import tensorflow as tf# 輸入數據 from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("E:\\MNIST_data", one_hot=True)# 定義網絡的超參數 learning_rate = 0.001 training_iters = 200000 batch_size = 128 display_step = 5# 定義網絡的參數 # 輸入的維度 (img shape: 28*28) n_input = 784 # 標記的維度 (0-9 digits) n_classes = 10 # Dropout的概率，輸出的可能性 dropout = 0.75 # 輸入占位符 x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_classes]) #dropout (keep probability) keep_prob = tf.placeholder(tf.float32) # 定義卷積操作 def conv2d(name,x, W, b, strides=1):# Conv2D wrapper, with bias and relu activationx = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')x = tf.nn.bias_add(x, b)# 使用relu激活函數return tf.nn.relu(x,name=name) # 定義池化層操作 def maxpool2d(name,x, k=2):# MaxPool2D wrapperreturn tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME',name=name)# 規范化操作 def norm(name, l_input, lsize=4):return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0,beta=0.75, name=name)# 定義所有的網絡參數 weights = {'wc1': tf.Variable(tf.random_normal([11, 11, 1, 96])),'wc2': tf.Variable(tf.random_normal([5, 5, 96, 256])),'wc3': tf.Variable(tf.random_normal([3, 3, 256, 384])),'wc4': tf.Variable(tf.random_normal([3, 3, 384, 384])),'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])),'wd1': tf.Variable(tf.random_normal([4*4*256, 4096])),'wd2': tf.Variable(tf.random_normal([4096, 1024])),'out': tf.Variable(tf.random_normal([1024, n_classes])) } biases = {'bc1': tf.Variable(tf.random_normal([96])),'bc2': tf.Variable(tf.random_normal([256])),'bc3': tf.Variable(tf.random_normal([384])),'bc4': tf.Variable(tf.random_normal([384])),'bc5': tf.Variable(tf.random_normal([256])),'bd1': tf.Variable(tf.random_normal([4096])),'bd2': tf.Variable(tf.random_normal([1024])),'out': tf.Variable(tf.random_normal([n_classes])) }# 定義整個網絡 def alex_net(x, weights, biases, dropout):# 向量轉為矩陣 Reshape input picturex = tf.reshape(x, shape=[-1, 28, 28, 1])# 第一層卷積# 卷積conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1'])# 下采樣pool1 = maxpool2d('pool1', conv1, k=2)# 規范化norm1 = norm('norm1', pool1, lsize=4)# 第二層卷積# 卷積conv2 = conv2d('conv2', norm1, weights['wc2'], biases['bc2'])# 最大池化（向下采樣）pool2 = maxpool2d('pool2', conv2, k=2)# 規范化norm2 = norm('norm2', pool2, lsize=4)# 第三層卷積# 卷積conv3 = conv2d('conv3', norm2, weights['wc3'], biases['bc3'])# 規范化norm3 = norm('norm3', conv3, lsize=4)# 第四層卷積conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4'])# 第五層卷積conv5 = conv2d('conv5', conv4, weights['wc5'], biases['bc5'])# 最大池化（向下采樣）pool5 = maxpool2d('pool5', conv5, k=2)# 規范化norm5 = norm('norm5', pool5, lsize=4)# 全連接層1fc1 = tf.reshape(norm5, [-1, weights['wd1'].get_shape().as_list()[0]])fc1 =tf.add(tf.matmul(fc1, weights['wd1']),biases['bd1'])fc1 = tf.nn.relu(fc1)# dropoutfc1=tf.nn.dropout(fc1,dropout)# 全連接層2fc2 = tf.reshape(fc1, [-1, weights['wd2'].get_shape().as_list()[0]])fc2 =tf.add(tf.matmul(fc2, weights['wd2']),biases['bd2'])fc2 = tf.nn.relu(fc2)# dropoutfc2=tf.nn.dropout(fc2,dropout)# 輸出層out = tf.add(tf.matmul(fc2, weights['out']) ,biases['out'])return out# 構建模型 pred = alex_net(x, weights, biases, keep_prob)# 定義損失函數和優化器 cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# 評估函數 correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# 初始化變量 init = tf.global_variables_initializer()# 開啟一個訓練 with tf.Session() as sess:sess.run(init)step = 1# 開始訓練，直到達到training_iters，即200000while step * batch_size < training_iters:#獲取批量數據batch_x, batch_y = mnist.train.next_batch(batch_size)sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})if step % display_step == 0:# 計算損失值和準確度，輸出loss,acc = sess.run([cost,accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))step += 1print ("Optimization Finished!")# 計算測試集的精確度print ("Testing Accuracy:",sess.run(accuracy, feed_dict={x: mnist.test.images[:256],y: mnist.test.labels[:256],keep_prob: 1.}))

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