Tensorflow项目实战一:MNIST手写数字识别
此模型中,输入是28*28*1的图片,经过两个卷积层(卷积+池化)层之后,尺寸变为7*7*64,将最后一个卷积层展成一个以为向量,然后接两个全连接层,第一个全连接层加一个dropout,最后一个全连接层输出10个分类的预测结果,然后计算损失,进行训练。
代码如下:
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #定义一个获取卷积核的函数 def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) #定义一个获取偏置值的函数 def bias_variable(shape): initial = tf.constant(0.1,shape=shape) return tf.Variable(initial) #定义一个卷积函数 def conv2d(x,W): return tf.nn.conv2d(x,W,[1,1,1,1],padding="SAME") #定义一个池化函数 def max_pool_2x2(x): return tf.nn.max_pool(x,ksize=[1,2,2,1], strides=[1,2,2,1],padding="VALID") if __name__ == "__main__": mnist = input_data.read_data_sets("MNIST_data/",one_hot=True) x = tf.placeholder(shape=[None,28*28],dtype=tf.float32) lable = tf.placeholder(shape=[None,10],dtype=tf.float32) x_image = tf.reshape(x,[-1,28,28,1]) #第一个卷积层 W_conv1 = weight_variable([5,5,1,32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1) h_pool1 = max_pool_2x2(h_conv1) #14*14*32 #第二个卷积层 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) #7*7*64 #全连接层,输出为1024维向量 W_fc1 = weight_variable([7*7*64,1024]) b_fc1 = weight_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(tf.float32) h_fc1_dropout = tf.nn.dropout(h_fc1,keep_prob=keep_prob) #把1024维向量转换成10维,对应10个类别 W_fc2 = weight_variable([1024,10]) b_fc2 = weight_variable([10]) y_conv = tf.matmul(h_fc1,W_fc2)+b_fc2 #直接使用tf.nn.softmax_cross_entropy_with_logits直接计算交叉熵 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=lable,logits=y_conv)) #定义train_step train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #定义测试的准确率 correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(lable,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 创建Session和变量初始化 sess = tf.InteractiveSession() sess.run(tf.global_variables_initializer()) #训练20000步 for i in range(20000): batch = mnist.train.next_batch(50) if i % 100==0: train_accuracy = sess.run(accuracy,feed_dict={ x:batch[0],lable:batch[1],keep_prob: 1.0}) print("step %d, training accuracy %g" % (i, train_accuracy)) _ = sess.run(train_step, feed_dict={x: batch[0], lable: batch[1], keep_prob: 0.5}) print("test accuracy %g" % sess.run(accuracy, feed_dict={ x: mnist.test.images, lable: mnist.test.labels, keep_prob: 1.0}))
手与大脑的距离决定了理想与现实的相似度