实现卷积神经网络：吴恩达Course 4-卷积神经网络-week1作业 pytorch版

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注意事项

和老师用tensorflow写的结果不同，不过测试集准度更高了，以下几点原因：
1.没用Xaiver初始化，使用了pytorch默认的初始化方式
2.pytorch和tensorflow的padding机制不同，没有特意去还原tensorflow的方式，pytorch的padding设置值不可以比卷积核的宽度一半大，所以当步长大，用默认方式“SAME”不了
3.全连接层的线性函数输出神经元老师用的是6个，这里用了20个（因为第2点，加上ReLU，用6个神经元会梯度消失，100步以内不收敛）

吐槽一句pytorch的padding方式不太友好，要自己算，tensorflow直接padding="SAME"就行了

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资料下载

相关文件可在【tensorflow版大佬】下载

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备注

week1作业有两个：
第一部分：用numpy还原卷积神经网络的实现过程
第二部分：用框架实现卷积神经网络

这里只包含第二部分的内容

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载入库

import torch
from torch.utils.data import DataLoader, TensorDataset
from torch import nn
import numpy as np
from matplotlib import pyplot as plt
from cnn_utils import load_dataset

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数据预处理

# 设置随机种子
torch.manual_seed(1)# 载入数据
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()# 可视化一个样本
index = 6
plt.imshow(X_train_orig[index])
print('y=' + str(np.squeeze(Y_train_orig[:, index])))
plt.show()# 归一化数据集
X_train = np.transpose(X_train_orig, (0, 3, 1, 2))/255          # 将维度转为(1080, 3, 64, 64)
X_test = np.transpose(X_test_orig, (0, 3, 1, 2))/255            # 将维度转为(120, 3, 64, 64)
# 转置y
Y_train = Y_train_orig.T                    # (1080, 1)
Y_test = Y_test_orig.T                      # (120, 1)print('number of training examples = ' + str(X_train.shape[0]))
print('number of test examples = ' + str(X_test.shape[0]))
print('X_train shape: ' + str(X_train.shape))
print('Y_train shape: ' + str(Y_train.shape))
print('X_test shape: ' + str(X_test.shape))
print('Y_test shape: ' + str(Y_test.shape))

可视化一个样本：

打印维度：

number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 3, 64, 64)
Y_train shape: (1080, 1)
X_test shape: (120, 3, 64, 64)
Y_test shape: (120, 1)

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建模部分

# 创建数据接口
def data_loader(X_train, Y_train, batch_size=64):train_db = TensorDataset(torch.from_numpy(X_train).float(), torch.squeeze(torch.from_numpy(Y_train)))train_loader = DataLoader(train_db, batch_size=batch_size, shuffle=True)return train_loader# 构建模型
class CNN(nn.Module):def __init__(self):# 继承模块super(CNN, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(                              # input shape (3, 64, 64)in_channels=3,                      # input通道数out_channels=8,                     # output通道数kernel_size=4,                      # 卷积核的边长fstride=1,                           # 步长padding=1                           # padding模式为SAME，=[(s-1)n-s+f]/2，这里算出来不是整数，向下取整了),nn.ReLU(),nn.MaxPool2d(kernel_size=8, stride=8, padding=4))self.conv2 = nn.Sequential(                 # input shape (8, 64, 64)nn.Conv2d(8, 16, 2, 1, 1),nn.ReLU(),nn.MaxPool2d(kernel_size=4, stride=4, padding=2))self.fullconnect = nn.Sequential(nn.Linear(16 * 3 * 3, 20),nn.ReLU())self.classifier = nn.LogSoftmax(dim=1)def forward(self, x):x = self.conv1(x)x = self.conv2(x)# 展平x = x.view(x.size(0), -1)x = self.fullconnect(x)output = self.classifier(x)return outputdef weigth_init(m):if isinstance(m, nn.Conv2d):nn.init.xavier_uniform_(m.weight.data)nn.init.constant_(m.bias.data, 0)elif isinstance(m, nn.Linear):nn.init.xavier_uniform_(m.weight.data)nn.init.constant_(m.bias.data, 0)def model(X_train, Y_train, X_test, Y_test, learning_rate=0.009, num_epochs=100, minibatch_size=64, print_cost=True,is_plot=True):train_loader = data_loader(X_train, Y_train, minibatch_size)cnn = CNN()# cnn.apply(weigth_init)cost_func = nn.NLLLoss()optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate, betas=(0.9, 0.999))# 保存每次迭代的cost的列表costs = []# 批次数量m = X_train.shape[0]num_batch = m / minibatch_sizefor epoch in range(num_epochs):epoch_cost = 0for step, (batch_x, batch_y) in enumerate(train_loader):# 前向传播output = cnn(batch_x)# 计算成本cost = cost_func(output, batch_y)epoch_cost += cost.data.numpy() / num_batch# 梯度归零optimizer.zero_grad()# 反向传播cost.backward()# 更新参数optimizer.step()if print_cost and epoch % 5 == 0:costs.append(epoch_cost)print('Cost after epoch %i : %f' % (epoch, epoch_cost))# 画学习曲线if is_plot:plt.plot(costs)plt.xlabel('iterations per 5')plt.ylabel('cost')plt.show()# 保存学习后的参数torch.save(cnn.state_dict(), 'net_params.pkl')print('参数已保存到本地pkl文件。')# 预测训练集cnn.load_state_dict(torch.load('net_params.pkl'))output_train = cnn(torch.from_numpy(X_train).float())pred_Y_train = torch.max(output_train, dim=1)[1].data.numpy()# 预测测试集output_test = cnn(torch.from_numpy(X_test).float())pred_Y_test= torch.max(output_test, dim=1)[1].data.numpy()# 训练集准确率print('Train Accuracy: %.2f %%' % float(np.sum(np.squeeze(Y_train) == pred_Y_train)/m*100))# 测试集准确率print('Test Accuracy: %.2f %%' % float(np.sum(np.squeeze(Y_test) == pred_Y_test)/X_test.shape[0]*100))return cnnmodel(X_train, Y_train, X_test, Y_test)

迭代过程：

Cost after epoch 0 : 2.401703
Cost after epoch 5 : 1.341189
Cost after epoch 10 : 0.801924
Cost after epoch 15 : 0.567850
Cost after epoch 20 : 0.446336
Cost after epoch 25 : 0.342109
Cost after epoch 30 : 0.278837
Cost after epoch 35 : 0.182508
Cost after epoch 40 : 0.152718
Cost after epoch 45 : 0.124633
Cost after epoch 50 : 0.103368
Cost after epoch 55 : 0.099265
Cost after epoch 60 : 0.092497
Cost after epoch 65 : 0.067059
Cost after epoch 70 : 0.080446
Cost after epoch 75 : 0.101512
Cost after epoch 80 : 0.051409
Cost after epoch 85 : 0.021475
Cost after epoch 90 : 0.017657
Cost after epoch 95 : 0.010164

学习曲线：

训练准确率与测试准确率：

Train Accuracy: 100.00 %
Test Accuracy: 90.83 %

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到这就完成啦~

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