9926 - val_acc: 0.4974\n", "Epoch 436/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8305 - acc: 0.6706\n", "Epoch 00436: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8301 - acc: 0.6708 - val_loss: 0.9926 - val_acc: 0.4961\n", "Epoch 437/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8181 - acc: 0.6751\n", "Epoch 00437: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8188 - acc: 0.6746 - val_loss: 0.9901 - val_acc: 0.5039\n", "Epoch 438/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8273 - acc: 0.6739\n", "Epoch 00438: val_acc did not improve from 0.50392\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8280 - acc: 0.6731 - val_loss: 0.9943 - val_acc: 0.5039\n", "Epoch 439/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8241 - acc: 0.6792\n", "Epoch 00439: val_acc improved from 0.50392 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.8242 - acc: 0.6791 - val_loss: 0.9931 - val_acc: 0.5052\n", "Epoch 440/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8279 - acc: 0.6679\n", "Epoch 00440: val_acc did not improve from 0.50522\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8279 - acc: 0.6682 - val_loss: 0.9905 - val_acc: 0.5000\n", "Epoch 441/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8253 - acc: 0.6743\n", "Epoch 00441: 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.h5\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8255 - acc: 0.6742 - val_loss: 0.9906 - val_acc: 0.5065\n", "Epoch 442/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8242 - acc: 0.6649\n", "Epoch 00442: val_acc did not improve from 0.50653\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8241 - acc: 0.6652 - val_loss: 0.9953 - val_acc: 0.5039\n", "Epoch 443/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8231 - acc: 0.6679\n", "Epoch 00443: val_acc did not improve from 0.50653\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8229 - acc: 0.6682 - val_loss: 0.9928 - val_acc: 0.5000\n", "Epoch 444/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8139 - acc: 0.6856\n", "Epoch 00444: val_acc did not improve from 0.50653\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8139 - acc: 0.6858 - val_loss: 0.9912 - val_acc: 0.5065\n", "Epoch 445/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8226 - acc: 0.6788\n", "Epoch 00445: val_acc did not improve from 0.50653\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8229 - acc: 0.6787 - val_loss: 1.0006 - val_acc: 0.5013\n", "Epoch 446/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8130 - acc: 0.6728\n", "Epoch 00446: val_acc did not improve from 0.50653\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8125 - acc: 0.6734 - val_loss: 0.9961 - val_acc: 0.4987\n", "Epoch 447/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8194 - acc: 0.6792\n", "Epoch 00447: 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.8205 - acc: 0.6783 - val_loss: 0.9972 - val_acc: 0.5078\n", "Epoch 448/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8183 - acc: 0.6721\n", "Epoch 00448: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8186 - acc: 0.6723 - val_loss: 0.9955 - val_acc: 0.5000\n", "Epoch 449/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8229 - acc: 0.6713\n", "Epoch 00449: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8226 - acc: 0.6716 - val_loss: 0.9958 - val_acc: 0.5026\n", "Epoch 450/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8168 - acc: 0.6770\n", "Epoch 00450: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8165 - acc: 0.6772 - val_loss: 0.9937 - val_acc: 0.4987\n", "Epoch 451/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.8228 - acc: 0.6669\n", "Epoch 00451: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8224 - acc: 0.6670 - val_loss: 0.9945 - val_acc: 0.4922\n", "Epoch 452/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8274 - acc: 0.6634\n", "Epoch 00452: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8270 - acc: 0.6637 - val_loss: 1.0029 - val_acc: 0.5065\n", "Epoch 453/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8171 - acc: 0.6747\n", "Epoch 00453: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8173 - acc: 0.6746 - val_loss: 0.9968 - val_acc: 0.5052\n", "Epoch 454/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8190 - acc: 0.6739\n", "Epoch 00454: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8188 - acc: 0.6742 - val_loss: 0.9981 - val_acc: 0.5039\n", "Epoch 455/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8077 - acc: 0.6849\n", "Epoch 00455: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8079 - acc: 0.6851 - val_loss: 0.9960 - val_acc: 0.5039\n", "Epoch 456/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8116 - acc: 0.6837\n", "Epoch 00456: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8122 - acc: 0.6832 - val_loss: 0.9969 - val_acc: 0.5026\n", "Epoch 457/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8140 - acc: 0.6788\n", "Epoch 00457: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8144 - acc: 0.6783 - val_loss: 1.0001 - val_acc: 0.5078\n", "Epoch 458/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8162 - acc: 0.6800\n", "Epoch 00458: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8165 - acc: 0.6798 - val_loss: 0.9980 - val_acc: 0.5065\n", "Epoch 459/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8058 - acc: 0.6980\n", "Epoch 00459: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8062 - acc: 0.6979 - val_loss: 0.9953 - val_acc: 0.5065\n", "Epoch 460/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8083 - acc: 0.6819\n", "Epoch 00460: val_acc did not improve from 0.50783\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8080 - acc: 0.6821 - val_loss: 0.9960 - val_acc: 0.5065\n", "Epoch 461/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8059 - acc: 0.6788\n", "Epoch 00461: val_acc improved from 0.50783 to 0.50914, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.8059 - acc: 0.6791 - val_loss: 0.9994 - val_acc: 0.5091\n", "Epoch 462/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8095 - acc: 0.6788\n", "Epoch 00462: val_acc did not improve from 0.50914\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8092 - acc: 0.6791 - val_loss: 0.9950 - val_acc: 0.4987\n", "Epoch 463/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8153 - acc: 0.6800\n", "Epoch 00463: val_acc did not improve from 0.50914\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8149 - acc: 0.6802 - val_loss: 0.9986 - val_acc: 0.5065\n", "Epoch 464/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8080 - acc: 0.6830\n", "Epoch 00464: val_acc did not improve from 0.50914\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8077 - acc: 0.6832 - val_loss: 0.9983 - val_acc: 0.5091\n", "Epoch 465/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7976 - acc: 0.6943\n", "Epoch 00465: val_acc did not improve from 0.50914\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7972 - acc: 0.6949 - val_loss: 0.9956 - val_acc: 0.5013\n", "Epoch 466/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8057 - acc: 0.6909\n", "Epoch 00466: val_acc did not improve from 0.50914\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8062 - acc: 0.6907 - val_loss: 0.9958 - val_acc: 0.5000\n", "Epoch 467/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8051 - acc: 0.6852\n", "Epoch 00467: val_acc did not improve from 0.50914\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8057 - acc: 0.6851 - val_loss: 0.9962 - val_acc: 0.5013\n", "Epoch 468/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8030 - acc: 0.6916\n", "Epoch 00468: val_acc did not improve from 0.50914\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8029 - acc: 0.6918 - val_loss: 0.9950 - val_acc: 0.5013\n", "Epoch 469/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8093 - acc: 0.6909\n", "Epoch 00469: val_acc improved from 0.50914 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.8093 - acc: 0.6907 - val_loss: 0.9986 - val_acc: 0.5104\n", "Epoch 470/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7951 - acc: 0.6973\n", "Epoch 00470: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7952 - acc: 0.6971 - val_loss: 0.9955 - val_acc: 0.5000\n", "Epoch 471/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8067 - acc: 0.6773\n", "Epoch 00471: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8065 - acc: 0.6776 - val_loss: 1.0006 - val_acc: 0.4987\n", "Epoch 472/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8165 - acc: 0.6837\n", "Epoch 00472: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8159 - acc: 0.6843 - val_loss: 0.9966 - val_acc: 0.5013\n", "Epoch 473/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8013 - acc: 0.6856\n", "Epoch 00473: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8010 - acc: 0.6855 - val_loss: 0.9968 - val_acc: 0.5013\n", "Epoch 474/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7911 - acc: 0.6977\n", "Epoch 00474: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7910 - acc: 0.6975 - val_loss: 0.9946 - val_acc: 0.5026\n", "Epoch 475/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7952 - acc: 0.6954\n", "Epoch 00475: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7955 - acc: 0.6956 - val_loss: 0.9955 - val_acc: 0.5078\n", "Epoch 476/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7934 - acc: 0.6849\n", "Epoch 00476: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7935 - acc: 0.6847 - val_loss: 1.0069 - val_acc: 0.5000\n", "Epoch 477/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8001 - acc: 0.6788\n", "Epoch 