Mnist数据集获取
这里有input_data.py,但是我们下载不到，被墙了，所以从其他途径下好那四个压缩包，然后修改一下这里面的代码就可以像中文社区里的教程那样用
import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # local_file = maybe_download(TRAIN_IMAGES, train_dir) train_images = extract_images('MNIST/train-images-idx3-ubyte.gz') # local_file = maybe_download(TRAIN_LABELS, train_dir) train_labels = extract_labels('MNIST/train-labels-idx1-ubyte.gz', one_hot=one_hot) # local_file = maybe_download(TEST_IMAGES, train_dir) test_images = extract_images('MNIST/t10k-images-idx3-ubyte.gz') # local_file = maybe_download(TEST_LABELS, train_dir) test_labels = extract_labels('MNIST/t10k-labels-idx1-ubyte.gz', one_hot=one_hot)把这几行修改成不用 下载，并且直接解压已经下载好的数据集，路径改成自己下载的路径即可然后是运行一下多层神经网络的mnist代码
import tensorflow as tf import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #single softMax layer # x = tf.placeholder("float", [None, 784]) # W = tf.Variable(tf.zeros([784,10])) # b = tf.Variable(tf.zeros([10])) # y = tf.nn.softmax(tf.matmul(x,W) + b) # y_ = tf.placeholder("float", [None,10]) # cross_entropy = -tf.reduce_sum(y_*tf.log(y)) # train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) # init = tf.initialize_all_variables() # sess = tf.Session() # sess.run(init) # for i in range(1000): # batch_xs, batch_ys = mnist.train.next_batch(100) # sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) # accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}) sess = tf.InteractiveSession() def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #the first layer 可以理解卷积为生成了32副新的图像，现在数量是28*28*32 W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x = tf.placeholder("float", [None, 784]) x_image = tf.reshape(x, [-1,28,28,1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) #一次池化，现在数量为14*14*32 #the second layer 现在对上面的32副图像每一副再生成64张图像，现在规模14*14*64，注意每一个卷积核处理32个通道，然后对32个通道进行累加之后再取激活函数值得到一个通道 W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) #再次池化，规模为7*7*64 # all-connection layer #对64个通道副图像做全连接，输出1024个激励值 W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) #dropout keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #the output layer #对1024的向量再变成进行10维，代表十个数字 W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) y_ = tf.placeholder("float", [None,10]) cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) sess.run(tf.initialize_all_variables()) for i in range(20000): batch = mnist.train.next_batch(50) if i0 == 0: train_accuracy = accuracy.eval(feed_dict={ x:batch[0], y_: batch[1], keep_prob: 1.0}) print "step %d, training accuracy %g"%(i, train_accuracy) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) print "test accuracy %g"