def train(x_train, y_train, vocab_size, feature_size, save_path):
print("\nTrain...")
model = TextCNN(vocab_size, feature_size, args.embed_size,
args.num_classes, args.num_filters, args.filter_sizes,
args.regularizers_lambda, args.dropout_rate)
model.summary()
model.compile(tf.optimizers.Adam(learning_rate=args.learning_rate),
loss='binary_crossentropy',
metrics=[micro_f1, macro_f1])
# parallel_model = keras.utils.multi_gpu_model(model, gpus=2)
# parallel_model.compile(tf.optimizers.Adam(learning_rate=args.learning_rate), loss='binary_crossentropy',
# metrics=[micro_f1, macro_f1])
# keras.utils.plot_model(model, show_shapes=True, to_file=os.path.join(args.results_dir, timestamp, "model.pdf"))
# y_train = tf.one_hot(y_train, args.num_classes)
# tb_callback = keras.callbacks.TensorBoard(os.path.join(args.results_dir, timestamp, 'log/'),
# histogram_freq=0.1, write_graph=True,
# write_grads=True, write_images=True,
# embeddings_freq=0.5, update_freq='batch')
history = model.fit(
x=x_train,
y=y_train,
batch_size=args.batch_size,
epochs=args.epochs,
steps_per_epoch=19302 // args.batch_size,
# callbacks=[tb_callback],
validation_split=args.fraction_validation,
shuffle=True)
print("\nSaving model...")
keras.models.save_model(model, save_path)
pprint(history.history)
batch_size = 32
embedding_dims = 50
epochs = 10
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)...')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
print('Build model...')
model = TextCNN(maxlen, max_features, embedding_dims).get_model()
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
print('Train...')
early_stopping = EarlyStopping(monitor='val_acc', patience=3, mode='max')
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[early_stopping],
validation_data=(x_test, y_test))
print('Test...')
result = model.predict(x_test)