def model_wrapper():
model = Sequential()
model.add(
Dense(
dense_width,
input_dim=vector_size,
kernel_regularizer=regularizers.l2(regularization),
activity_regularizer=regularizers.l1(regularization),
activation='relu',
))
# add Dense layer with relu activation
model.add(
Dense(
dense_width,
kernel_regularizer=regularizers.l2(regularization),
activity_regularizer=regularizers.l1(regularization),
activation='relu',
))
# add Dense layer
model.add(Dense(1, activation='sigmoid'))
optimizer_fn = _get_optimizer(optimizer, learn_rate_mult)
# Compile model
model.compile(loss='binary_crossentropy',
optimizer=optimizer_fn,
metrics=['acc'])
if verbose >= 1:
model.summary()
return model
def model_wrapper():
model = Sequential()
# add first embedding layer with pretrained wikipedia weights
model.add(
Embedding(
embedding_matrix.shape[0],
embedding_matrix.shape[1],
weights=[embedding_matrix],
input_length=max_sequence_length,
trainable=False
)
)
# add LSTM layer
model.add(
LSTM(
lstm_out_width,
input_shape=(max_sequence_length, ),
go_backwards=backwards,
dropout=dropout,
recurrent_dropout=dropout,
return_sequences=True,
kernel_constraint=MaxNorm(),
)
)
model.add(
MaxPooling1D(
pool_size=lstm_pool_size,
)
)
model.add(
Flatten()
)
# Add output layer
model.add(
Dense(
1,
activation='sigmoid'
)
)
optimizer_fn = _get_optimizer(optimizer, learn_rate)
# Compile model
model.compile(
loss='binary_crossentropy', optimizer=optimizer_fn,
metrics=['acc'])
if verbose >= 1:
model.summary()
return model