deeplearn学习笔记 tensorflow2

tensorflow第二弹

继续学习tensorflow

构建了自己的第一个神经网络，训练函数y=x^2-0.5，并且用图标打印出来，并且可以显示函数训练变化过程

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

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

fig = plt.figure() ##生成一个图片框
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x_data, y_data)
plt.show()



for i in range(1000):
    sess.run(train_step, feed_dict = {xs: x_data, ys: y_data} )
    if i % 50:
        #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) #暂停