00477: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8000 - acc: 0.6787 - val_loss: 0.9978 - val_acc: 0.5104\n", "Epoch 478/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8008 - acc: 0.6898\n", "Epoch 00478: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8005 - acc: 0.6900 - val_loss: 0.9959 - val_acc: 0.5052\n", "Epoch 479/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7944 - acc: 0.6988\n", "Epoch 00479: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7948 - acc: 0.6982 - val_loss: 0.9992 - val_acc: 0.5052\n", "Epoch 480/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7978 - acc: 0.6935\n", "Epoch 00480: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7978 - acc: 0.6937 - val_loss: 0.9974 - val_acc: 0.5052\n", "Epoch 481/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7989 - acc: 0.6909\n", "Epoch 00481: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7988 - acc: 0.6911 - val_loss: 0.9969 - val_acc: 0.5026\n", "Epoch 482/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8024 - acc: 0.6788\n", "Epoch 00482: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8030 - acc: 0.6791 - val_loss: 0.9971 - val_acc: 0.5039\n", "Epoch 483/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7890 - acc: 0.6988\n", "Epoch 00483: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7890 - acc: 0.6990 - val_loss: 0.9964 - val_acc: 0.5039\n", "Epoch 484/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7903 - acc: 0.7033\n", "Epoch 00484: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7901 - acc: 0.7035 - val_loss: 0.9979 - val_acc: 0.5065\n", "Epoch 485/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7988 - acc: 0.7003\n", "Epoch 00485: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7987 - acc: 0.7001 - val_loss: 1.0003 - val_acc: 0.5091\n", "Epoch 486/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7940 - acc: 0.6890\n", "Epoch 00486: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7937 - acc: 0.6892 - val_loss: 1.0051 - val_acc: 0.5078\n", "Epoch 487/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.8005 - acc: 0.6924\n", "Epoch 00487: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.8009 - acc: 0.6918 - val_loss: 0.9996 - val_acc: 0.5039\n", "Epoch 488/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7881 - acc: 0.7029\n", "Epoch 00488: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7880 - acc: 0.7027 - val_loss: 1.0008 - val_acc: 0.5013\n", "Epoch 489/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7858 - acc: 0.6950\n", "Epoch 00489: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7855 - acc: 0.6952 - val_loss: 1.0000 - val_acc: 0.5013\n", "Epoch 490/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7900 - acc: 0.7059\n", "Epoch 00490: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7900 - acc: 0.7061 - val_loss: 0.9986 - val_acc: 0.5000\n", "Epoch 491/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7839 - acc: 0.6984\n", "Epoch 00491: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7836 - acc: 0.6986 - val_loss: 1.0051 - val_acc: 0.5052\n", "Epoch 492/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7914 - acc: 0.6958\n", "Epoch 00492: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7919 - acc: 0.6956 - val_loss: 0.9969 - val_acc: 0.5026\n", "Epoch 493/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7790 - acc: 0.7059\n", "Epoch 00493: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7793 - acc: 0.7061 - val_loss: 1.0000 - val_acc: 0.5091\n", "Epoch 494/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7890 - acc: 0.7029\n", "Epoch 00494: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7887 - acc: 0.7031 - val_loss: 1.0010 - val_acc: 0.5052\n", "Epoch 495/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7902 - acc: 0.6954\n", "Epoch 00495: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7904 - acc: 0.6956 - val_loss: 1.0008 - val_acc: 0.5026\n", "Epoch 496/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7829 - acc: 0.7003\n", "Epoch 00496: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7825 - acc: 0.7009 - val_loss: 0.9976 - val_acc: 0.5052\n", "Epoch 497/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7792 - acc: 0.6905\n", "Epoch 00497: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7790 - acc: 0.6907 - val_loss: 1.0017 - val_acc: 0.5104\n", "Epoch 498/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7951 - acc: 0.6819\n", "Epoch 00498: val_acc did not improve from 0.51044\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7953 - acc: 0.6817 - val_loss: 1.0026 - val_acc: 0.5013\n", "Epoch 499/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7834 - acc: 0.6984\n", "Epoch 00499: val_acc improved from 0.51044 to 0.51567, 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.h5\n", "2661/2661 [==============================] - 4s 1ms/sample - loss: 0.7834 - acc: 0.6982 - val_loss: 1.0028 - val_acc: 0.5157\n", "Epoch 500/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7715 - acc: 0.7108\n", "Epoch 00500: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7712 - acc: 0.7110 - val_loss: 1.0005 - val_acc: 0.5026\n", "Epoch 501/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7713 - acc: 0.7112\n", "Epoch 00501: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7713 - acc: 0.7110 - val_loss: 1.0051 - val_acc: 0.5039\n", "Epoch 502/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7848 - acc: 0.7007\n", "Epoch 00502: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7844 - acc: 0.7012 - val_loss: 1.0022 - val_acc: 0.5000\n", "Epoch 503/4000\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.7904 - acc: 0.7039\n", "Epoch 00503: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7896 - acc: 0.7039 - val_loss: 1.0008 - val_acc: 0.5013\n", "Epoch 504/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7867 - acc: 0.7018\n", "Epoch 00504: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7871 - acc: 0.7012 - val_loss: 1.0068 - val_acc: 0.5052\n", "Epoch 505/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7784 - acc: 0.6999\n", "Epoch 00505: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7782 - acc: 0.7001 - val_loss: 1.0015 - val_acc: 0.5065\n", "Epoch 506/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7679 - acc: 0.7150\n", "Epoch 00506: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7682 - acc: 0.7148 - val_loss: 1.0002 - val_acc: 0.5065\n", "Epoch 507/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7745 - acc: 0.7078\n", "Epoch 00507: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7750 - acc: 0.7073 - val_loss: 1.0026 - val_acc: 0.5052\n", "Epoch 508/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7779 - acc: 0.7003\n", "Epoch 00508: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7773 - acc: 0.7009 - val_loss: 1.0043 - val_acc: 0.5039\n", "Epoch 509/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7773 - acc: 0.7056\n", "Epoch 00509: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7773 - acc: 0.7057 - val_loss: 1.0043 - val_acc: 0.5026\n", "Epoch 510/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7688 - acc: 0.7078\n", "Epoch 00510: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7685 - acc: 0.7084 - val_loss: 1.0045 - val_acc: 0.5052\n", "Epoch 511/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7695 - acc: 0.7090\n", "Epoch 00511: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7697 - acc: 0.7088 - val_loss: 1.0071 - val_acc: 0.5026\n", "Epoch 512/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7813 - acc: 0.6995\n", "Epoch 00512: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7818 - acc: 0.6994 - val_loss: 1.0039 - val_acc: 0.5065\n", "Epoch 513/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7670 - acc: 0.7146\n", "Epoch 00513: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7668 - acc: 0.7148 - val_loss: 1.0040 - val_acc: 0.5078\n", "Epoch 514/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7776 - acc: 0.7018\n", "Epoch 00514: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7779 - acc: 0.7020 - val_loss: 1.0046 - val_acc: 0.5078\n", "Epoch 515/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7786 - acc: 0.7033\n", "Epoch 00515: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7784 - acc: 0.7031 - val_loss: 1.0044 - val_acc: 0.5026\n", "Epoch 516/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7779 - acc: 0.7029\n", "Epoch 00516: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7771 - acc: 0.7035 - val_loss: 1.0035 - val_acc: 0.5039\n", "Epoch 517/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7677 - acc: 0.7135\n", "Epoch 00517: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7675 - acc: 0.7133 - val_loss: 1.0030 - val_acc: 0.5078\n", "Epoch 518/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7720 - acc: 0.7116\n", "Epoch 00518: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7719 - acc: 0.7118 - val_loss: 1.0068 - val_acc: 0.5039\n", "Epoch 519/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7720 - acc: 0.7108\n", "Epoch 00519: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7723 - acc: 0.7106 - val_loss: 1.0073 - val_acc: 0.5078\n", "Epoch 520/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7684 - acc: 0.7199\n", "Epoch 00520: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7676 - acc: 0.7204 - val_loss: 1.0007 - val_acc: 0.5065\n", "Epoch 521/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7552 - acc: 0.7188\n", "Epoch 00521: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7550 - acc: 0.7189 - val_loss: 1.0028 - val_acc: 0.5091\n", "Epoch 522/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7724 - acc: 0.7093\n", "Epoch 00522: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7725 - acc: 0.7095 - val_loss: 1.0059 - val_acc: 0.5052\n", "Epoch 523/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7641 - acc: 0.7142\n", "Epoch 00523: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7639 - acc: 0.7144 - val_loss: 1.0059 - val_acc: 0.5026\n", "Epoch 524/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7647 - acc: 0.7195\n", "Epoch 00524: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7653 - acc: 0.7193 - val_loss: 1.0117 - val_acc: 0.5078\n", "Epoch 525/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7668 - acc: 0.7146\n", "Epoch 00525: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7669 - acc: 0.7144 - val_loss: 1.0104 - val_acc: 0.4935\n", "Epoch 526/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7665 - acc: 0.7067\n", "Epoch 00526: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7664 - acc: 0.7069 - val_loss: 1.0074 - val_acc: 0.5078\n", "Epoch 527/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7747 - acc: 0.7090\n", "Epoch 00527: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7749 - acc: 0.7088 - val_loss: 1.0114 - val_acc: 0.5000\n", "Epoch 528/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7687 - acc: 0.7029\n", "Epoch 00528: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7694 - acc: 0.7027 - val_loss: 1.0075 - val_acc: 0.5052\n", "Epoch 529/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7610 - acc: 0.7206\n", "Epoch 00529: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7605 - acc: 0.7212 - val_loss: 1.0065 - val_acc: 0.5052\n", "Epoch 530/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7643 - acc: 0.7203\n", "Epoch 00530: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7636 - acc: 0.7208 - val_loss: 1.0099 - val_acc: 0.5065\n", "Epoch 531/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7729 - acc: 0.7131\n", "Epoch 00531: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7731 - acc: 0.7129 - val_loss: 1.0100 - val_acc: 0.5026\n", "Epoch 532/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7591 - acc: 0.7210\n", "Epoch 00532: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7590 - acc: 0.7208 - val_loss: 1.0107 - val_acc: 0.5039\n", "Epoch 533/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7542 - acc: 0.7263\n", "Epoch 00533: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7541 - acc: 0.7264 - val_loss: 1.0092 - val_acc: 0.5091\n", "Epoch 534/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7631 - acc: 0.7131\n", "Epoch 00534: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7626 - acc: 0.7133 - val_loss: 1.0096 - val_acc: 0.5065\n", "Epoch 535/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7612 - acc: 0.7161\n", "Epoch 00535: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7610 - acc: 0.7163 - val_loss: 1.0080 - val_acc: 0.5026\n", "Epoch 536/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7671 - acc: 0.7048\n", "Epoch 00536: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7670 - acc: 0.7046 - val_loss: 1.0112 - val_acc: 0.5065\n", "Epoch 537/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7621 - acc: 0.7165\n", "Epoch 00537: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7614 - acc: 0.7170 - val_loss: 1.0098 - val_acc: 0.5039\n", "Epoch 538/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7573 - acc: 0.7218\n", "Epoch 00538: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7567 - acc: 0.7223 - val_loss: 1.0101 - val_acc: 0.5052\n", "Epoch 539/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7584 - acc: 0.7214\n", "Epoch 00539: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7582 - acc: 0.7215 - val_loss: 1.0089 - val_acc: 0.5065\n", "Epoch 540/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7662 - acc: 0.7142\n", "Epoch 00540: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7660 - acc: 0.7140 - val_loss: 1.0101 - val_acc: 0.5039\n", "Epoch 541/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7490 - acc: 0.7274\n", "Epoch 00541: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7484 - acc: 0.7279 - val_loss: 1.0095 - val_acc: 0.5052\n", "Epoch 542/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7485 - acc: 0.7218\n", "Epoch 00542: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7484 - acc: 0.7215 - val_loss: 1.0107 - val_acc: 0.5052\n", "Epoch 543/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7638 - acc: 0.7142\n", "Epoch 00543: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7640 - acc: 0.7140 - val_loss: 1.0096 - val_acc: 0.5052\n", "Epoch 544/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7537 - acc: 