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import os
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import torch.nn.functional as F
import numpy as np
learning_rate = 1e-4
keep_prob_rate = 0.7 #
max_epoch = 3
BATCH_SIZE = 50DOWNLOAD_MNIST = False
if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):# not mnist dir or mnist is empyt dirDOWNLOAD_MNIST = Truetrain_data = torchvision.datasets.MNIST(root='./mnist/',train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,)
train_loader = Data.DataLoader(dataset = train_data ,batch_size= BATCH_SIZE ,shuffle= True)test_data = torchvision.datasets.MNIST(root = './mnist/',train = False)
test_x = Variable(torch.unsqueeze(test_data.test_data,dim  = 1),volatile = True).type(torch.FloatTensor)[:500]/255.
test_y = test_data.test_labels[:500].numpy()class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d( # ???# patch 7 * 7 ; 1  in channels ; 32 out channels ; ; stride is 1# padding style is same(that means the convolution opration's input and output have the same size)in_channels=      ,  out_channels=     ,kernel_size=      ,stride=           ,padding=          ,),nn.ReLU(),        # activation functionnn.MaxPool2d(2),  # pooling operation)self.conv2 = nn.Sequential( # ???# line 1 : convolution function, patch 5*5 , 32 in channels ;64 out channels; padding style is same; stride is 1# line 2 : choosing your activation funciont# line 3 : pooling operation function.)self.out1 = nn.Linear( 7*7*64 , 1024 , bias= True)   # full connection layer oneself.dropout = nn.Dropout(keep_prob_rate)self.out2 = nn.Linear(1024,10,bias=True)def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = x.view(      )  # flatten the output of coonv2 to (batch_size ,32 * 7 * 7)    # ???out1 = self.out1(x)out1 = F.relu(out1)out1 = self.dropout(out1)out2 = self.out2(out1)output = F.softmax(out2)return outputdef test(cnn):global predictiony_pre = cnn(test_x)_,pre_index= torch.max(y_pre,1)pre_index= pre_index.view(-1)prediction = pre_index.data.numpy()correct  = np.sum(prediction == test_y)return correct / 500.0def train(cnn):optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate )loss_func = nn.CrossEntropyLoss()for epoch in range(max_epoch):for step, (x_, y_) in enumerate(train_loader):x ,y= Variable(x_),Variable(y_)output = cnn(x)  loss = loss_func(output,y)optimizer.zero_grad()loss.backward()optimizer.step()if step != 0 and step % 20 ==0:print("=" * 10,step,"="*5,"="*5, "test accuracy is ",test(cnn) ,"=" * 10 )if __name__ == '__main__':cnn = CNN()train(cnn)

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)learning_rate = 1e-4
keep_prob_rate = 0.7 #
max_epoch = 2000
def compute_accuracy(v_xs, v_ys):global predictiony_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})return resultdef 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):# 每一维度  滑动步长全部是 1， padding 方式 选择 same# 提示 使用函数  tf.nn.conv2dreturn def max_pool_2x2(x):# 滑动步长 是 2步; 池化窗口的尺度 高和宽度都是2; padding 方式 请选择 same# 提示 使用函数  tf.nn.max_poolreturn # define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784])/255.
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])#  卷积层 1
## conv1 layer ##W_conv1 =                       # patch 7x7, in size 1, out size 32
b_conv1 =
h_conv1 =                       # 卷积  自己选择 选择激活函数
h_pool1 =                       # 池化               # 卷积层 2
W_conv2 =                        # patch 5x5, in size 32, out size 64
b_conv2 =
h_conv2 =                        # 卷积  自己选择 选择激活函数
h_pool2 =                        # 池化#  全连接层 1
## fc1 layer ##
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)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)# 全连接层 2
## fc2 layer ##
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)# 交叉熵函数
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)with tf.Session() as sess:init = tf.global_variables_initializer()sess.run(init)for i in range(max_epoch):batch_xs, batch_ys = mnist.train.next_batch(100)sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob:keep_prob_rate})if i % 100 == 0:print(compute_accuracy(mnist.test.images[:1000], mnist.test.labels[:1000]))