python版本 3.6.3
TensorFlow版本 1.2.1
用写TensorFlow的时候出现了一个bug：
TypeError: Input 'b' of 'MatMul' Op has type float32 that does not match type float64 of argument 'a'.
数据类型错误：MatMul里输入的数据类型为‘float32’的‘b’无法和数据类型为‘float64’的‘a’匹配。
1.看一下添加层内部matmul里的情况：
Wx_plus_b = tf.matmul(inputs,Weights) + biases
2.那么inputs应该就是底层实现里的‘a’，Weights为‘b’，
沿着参数传递找到数据源头：
x_data = np.linspace(-1,1,300)[:,np.newaxis]
3.在代码后方加入一个类型转换：
x_data = np.linspace(-1,1,300)[:,np.newaxis].astype('float32')
F5
Well done.
源码：
import tensorflow as tf
import numpy as np
#定义添加层
def add_layer(inputs, in_size, out_size,
activation_function = None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1,out_size])+0.1)
Wx_plus_b = tf.matmul(inputs,Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
#生成数据
x_data = np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data) - 0.5 + noise
#建立空间
xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])
#添加层
l1 = add_layer(x_data,1,10,activation_function = tf.nn.relu)
prediction = add_layer(l1,10,1,activation_function = None)
#设置参数
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),
reduction_indices = [1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()
#会话控制
sess = tf.Session()
sess.run(init)
for i in range(1000):
sess.run(train_step,feed_dict = {xs:x_data,ys:y_data})
if i %50 ==0:
print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
