X = tf.placeholder(tf.float32, [None, time_step, input_size])

Y = tf.placeholder(tf.float32, [None, time_step, output_size])

输入层、输出层的权重和偏置

weights = {

'in': tf.Variable(tf.random_normal([input_size, rnn_unit])),

'out': tf.Variable(tf.random_normal([rnn_unit, 1]))

}

biases = {

'in': tf.Variable(tf.constant(0.1, shape=[rnn_unit, ])),

'out': tf.Variable(tf.constant(0.1, shape=[1, ]))

}

X = tf.placeholder(tf.float32, [None, time_step, input_size]) 这个相当于创建一个内存大小的空间

'in': tf.Variable(tf.random_normal([input_size, rnn_unit])) 利用正态分布初始化参数