tf 神經網路的簡單搭建

import tensorflow as tf

import numpy as np
defadd_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]))
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
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
xs = tf.placeholder(tf.float32,[none,1])
ys = tf.placeholder(tf.float32,[none,1])
l1 = add_layer(xs, 1, 10, activation_function=tf.nn
.relu)
prediction = add_layer(l1, 10, 1, activation_function=none)
# loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))  same as following
loss = tf.reduce_mean(tf.square(ys - prediction))
train_step = tf.train
.gradientdescentoptimizer(0.5).minimize(loss)
init = tf.global_variables_initializer()
sess = tf.session()
sess.run(init)
for i in range(1000):
sess.run(train_step,feed_dict=)
if i%20 == 0:
print(i,sess.run(loss,feed_dict=))

一目了然，當沒有設定該引數時，該引數的預設值是none,將tensor降到0維，也就是乙個數

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