cnn-mnist.rar_CNN_CNN mnist_MNIST_MNIST CNN_卷积神经网络

import tensorflow as tf import numpy as np def add_layer(inputs, in_size, out_size, activation_function=): 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(-1,1,10, dtype=np.float32)[:, np.newaxis] #noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32) y_data = x_data #- 0.5 #+ noise # print(type(x_data)) # print(y_data) 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() # tf 马上就要废弃这种写法 init = tf.global_variables_initializer() # 替换成这样就好 sess = tf.Session() sess.run(init) #for i in range(0): # training print(sess.run(loss, feed_dict={xs: x_data, ys: y_data})) sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) # to see the step improvement print(sess.run(loss, feed_dict={xs: x_data, ys: y_data})) sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) # to see the step improvement print(sess.run(loss, feed_dict={xs: x_data, ys: y_data})) sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) # to see the step improvement print(sess.run(loss, feed_dict={xs: x_data, ys: y_data})) import matplotlib.pyplot as plt # plot the real data fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.scatter(x_data, y_data) plt.ion()#本次运行请注释，全局运行不要注释 plt.show() for i in range(1000): # training sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) if i % 50 == 0: # to visualize the result and improvement try: ax.lines.remove(lines[0]) except Exception: pass prediction_value = sess.run(prediction, feed_dict={xs: x_data}) # plot the prediction lines = ax.plot(x_data, prediction_value, 'r-', lw=5) plt.pause(0.1)