MNIST手寫數字識別

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1. MNIST數據集

導入數據集：

import tensorflow.examples.tutorials.mnist.input_data as input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

數據集結構

圖片集為二維矩陣：一張圖片占一行，尺寸為28x28=784。
標簽集為二維矩陣：一個標簽占一行，one-hot：一個標簽用10維向量表示。
- mnist.train.images：訓練集：55000 張圖片。[55000,784]
- mnist.train.labels：訓練集標簽。[55000,10]
- mnist.validation.images：驗證集：5000 張圖片
- mnist.validation.labels：驗證集標簽
- mnist.test.images：測試集：10000 張 圖片
- mnist.test.labels：測試集標簽

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2. 一層神經網絡

import tensorflow.examples.tutorials.mnist.input_data as input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) import tensorflow as tf# creat placeholder x = tf.placeholder(tf.float32, [None, 784],name="x_input") y_ = tf.placeholder("float", [None,10])# creat variable W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10]))# define how to compute y = tf.nn.softmax(tf.matmul(x,W) + b) tf.add_to_collection('predict', y) #用于加載模型獲取要預測的網絡結構# define cost function cross_entropy = -tf.reduce_sum(y_*tf.log(y))# define optinizer train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)# define how to compute accuracy # tf.argmax給出tensor對象在某一維上的數據最大值所在的索引值。0：列最大；1：航最大 # tf.equal元素比較，相等為True,不等為False。 correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) #tf.cast數據類型轉換 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))#define initialize operation init = tf.global_variables_initializer() #creat a Session sess = tf.Session() total_step = 0 sess.run(init) for i in range(1000):total_step += 1batch_xs, batch_ys = mnist.train.next_batch(100)sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})if total_step % 100 == 0:# Calculate batch accuracyprint(sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys}))# Calculate batch lossprint(loss = sess.run(cross_entropy, feed_dict={x: batch_xs, y_: batch_ys})) # Calculate testset accuracy print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) sess.close()

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3. CNN

import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) import tensorflow as tfsess = tf.InteractiveSession()x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10])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')W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) 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)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)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)keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)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) 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(2000):batch = mnist.train.next_batch(50)if i%100 == 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"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

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