@H_489_3@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)

@H_489_3@import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers, regularizers import numpy as np import os import cv2 import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "1"

@H_489_3@resize = 240 path ="D:/JupyterLab-Portable-3.1.0-3.9/notebooks/AI/Train" def load_data(): imgs = os.listdir(path) num = len(imgs) Train_data = np.empty((5000, resize, resize, 3), dtype="int32") Train_label = np.empty((5000, ), dtype="int32") test_data = np.empty((5000, resize, resize, 3), dtype="int32") test_label = np.empty((5000, ), dtype="int32") for i in range(5000): if i % 2: Train_data[i] = cv2.resize(cv2.imread(path+'/'+ 'dog.' + str(i) + '.jpg'), (resize, resizE)) Train_label[i] = 1 else: Train_data[i] = cv2.resize(cv2.imread(path+'/' + 'cat.' + str(i) + '.jpg'), (resize, resizE)) Train_label[i] = 0 for i in range(5000, 10000): if i % 2: test_data[i-5000] = cv2.resize(cv2.imread(path+'/' + 'dog.' + str(i) + '.jpg'), (resize, resizE)) test_label[i-5000] = 1 else: test_data[i-5000] = cv2.resize(cv2.imread(path+'/' + 'cat.' + str(i) + '.jpg'), (resize, resizE)) test_label[i-5000] = 0 return Train_data, Train_label, test_data, test_label

@H_489_3@def vgg16(): weight_decay = 0.0005 nb_epoch = 100 batch_size = 32 # layer1 model = keras.Sequential() model.add(layers.Conv2D(64, (3, 3), padding='same', input_shape=(224, 224, 3), kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.3)) # layer2 model.add(layers.Conv2D(64, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer3 model.add(layers.Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.4)) # layer4 model.add(layers.Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer5 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.4)) # layer6 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.4)) # layer7 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer8 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.4)) # layer9 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.4)) # layer10 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) # layer11 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.4)) # layer12 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.Dropout(0.4)) # layer13 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2, 2))) model.add(layers.Dropout(0.5)) # layer14 model.add(layers.Flatten()) model.add(layers.Dense(512, kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) # layer15 model.add(layers.Dense(512, kernel_regularizer=regularizers.l2(weight_decay))) model.add(layers.Activation('relu')) model.add(layers.batchNormalization()) # layer16 model.add(layers.Dropout(0.5)) model.add(layers.Dense(2)) model.add(layers.Activation('softmax')) return model

@H_489_3@#if __name__ == '__main__': Train_data, Train_label, test_data, test_label = load_data() print(Train_data) Train_data = Train_data.astype('float32') test_data = test_data.astype('float32') Train_label = keraS.Utils.to_categorical(Train_label, 2) test_label = keraS.Utils.to_categorical(test_label, 2)

@H_489_3@#定义训练方法,超参数设置 model = vgg16() sgd = tf.keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=TruE) #设置优化器为 SGD model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) history = model.fit(Train_data, Train_label, batch_size=20, epochs=10, validation_split=0.2, shuffle=TruE) scores = model.evaluate(test_data,test_label,verbose=1) print(scores) model.save('D:JupyterLab-Portable-3.1.0-3.9notebooksAITrainmodelvgg16dogcat.h5')

@H_489_3@acc = history.historY['accuracy'] # 获取训练集准确性数据 val_acc = history.historY['val_accuracy'] # 获取验证集准确性数据 loss = history.historY['loss'] # 获取训练集错误值数据 val_loss = history.historY['val_loss'] # 获取验证集错误值数据 epochs = range(1, len(acC) + 1) plt.plot(epochs, acc, 'bo', label='Trainning acc') # 以epochs为横坐标，以训练集准确性为纵坐标 plt.plot(epochs, val_acc, 'b', label='Vaildation acc') # 以epochs为横坐标，以验证集准确性为纵坐标 plt.legend() # 绘制图例，即标明图中的线段代表何种含义 plt.show()