def train_discriminator(X_train, Y_train, model):
sgd = optimizers.SGD(lr=0.01)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
earlyStopping = callbacks.EarlyStopping(monitor='val_loss',
patience=10,
verbose=0,
mode='min')
mcp_save = callbacks.ModelCheckpoint('bestmodel.hdf5',
save_best_only=True,
monitor='val_loss',
mode='min')
reduce_lr_loss = callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10,
verbose=1,
epsilon=1e-4,
mode='min')
csv_logger = callbacks.CSVLogger('Discriminator_stats_cnn.csv')
history = model.fit(X_train,
Y_train,
callbacks=[mcp_save, reduce_lr_loss, csv_logger],
validation_split=0.1,
epochs=100,
batch_size=16)
outs = Dense(units=num_intents,
activation='softmax')(flat) # Sigmoid or softmax?
model = Model(inputs=main_input, outputs=outs)
INITIAL_LEARN_RATE = 10e-5
optimizer = Adam(learning_rate=INITIAL_LEARN_RATE)
# optimizer = RMSprop(learning_rate = 3e-5)
model.compile(optimizer,
loss=categorical_crossentropy(label_smoothing=0.005),
metrics=['accuracy'])
cbks = [
callbacks.TerminateOnNaN(),
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=1),
callbacks.EarlyStopping(monitor='loss',
min_delta=0,
patience=10,
verbose=1,
baseline=None,
restore_best_weights=True)
]
model.summary()
test_set = (test_x, test_y)
# Can't have validation split because too many intents.
# Val accuracy of 0.94 is good
model.fit(x=embed_xvals,