Tensorflow 搭建自己的神经网络(一)

下述几段代码，是看b站上莫凡的视频学习的：

例2：

import tensorflow as tf
import numpy as np

#creat da
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

#create tensorflow structure start
Weights = tf.Variable(tf.random_uniform([1], -1, 0, 1))
biases = tf.Variable(tf.zeros([1]))

y=Weights * x_data+biases

loss = tf.reduce_mean(tf.square(y-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.initialize_all_variables()
#create tensorflow structure end

sess = tf.Session()
sess.run(init) #Very inmportant
step in range(201):
    sess.run(train)
    if(step % 20 == 0):
        print(step,sess.run(Weights), sess.run(biases))

 

Session会话控制：

import numpy as np
import tensorflow as tf

state = tf.Variable(0,name = "counter")
one = tf.constant(1)

new_value = tf.add(state, )
update = tf.assign(state, new_value)
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))

 

placeholder：

import numpy as np
import tensorflow as tf

input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)

res = tf.multiply(input1,input2)

with tf.Session() as sess:
    print(sess.run(res,feed_dict={input1:[7.,2.],input2:[2.]}))

 

例3 build a neural network（输入层1个节点，隐藏层10个节点，输出层1个节点）

import numpy as np
import tensorflow as tf

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

x_data = np.linspace(-1, 1, 300)[:,np.newaxis]#array to 300*1 matrix
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]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init)

for i in range(1001):
    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}))

 可视化：

import matplotlib.pyplot as plt
.
.
.
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()
for i in range(1001):
    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}))
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value = sess.run(prediction, feed_dict={xs:x_data})
        lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
        plt.pause(0.1)
        
#plt.show()