def create_model(embeddings_file: str, input_size, input_length, hidden_size):
"""
Create simple regression model with a single embedding layer.
:param embeddings_file: embeddings file to load
:param input_size: size of input layer
:param hidden_size: size of embeddings
:return: Keras model
"""
model = Sequential()
model.add(
Embedding(input_size,
hidden_size,
input_length=input_length,
name='embedding'))
model.add(keras.layers.Lambda(lambda x: keras.backend.sum(x, axis=1)))
#model.add(Flatten())
model.add(Dense(92, activation="sigmoid"))
if embeddings_file is not None:
embeddings = np.loadtxt(embeddings_file)
model.get_layer("embedding").set_weights([embeddings])
#model.summary()
return model
def create_model(embeddings_file: str, input_size, hidden_size, output_size):
"""
Create simple regression model with a single embedding layer.
:param embeddings_file: embeddings file to load
:param input_size: size of input layer
:param hidden_size: size of embeddings
:param output_size: size of output layer
:return: Keras model
"""
model = Sequential()
model.add(
Embedding(input_size, hidden_size, input_length=1, name='embedding'))
if embeddings_file is not None:
embeddings = np.loadtxt(embeddings_file)
model.get_layer("embedding").set_weights([embeddings])
model.add(Flatten())
model.add(Dense(output_size, activation="sigmoid"))
return model
