撒花！《神经网络与深度学习》中文教程正式开源！全书 pdf、ppt 和代码一同放出

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 = 50
DOWNLOAD_MNIST = False
if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
# not mnist dir or mnist is empyt dir
DOWNLOAD_MNIST = True
train_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 function
nn.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 one
self.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 output
def test(cnn):
global prediction
y_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 / 500.0
def 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 prediction
y_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 result
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):
# 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.conv2d
return
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_pool
return
# 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]))