ValueError:不能为张量的Placeholder_32:0提供形状(200)的值，它有形状(?,1)

I am new to tensorflow. This code is just for a simple neural network. I think the problem maybe is from:

我是新手。这个代码仅仅是一个简单的神经网络。我认为问题可能来自:

x_data = np.linspace(-0.5,0.5,200)[:np..newaxis]

I tried to write without [:np.newaxis], but it looks like the same.

我试着不写[:np]。但是它看起来是一样的。

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

x_data = np.linspace(-0.5,0.5,200)[:np.newaxis]
noise = np.random.normal(0,0.02,x_data.shape)
y_data = np.square(x_data) + noise

x = tf.placeholder(tf.float32,[None,1])
y = tf.placeholder(tf.float32,[None,1])

Weights_L1 = tf.Variable(tf.random_normal([1,10]))
biases_L1 = tf.Variable(tf.zeros([1,10]))
Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + biases_L1
L1 = tf.nn.tanh(Wx_plus_b_L1)

Weights_L2 = tf.Variable(tf.random_normal([10,1]))
biases_L2 = tf.Variable(tf.zeros([1,1]))
Wx_plus_b_L2 = tf.matmul(L1,Weights_L2) + biases_L2
prediction = tf.nn.tanh(Wx_plus_b_L2)

loss = tf.reduce_mean(tf.square(y-prediction))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for _ in range(2000):
        sess.run(train_step,feed_dict={x:x_data,y:y_data})

    prediction_value = sess.run(prediction,feed_dict={x:x_data})
    plt.figure()
    plt.scatter(x_data,y_data)
    plt.plot(x_data,prediction_value,'r-',lw=5)
    plt.show()

1 个解决方案

#1

The defined placeholders (both x and y) are 2-dimensional, so you should reshape the input arrays to rank 2. Try to add this:

定义的占位符(x和y)都是二维的，所以您应该将输入数组重新调整为2。尝试添加:

x_data = x_data.reshape([-1,1])
y_data = y_data.reshape([-1,1])