refactor.py 文件源码

def mlp(x, y):
    op_list = list()
    op_list.append(x)

    w_input = tf.Variable(tf.truncated_normal([n_input, n_hidden], stddev=0.1))
    b_input = tf.Variable(tf.constant(0., shape=[n_hidden]))
    op_list.append(tf.matmul(op_list[-1], w_input) + b_input)
    for i in range(n_h_layer):
        w = tf.Variable(tf.truncated_normal([n_hidden, n_hidden], stddev=0.1))
        b = tf.Variable(tf.constant(0., shape=[n_hidden]))
        op_list.append(tf.matmul(op_list[-1], w) + b)

    w_output = tf.Variable(tf.truncated_normal([n_hidden, n_output], stddev=0.1))
    b_output = tf.Variable(tf.constant(0., shape=[n_output]))
    op_list.append(tf.matmul(op_list[-1], w_output) + b_output)
    loss = tf.nn.l2_loss(tf.sub(op_list[-1], y))
    return op_list[-1], loss