def train_cnn_model(emb_layer, x_train, y_train, x_val, y_val, opt):
model = CNN(embedding_layer=emb_layer,
num_words=opt.n_words,
embedding_dim=opt.embed_dim,
filter_sizes=opt.cnn_filter_shapes,
feature_maps=opt.filter_sizes,
max_seq_length=opt.sent_len,
dropout_rate=opt.dropout_ratio,
hidden_units=200,
nb_classes=2).build_model()
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(),
metrics=['accuracy'])
# y_train = y_train.reshape(-1, 1)
# model = build_model(emb_layer, opt)
print(model.summary())
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
history = model.fit(x_train,
y_train,
epochs=opt.cnn_epoch,
batch_size=opt.batch_size,
verbose=1,
validation_data=(x_val, y_val),
callbacks=[early_stopping])
with open("CNN_train_history.txt", "w") as f:
print(history.history, file=f)
return model
def train_baseline_cnn(emb_layer, x_train, y_train, x_val, y_val, opt):
model = CNN(embedding_layer=emb_layer,
num_words=opt.transfer_n_words,
embedding_dim=opt.baseline_embed_dim,
filter_sizes=opt.cnn_filter_shapes,
feature_maps=opt.filter_sizes,
max_seq_length=opt.baseline_sent_len,
dropout_rate=opt.baseline_drop_out_ratio,
hidden_units=200,
nb_classes=2).build_model()
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(),
metrics=['accuracy'])
# y_train = y_train.reshape(-1, 1)
# model = build_model(emb_layer, opt)
print(model.summary())
tb_call_back = TensorBoard(log_dir=f'{opt.tbpath}/baseline_cnn_{time()}',
histogram_freq=1,
write_graph=True,
write_images=True)
checkpoint = ModelCheckpoint("baseline_cnn.h5",
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
history = model.fit(x_train,
y_train,
epochs=opt.baseline_epochs,
batch_size=opt.baseline_batchsize,
verbose=1,
validation_data=(x_val, y_val),
callbacks=[early_stopping, tb_call_back, checkpoint])
with open("CNN_train_baseline_history.txt", "w") as f:
print(history.history, file=f)
return model
kernel_sizes=KERNEL_SIZES,
feature_maps=FEATURE_MAPS,
max_seq_length=MAX_SEQ_LENGTH,
use_char=USE_CHAR,
char_max_length=CHAR_MAX_LENGTH,
alphabet_size=ALPHABET_SIZE,
char_kernel_sizes=CHAR_KERNEL_SIZES,
char_feature_maps=CHAR_FEATURE_MAPS,
dropout_rate=DROPOUT_RATE,
hidden_units=HIDDEN_UNITS,
nb_classes=NB_CLASSES
).build_model()
model.compile(
loss='categorical_crossentropy',
optimizer=keras.optimizers.Adam(),
metrics=['accuracy']
)
# model.summary()
history = model.fit(
X_train, y_train,
epochs=NB_EPOCHS,
batch_size=BATCH_SIZE,
validation_data=(X_val, y_val),
callbacks=[
keras.callbacks.ModelCheckpoint(
'model-%i.h5' % (i + 1), monitor='val_loss', verbose=1, save_best_only=True, mode='min'
),
# keras.callbacks.TensorBoard(log_dir='./logs/temp', write_graph=True)