#从keras.model中导入model模块，为函数api搭建网络做准备
from tensorflow.keras import Model
from tensorflow.keras.layers import Flatten,Dense,Dropout,MaxPooling2D,Conv2D,BatchNormalization,Input,ZeroPadding2D,Concatenate
from tensorflow.keras import *
from tensorflow.keras import regularizers #正则化
from tensorflow.keras.optimizers import RMSprop #优化选择器
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.python.keras.utils import np_utils
#数据处理
(X_train,Y_train),(X_test,Y_test)=mnist.load_data()
X_test1=X_test
Y_test1=Y_test
X_train=X_train.reshape(-1,28,28,1).astype("float32")/255.0
X_test=X_test.reshape(-1,28,28,1).astype("float32")/255.0
Y_train=np_utils.to_categorical(Y_train,10)
Y_test=np_utils.to_categorical(Y_test,10)
print(X_train.shape)
print(Y_train.shape)
print(X_train.shape)
![]()
def vgg16():
x_input = Input((28, 28, 1)) # 输入数据形状28*28*1
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(x_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
#BLOCK 6
x=Flatten()(x)
x=Dense(256,activation="relu")(x)
x=Dropout(0.5)(x)
x = Dense(256, activation="relu")(x)
x = Dropout(0.5)(x)
#搭建最后一层，即输出层
x = Dense(10, activation="softmax")(x)
# 调用MDOEL函数，定义该网络模型的输入层为X_input,输出层为x.即全连接层
model = Model(inputs=x_input, outputs=x)
# 查看网络模型的摘要
model.summary()
return model
model=vgg16()
optimizer=RMSprop(lr=1e-4)
model.compile(loss="binary_crossentropy",optimizer=optimizer,metrics=["accuracy"])
#训练加评估模型
n_epoch=4
batch_size=128
def run_model(): #训练模型
training=model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=n_epoch,
validation_split=0.25,
verbose=1
)
test=model.evaluate(X_train,Y_train,verbose=1)
return training,test
training,test=run_model()
print("误差：",test[0])
print("准确率：",test[1])model=vgg16()
optimizer=RMSprop(lr=1e-4)
model.compile(loss="binary_crossentropy",optimizer=optimizer,metrics=["accuracy"])
#训练加评估模型
n_epoch=4
batch_size=128
def run_model(): #训练模型
training=model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=n_epoch,
validation_split=0.25,
verbose=1
)
test=model.evaluate(X_train,Y_train,verbose=1)
return training,test
training,test=run_model()
print("误差：",test[0])
print("准确率：",test[1])model=vgg16()
optimizer=RMSprop(lr=1e-4)
model.compile(loss="binary_crossentropy",optimizer=optimizer,metrics=["accuracy"])
#训练加评估模型
n_epoch=4
batch_size=128
def run_model(): #训练模型
training=model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=n_epoch,
validation_split=0.25,
verbose=1
)
test=model.evaluate(X_train,Y_train,verbose=1)
return training,test
training,test=run_model()
print("误差：",test[0])
print("准确率：",test[1])
![]()
def show_train(training_history,train, validation):
plt.plot(training.history[train],linestyle="-",color="b")
plt.plot(training.history[validation] ,linestyle="--",color="r")
plt.title("training history")
plt.xlabel("epoch")
plt.ylabel("accuracy")
plt.legend(["training","validation"],loc="lower right")
plt.show()
show_train(training,"accuracy","val_accuracy")
![]()
def show_train1(training_history,train, validation):
plt.plot(training.history[train],linestyle="-",color="b")
plt.plot(training.history[validation] ,linestyle="--",color="r")
plt.title("training history")
plt.xlabel("epoch")
plt.ylabel("loss")
plt.legend(["training","validation"],loc="upper right")
plt.show()
show_train1(training,"loss","val_loss")
![]()
prediction=model.predict(X_test)
def image_show(image):
fig=plt.gcf() #获取当前图像
fig.set_size_inches(2,2) #改变图像大小
plt.imshow(image,cmap="binary") #显示图像
plt.show()
def result(i):
image_show(X_test1[i])
print("真实值：",Y_test1[i])
print("预测值：",np.argmax(prediction[i]))
result(0)
result(1)
![]()
- PyTorch神经网络以及图像分类器
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 5x5 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
# If the size is a square you can only specify a single number
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
net = Net()
print(net)
![]()
params = list(net.parameters())
print(len(params))
print(params[0].size()) # conv1's .weight
![]()
input = torch.randn(1, 1, 32, 32)
out = net(input)
print(out)
![]()
net.zero_grad()
out.backward(torch.randn(1, 10))
output = net(input)
target = torch.randn(10) # a dummy target, for example
target = target.view(1, -1) # make it the same shape as output
criterion = nn.MSELoss()
loss = criterion(output, target)
print(loss)
![]()
print(loss.grad_fn) # MSELoss
print(loss.grad_fn.next_functions[0][0]) # Linear
print(loss.grad_fn.next_functions[0][0].next_functions[0][0]) # ReLU
![]()
net.zero_grad() # zeroes the gradient buffers of all parameters
print('conv1.bias.grad before backward')
print(net.conv1.bias.grad)
loss.backward()
print('conv1.bias.grad after backward')
print(net.conv1.bias.grad)
![]()
learning_rate = 0.01
for f in net.parameters():
f.data.sub_(f.grad.data * learning_rate)
import torch.optim as optim
# create your optimizer
optimizer = optim.SGD(net.parameters(), lr=0.01)
# in your training loop:
optimizer.zero_grad() # zero the gradient buffers
output = net(input)
loss = criterion(output, target)
loss.backward()
optimizer.step() # Does the update
- PyTorch图像分类器
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
![]()
import matplotlib.pyplot as plt
import numpy as np
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
![]()
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
![]()
outputs = net(images)
_, predicted = torch.max(outputs, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
for j in range(4)))
![]()
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
![]()
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))
![]()
浙公网安备 33010602011771号