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DeepLearning入门笔记（二），用TensorFlow实现简单的CNN跑MNIST数据集

本文代码根据TensorFlow的官方教程实现，有一些小的修改并加入了注释，在MNIST数据集上可以达到99.1%的准确率
https://www.tensorflow.org/versions/r0.11/tutorials/mnist/pros/index.html
在此之前，建议先学习Google在Udacity上开设的Deep Learning课程，非常简短，但介绍了许多重要的概念
然后可以直接观看CS231n Lecture 7的视频，对CNN讲解得很清楚
https://www.youtube.com/watch?v=LxfUGhug-iQ&index=7&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC
【DeepLearning入门笔记（二），用TensorFlow实现简单的CNN跑MNIST数据集】如果不考虑CNN的反向传播算法，只学习如何搭建网络，以上材料已经足够

import argparse# Import data from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfFLAGS = Nonedef 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')def add_layer(inputs, in_size, out_size, activation_function=None): # add a fully collected layer Weights = weight_variable([in_size, out_size]) biases = bias_variable([out_size]) Wx_plus_b = tf.matmul(inputs, Weights) + biases if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputsdef main(_): mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)# reshape the input to have batch size, width, height, channel size x = tf.placeholder(tf.float32, [None, 784]) x_image = tf.reshape(x, [-1, 28, 28, 1])# 5*5 patch size, input channel is 1, output channel is 32 W_conv1 = weight_variable([5, 5, 1, 32])# bias, same size with the output channel b_conv1 = bias_variable([32])# the first convolutional layer with a max pooling layer h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1)#after pooling, we have a tensor with shape[-1, 14, 14, 32]# the weights and bias for the second layer, we will get 64 channels W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64])# the second convolutional layer with a max pooling layer h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2)# after pooling, we have a tensor with shape[-1, 7, 7, 64]# add a fully connected layer with 1024 neurons and use relu as the activation function h_pool2_flat = tf.reshape(h_pool2, [-1,7*7*64]) h_fc1 = add_layer(h_pool2_flat, 7*7*64, 1024, tf.nn.relu)# we add dropout for the fully connected layer to avoid overfitting keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)# finally, the output layer y_conv = add_layer(h_fc1_drop, 1024, 10, None)# loss function and so on y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_)) 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, tf.float32))# start training, and we test our model every 100 steps sess = tf.InteractiveSession() sess.run(tf.initialize_all_variables()) for i in range(10000): batch = mnist.train.next_batch(100) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}) print("step %d, training accuracy %g, test accuracy %g" % (i, train_accuracy, test_accuracy))train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})if __name__ == '__main__': parser = argparse.ArgumentParser()# modify the dir path to your own dataset parser.add_argument('--data_dir', type=str, default='/tmp/mnist', help='Directory for storing data') FLAGS = parser.parse_args() tf.app.run()

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