我是tensorflow的新手 . 此代码仅适用于简单的神经网络 . 我认为问题可能来自：
x_data = np.linspace(-0.5,0.5,200)[:np..newaxis]
我试着写没有 [:np.newaxis] ，但它看起来是一样的 .
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 回答
定义的占位符（
x和y）都是二维的，因此您应该将输入数组重新排名为2级 . 尝试添加以下内容：