0.7240\n", "Epoch 00544: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7542 - acc: 0.7234 - val_loss: 1.0102 - val_acc: 0.5052\n", "Epoch 545/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7459 - acc: 0.7327\n", "Epoch 00545: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7455 - acc: 0.7328 - val_loss: 1.0102 - val_acc: 0.5026\n", "Epoch 546/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7470 - acc: 0.7248\n", "Epoch 00546: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7469 - acc: 0.7249 - val_loss: 1.0107 - val_acc: 0.5013\n", "Epoch 547/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7487 - acc: 0.7263\n", "Epoch 00547: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7498 - acc: 0.7257 - val_loss: 1.0102 - val_acc: 0.5013\n", "Epoch 548/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7483 - acc: 0.7255\n", "Epoch 00548: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7478 - acc: 0.7257 - val_loss: 1.0116 - val_acc: 0.5065\n", "Epoch 549/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7483 - acc: 0.7293\n", "Epoch 00549: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7479 - acc: 0.7298 - val_loss: 1.0091 - val_acc: 0.5091\n", "Epoch 550/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7514 - acc: 0.7188\n", "Epoch 00550: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7510 - acc: 0.7189 - val_loss: 1.0121 - val_acc: 0.5039\n", "Epoch 551/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7542 - acc: 0.7274\n", "Epoch 00551: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7539 - acc: 0.7275 - val_loss: 1.0148 - val_acc: 0.5039\n", "Epoch 552/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7542 - acc: 0.7244\n", "Epoch 00552: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7545 - acc: 0.7242 - val_loss: 1.0110 - val_acc: 0.5091\n", "Epoch 553/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7303 - acc: 0.7410\n", "Epoch 00553: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7300 - acc: 0.7411 - val_loss: 1.0147 - val_acc: 0.5052\n", "Epoch 554/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7418 - acc: 0.7225\n", "Epoch 00554: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7417 - acc: 0.7223 - val_loss: 1.0145 - val_acc: 0.5000\n", "Epoch 555/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7448 - acc: 0.7263\n", "Epoch 00555: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7448 - acc: 0.7260 - val_loss: 1.0175 - val_acc: 0.5039\n", "Epoch 556/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7391 - acc: 0.7236\n", "Epoch 00556: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7396 - acc: 0.7234 - val_loss: 1.0170 - val_acc: 0.5039\n", "Epoch 557/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7383 - acc: 0.7304\n", "Epoch 00557: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7384 - acc: 0.7306 - val_loss: 1.0155 - val_acc: 0.5013\n", "Epoch 558/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7476 - acc: 0.7210\n", "Epoch 00558: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7474 - acc: 0.7212 - val_loss: 1.0210 - val_acc: 0.5039\n", "Epoch 559/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7526 - acc: 0.7184\n", "Epoch 00559: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7527 - acc: 0.7178 - val_loss: 1.0203 - val_acc: 0.4987\n", "Epoch 560/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7540 - acc: 0.7150\n", "Epoch 00560: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7540 - acc: 0.7148 - val_loss: 1.0146 - val_acc: 0.5013\n", "Epoch 561/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7406 - acc: 0.7267\n", "Epoch 00561: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7400 - acc: 0.7268 - val_loss: 1.0191 - val_acc: 0.5039\n", "Epoch 562/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7459 - acc: 0.7188\n", "Epoch 00562: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7456 - acc: 0.7185 - val_loss: 1.0171 - val_acc: 0.5026\n", "Epoch 563/4000\n", "2656/2661 [============================>.] - ETA: 0s - loss: 0.7364 - acc: 0.7331\n", "Epoch 00563: val_acc did not improve from 0.51567\n", "2661/2661 [==============================] - 3s 1ms/sample - loss: 0.7365 - acc: 0.7328 - val_loss: 1.0169 - val_acc: 0.5052\n", "Epoch 00563: early stopping\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "w0XLsEfCMbtg", "colab_type": "code", "outputId": "ec17cf3b-3e1f-44c8-a49f-7ba49704fb69", "colab": { "base_uri": "https://localhost:8080/", "height": 283 } }, "source": [ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "\n", "\n", "\n", "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()\n", "\n", "plot_history(history)" ], "execution_count": 25, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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