莫烦tensorflow教程笔记（五）

import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltdef add_layer(inputs,in_size,out_size,activation=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 is None:        output = Wx_plus_b    else:        output = activation(Wx_plus_b)    return outputx_data = np.linspace(-1,1,300)[:,np.newaxis]#.astype(np.float32)noise = np.random.normal(0,0.05,x_data.shape)print(x_data.dtype)y_data = np.square(x_data) - 0.5 + noisexs = tf.placeholder(tf.float32,[None,1])ys = tf.placeholder(tf.float32,[None,1])L1 = add_layer(xs,in_size=1,out_size=10,activation=tf.nn.relu)prediction = add_layer(L1,in_size=10,out_size=1,activation=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()session = tf.Session()session.run(init)fig = plt.figure()ax = fig.add_subplot(1,1,1)ax.scatter(x_data,y_data)plt.ion()          ###开启interactive 模式 能连续作图plt.show()for step in range(1000):    session.run(train_step,feed_dict={xs:x_data,ys:y_data})    if step % 50 == 0:        print(step,session.run(loss,feed_dict={xs:x_data,ys:y_data}))        try:            ax.lines.remove(lines[0])        except Exception:            pass        prediction_value = session.run(prediction,feed_dict={xs:x_data,ys:y_data})        lines = ax.plot(x_data,prediction_value,'r-',lw=5)        plt.pause(0.1)plt.ioff()     ##如果不加入这两句 在pycharm中迭代完成后图像会自动关闭plt.show()