mirror.py 文件源码

def lstm_cell(X, output, state):
            """Create a LSTM cell. See e.g.: http://arxiv.org/pdf/1402.1128v1.pdf
            Note that in this formulation, we omit the various connections between the
            previous state and the gates."""
            X_output = tf.concat(1, [X, output])
            all_logits = tf.matmul(X_output, W_lstm) + b_lstm

            input_gate = tf.sigmoid(all_logits[:, :NUM_NODES])
            forget_gate = tf.sigmoid(all_logits[:, NUM_NODES: NUM_NODES * 2])
            output_gate = tf.sigmoid(all_logits[:, NUM_NODES * 2: NUM_NODES * 3])
            temp_state = all_logits[:, NUM_NODES * 3:]
            state = forget_gate * state + input_gate * tf.tanh(temp_state)

            return output_gate * tf.tanh(state), state


        # Input data.