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########MNIST手写识别########
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########反向传播的神经网络########
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#输入784，第一层30，第二层60，输出10的神经网络
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import tensorflow as tf
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import matplotlib.pyplot as plt
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import numpy as np
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import time
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def sigmaprime(x):
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"""
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用Sigmoid函数的导数更新权重
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:param x:
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:return: 更新后的权重
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"""
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return tf.multiply(tf.sigmoid(x), tf.subtract(tf.constant(1.0), tf.sigmoid(x)))
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#加载MNIST数据集
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#MNIST数据集包含70000（60000+10000）个样本，其中有60000个训练样本和10000个测试样本，每个样本的像素大小为28*28
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mnist = tf.keras.datasets.mnist
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(train_x,train_y),(test_x,test_y) = mnist.load_data()
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# 定义模型
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# 常数
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n_input = 784 # MNIST尺寸(28*28)
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n_classes = 10 # MNIST类别(0-9)
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# 超参数
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max_epochs = 10000 # 最大迭代数
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learning_rate = 0.5 # 学习率
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batch_size = 10 # 每批训练批量大小
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seed = 0 # 随机种子
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n_hidden = 30 # 隐藏层的神经元数
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# 占位符
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#x_in = tf.placeholder(tf.float32, [None, n_input])
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#y = tf.placeholder(tf.float32, [None, n_classes])
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# 创建模型
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def multilayer_perceptron(x, weights, biases):
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h_layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['h1']) # 隐藏层使用ReLU激活函数
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out_layer_1 = tf.sigmoid(h_layer_1)
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h_out = tf.matmul(out_layer_1, weights['out']) + biases['out'] # 输出层使用线性激活函数
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return tf.sigmoid(h_out), h_out, out_layer_1, h_layer_1
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weights = { # 权重
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'h1': tf.Variable(tf.random_normal([n_input, n_hidden], seed=seed)),
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'out': tf.Variable(tf.random_normal([n_hidden, n_classes], seed=seed))}
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biases = { # 偏置
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'h1': tf.Variable(tf.random_normal([1, n_hidden], seed=seed)),
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'out': tf.Variable(tf.random_normal([1, n_classes], seed=seed))}
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# 正向传播
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y_hat, h_2, o_1, h_1 = multilayer_perceptron(x_in, weights, biases)
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# 损失函数
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err = y - y_hat
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loss = tf.reduce_mean(tf.square(err, name='loss'))
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# 反向传播
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delta_2 = tf.multiply(err, sigmaprime(h_2))
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delta_w_2 = tf.matmul(tf.transpose(o_1), delta_2)
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wtd_error = tf.matmul(delta_2, tf.transpose(weights['out']))
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delta_1 = tf.multiply(wtd_error, sigmaprime(h_1))
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delta_w_1 = tf.matmul(tf.transpose(x_in), delta_1)
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eta = tf.constant(learning_rate)
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# 更新权重
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train = [
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tf.assign(weights['h1'], tf.add(weights['h1'], tf.multiply(eta, delta_w_1)))
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, tf.assign(biases['h1'], tf.add(biases['h1'], tf.multiply(eta, tf.reduce_mean(delta_1, axis=[0]))))
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, tf.assign(weights['out'], tf.add(weights['out'], tf.multiply(eta, delta_w_2)))
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, tf.assign(biases['out'], tf.add(biases['out'], tf.multiply(eta, tf.reduce_mean(delta_2, axis=[0]))))
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]
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# 定义精度
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acct_mat = tf.equal(tf.argmax(y_hat, 1), tf.argmax(y, 1))
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accuracy = tf.reduce_sum(tf.cast(acct_mat, tf.float32))
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# 训练
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init = tf.global_variables_initializer()
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with tf.Session() as sess:
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sess.run(init)
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for epoch in range(max_epochs):
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batch_xs, batch_ys = mnist.train.next_batch(batch_size)
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_, loss1 = sess.run([train, loss], feed_dict={x_in: batch_xs, y: batch_ys})
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if epoch % 1000 == 0:
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print('Epoch: {0} loss: {1}'.format(epoch, loss1))
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acc_test = sess.run(accuracy, feed_dict={x_in: mnist.test.images, y: mnist.test.labels})
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acc_train = sess.run(accuracy, feed_dict={x_in: mnist.train.images, y: mnist.train.labels})
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# 评估
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print('Accuracy Train%: {1} Accuracy Test%: {2}'
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.format(epoch, acc_train / 600, (acc_test / 100)))
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print('--------------')
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#结果可视化
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plt.figure(figsize=(10,3))
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#使用模型
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plt.figure()
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for i in range(10):
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num = np.random.randint(1,10000) #在MNIST数据集中随机一个数据
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plt.subplot(2,5,i+1) #按2行5列的格式
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plt.axis('off') #关闭坐标轴
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plt.imshow(test_x[num],cmap='gray')
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demo = tf.reshape(X_test[num],(1,28,28))
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y_pred = np.argmax(model.predict(demo))
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plt.title('标签值：'+str(test_y[num])+'\n预测值：'+str(y_pred)) #显示标签值和电脑预测值
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plt.show() |
