fashion-mnist

import tensorflow as tffrom tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_setsimport tensorflow.examples.tutorials.mnist.input_data as input_datafashion=input_data.read_data_sets("/home/henson/fashion/data/fashion",one_hot=True)sess = tf.InteractiveSession()x = tf.placeholder("float", shape=[None, 784])y_ = tf.placeholder("float", shape=[None, 10])w=tf.Variable(tf.zeros([784,10]))b=tf.Variable(tf.zeros([10]))y=tf.nn.softmax(tf.matmul(x,w)*b)#权重初始化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)#卷积层和池化层的定义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')#卷积和池化:第一层W_conv1=weight_Variable([5,5,1,32])  #前两个维度是patch的大小，接着是输入的通道数目，最后是输出的通道数目,每一个输出通道都有一个对应的偏置量b_conv1=bias_Variable([32])x_image=tf.reshape(x,[-1,28,28,1]) #[-1,宽，高，颜色通道数] 作为卷积层的输入h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)h_pool1=max_pool_2x2(h_conv1)#卷积和池化:第二层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)#第三层W_conv3=weight_Variable([5,5,64,128])b_conv3=bias_Variable([128])h_conv3=tf.nn.relu(conv2d(h_pool2,W_conv3)+b_conv3)h_pool3=max_pool_2x2(h_conv3)#第四层W_conv4=weight_Variable([5,5,128,256])b_conv4=bias_Variable([256])h_conv4=tf.nn.relu(conv2d(h_pool3,W_conv4)+b_conv4)h_pool4=max_pool_2x2(h_conv4)#全连接层W_fc1=weight_Variable([20*10*256,1024])  #1024个神经元的全连接层,why 10247?b_fc1=bias_Variable([1024])h_pool2_flat=tf.reshape(h_pool4,[-1,20*10*256])h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)#Dropoutkeep_prob=tf.placeholder("float")#用一个placeholder来代表一个神经元的输出在dropout中保持不变的概率h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)#输出层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)#训练和评估模型#tf.argmax 是一个非常有用的函数，它能给出某个tensor对象在某一维上的其数据最大值所在的索引值,# 由于标签向量是由0,1组成，因此最大值1所在的索引位置就是类别标签比如tf.argmax(y,1)返回的是模型对于任一输入x预>测到的标签值，# 而 tf.argmax(y_,1) 代表正确的标签，我们可以用 tf.equal 来检测我们的预测是否真实标签匹配(索引位置一样表示匹配)。cross_entropy=-tf.reduce_sum(y_*tf.log(y_conv))print("cross_entropy shape:", cross_entropy)train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_predicton=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))# tf.equal 来检测我们的预测是否真实标签匹配(索>引位置一样表示匹配)print("correct_predicton shape:", correct_predicton)accuracy=tf.reduce_mean(tf.cast(correct_predicton,"float"))#    将correct_predicton转换为float型print("accuracy shape:", accuracy)sess.run(tf.global_variables_initializer())for i in  range(20000):    batch=fashion.train.next_batch(50)# #按批次训练，每批50行数据    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})

拿四层卷积来训练，随便调了参数，出来的准确率低到不行，依然不知道该如何选择网络模型，以及参数如何设置，不过有点意思了。明天再看看，怎么提高