tensorflow56 《TensorFlow技术解析与实战》06 神经网络的发展及其Tensorflow实现

# 《TensorFlow技术解析与实战》06 神经网络的发展及其TensorFlow实现# win10 Tensorflow1.2.0-RC0 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:nntf06.01.py mnist的AlexNet实现# 参考:# https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py# https://github.com/tensorflow/models/blob/master/tutorials/image/alexnet/alexnet_benchmark.pyimport tensorflow as tf# 输入数据from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# 定义网络的超参数learing_rate = 0.001training_iters = 200000batch_size = 128displayer_step = 10# 定义网络的参数n_input = 784    # 输入维度(img shape: 28x28)n_classes = 10   # 标记维度(0-9 digits)dropout = 0.75   # Dropout概率，输出的可能性# 输入占位符x = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_classes])keep_prob = tf.placeholder(tf.float32) # dropout# 定义卷积操作def conv2d(name, x, W, b, strides=1):    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')    x = tf.nn.bias_add(x, b)    return tf.nn.relu(x, name=name)  # 使用relu激活函数# 定义池化操作def maxpool2d(name, x, k=2):    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)# 规范化操作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)# 定义所有网络参数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, 4096])),    'out': tf.Variable(tf.random_normal([4096, 10]))}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([4096])),    'out': tf.Variable(tf.random_normal([n_classes]))}# 定义网络def alex_net(x, weights, biases, dropout):    x = tf.reshape(x, shape=[-1, 28, 28, 1])    conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1'])    pool1 = maxpool2d('pool1', conv1, k=2)    norm1 = norm('norm1', pool1, lsize=4)    conv2 = conv2d('conv2', conv1, weights['wc2'], biases['bc2'])    pool2 = maxpool2d('pool2', conv2, k = 2)    norm2 = norm('norm2', pool2, lsize=4)    conv3 = conv2d('conv3', norm2, weights['wc3'], biases['bc3'])    pool3 = maxpool2d('pool3', conv3, k = 2)    norm3 = norm('norm3', pool3, lsize=4)    conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4'])    conv5 = conv2d('conv5', norm3, weights['wc5'], biases['bc5'])    pool5 = maxpool2d('pool5', conv5, k = 2)    norm5 = norm('norm5', pool5, lsize=4)    fc1 = 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)    fc1 = tf.nn.dropout(fc1, dropout)    fc2 = 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)    fc2 = tf.nn.dropout(fc2, dropout)    out = tf.add(tf.matmul(fc2, weights['out']), biases['out'])    return out#构建预测模型predict_model = alex_net(x, weights, biases, keep_prob)# 定义损失函数和优化器cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=predict_model))optimizer = tf.train.AdamOptimizer(learning_rate=learing_rate).minimize(cost)# 评估函数correct_pred = tf.equal(tf.argmax(predict_model, 1), tf.argmax(y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# 训练模型和评估模型# 初始化变量init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    step = 1    # 开始训练    while step*batch_size < training_iters:        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 % displayer_step == 0:            # 计算损失值和准确度，输出            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 += 1    print("Optimizer Finished!")    # 计算测试集的准确度    print("Testing Accuracy: ", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))'''Iter 1280, Minibatch Loss= 460011.468750, Training Accuracy= 0.36719Iter 2560, Minibatch Loss= 303076.562500, Training Accuracy= 0.62500...Iter 198400, Minibatch Loss= 4899.899414, Training Accuracy= 0.97656Iter 199680, Minibatch Loss= 447.203613, Training Accuracy= 0.99219Optimizer Finished!Testing Accuracy:  0.992188'''