def LSTM_sequence_classifer_net(feature, num_output_classes, embedding_dim, LSTM_dim, cell_dim):
embedding_function = embedding(feature, embedding_dim)
LSTM_function = LSTMP_component_with_self_stabilization(
embedding_function.output, LSTM_dim, cell_dim)[0]
thought_vector = sequence.last(LSTM_function)
return linear_layer(thought_vector, num_output_classes)
def LSTM_sequence_classifer_net(input, num_output_classes, embedding_dim, LSTM_dim, cell_dim):
embedding_function = embedding(input, embedding_dim)
LSTM_function = LSTMP_component_with_self_stabilization(
embedding_function.output, LSTM_dim, cell_dim)[0]
thought_vector = sequence.last(LSTM_function)
return linear_layer(thought_vector, num_output_classes)
def LSTM_sequence_classifer_net(input, num_output_classes, embedding_dim,
LSTM_dim, cell_dim):
embedded_inputs = embedding(input, embedding_dim)
lstm_outputs = simple_lstm(embedded_inputs, LSTM_dim, cell_dim)[0]
thought_vector = sequence.last(lstm_outputs)
return linear_layer(thought_vector, num_output_classes)
def LSTM_sequence_classifer_net(input, num_output_classes, embedding_dim,
LSTM_dim, cell_dim):
embedded_inputs = embedding(input, embedding_dim)
lstm_outputs = simple_lstm(embedded_inputs, LSTM_dim, cell_dim)[0]
thought_vector = sequence.last(lstm_outputs)
return linear_layer(thought_vector, num_output_classes)