def test_one_layer(self):
bi_lm = BiLM(token_num=101,
rnn_layer_num=1,
rnn_units=50,
rnn_type='gru')
input_layer, output_layer = bi_lm.get_feature_layers()
self.assertEqual((None, None), tuple(input_layer.shape))
self.assertEqual((None, None, 100), tuple(output_layer.shape))
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.summary()
def test_bidirectional(self):
bi_lm = BiLM(token_num=102,
rnn_layer_num=1,
rnn_units=50,
rnn_type='gru',
use_bidirectional=True)
input_layer, output_layer = bi_lm.get_feature_layers()
self.assertEqual((None, None), input_layer._keras_shape)
self.assertEqual((None, None, 200), output_layer._keras_shape)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.summary()
def test_weighted_sum(self):
bi_lm = BiLM(token_num=107,
embedding_dim=108,
rnn_layer_num=6,
rnn_keep_num=7,
rnn_units=108,
rnn_type='lstm',
use_normalization=True)
input_layer, output_layer = bi_lm.get_feature_layers(
use_weighted_sum=True)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.summary()
def test_input_layer(self):
input_layer = keras.layers.Input((None, ), name='New-Input')
bi_lm = BiLM(token_num=104,
rnn_layer_num=6,
rnn_keep_num=3,
rnn_units=50,
rnn_type='gru')
output_layer = bi_lm.get_feature_layers(input_layer=input_layer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.summary()
for layer in bi_lm.model.layers:
try:
new_layer = model.get_layer(name=layer.name)
self.assertEqual(layer.get_weights(), new_layer.get_weights())
except ValueError:
pass
def test_multiple_layers(self):
bi_lm = BiLM(token_num=103,
rnn_layer_num=6,
rnn_keep_num=3,
rnn_units=50,
rnn_type='lstm')
input_layer, output_layer = bi_lm.get_feature_layers()
self.assertEqual((None, None), tuple(input_layer.shape))
self.assertEqual((None, None, 300), tuple(output_layer.shape))
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.summary()
for layer in bi_lm.model.layers:
try:
new_layer = model.get_layer(name=layer.name)
self.assertEqual(layer.get_weights(), new_layer.get_weights())
except ValueError:
pass
def test_no_embedding(self):
input_layer = keras.layers.Input((None, 106), name='New-Input')
bi_lm = BiLM(token_num=105,
has_embedding=False,
embedding_dim=106,
rnn_layer_num=6,
rnn_keep_num=1,
rnn_units=50,
rnn_type='lstm')
output_layer = bi_lm.get_feature_layers(input_layer=input_layer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.summary()
for layer in bi_lm.model.layers:
try:
new_layer = model.get_layer(name=layer.name)
self.assertEqual(layer.get_weights(), new_layer.get_weights())
except ValueError:
pass
print('Fit LM...')
if os.path.exists(LM_MODEL_PATH):
bi_lm = BiLM(model_path=LM_MODEL_PATH)
else:
bi_lm = BiLM(token_num=len(word_dict))
bi_lm.model.fit_generator(
generator=train_lm_generator(batch_size=batch_size),
steps_per_epoch=len(sentences) // batch_size,
epochs=epoch_num,
verbose=True,
)
bi_lm.save_model(LM_MODEL_PATH)
# Build model for classification
input_layer, feature_layer = bi_lm.get_feature_layers()
lstm_layer = keras.layers.Bidirectional(
keras.layers.LSTM(units=50),
name='Bi-LSTM',
)(feature_layer)
dense_layer = keras.layers.Dense(
units=2,
activation='softmax',
name='Dense',
)(lstm_layer)
model = keras.models.Model(inputs=input_layer, outputs=dense_layer)
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['categorical_accuracy'],
)