prediction.py 文件源码

def RNN(x, weights, biases):
    # permuting batch_size and n_input
    x = tf.transpose(x, [1, 0, 2])
    # reshape into (n_steo*batch_size,n_input)
    x = tf.reshape(x, [-1, n_input])
    # split to get a list of 'n_steps'
    x = tf.split(0, n_steps, x)
    with tf.variable_scope('n_steps4'):
        lstm_cell = rnn_cell.BasicLSTMCell(
            n_hidden, forget_bias=1.0)
        outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
    return tf.matmul(outputs[-1], weights['out']) + biases['out']