torch& tensorflow

#torch
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 .weigh

input = torch.randn(1, 1, 32, 32)

out = net(input)

print(out)

vgg

#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])

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)

巴特西

torch& tensorflow

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