140 lines
4.4 KiB
Python
140 lines
4.4 KiB
Python
from pathlib import Path
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import sys
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import numpy
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import torch
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from torch.utils.data import DataLoader, Dataset
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import matplotlib.pyplot as plt
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import torch.nn.functional as F
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sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
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import gpu_utils
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class CNN(torch.nn.Module):
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"""卷积神经网络模型"""
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def __init__(self):
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super(CNN, self).__init__()
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# 使用Ceil模式设置MaxPooling，因为tensorflow默认是这个模式。
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self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=(3, 3))
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self.pool1 = torch.nn.MaxPool2d(kernel_size=(2, 2), ceil_mode=True)
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self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=(3, 3))
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self.pool2 = torch.nn.MaxPool2d(kernel_size=(2, 2), ceil_mode=True)
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self.conv3 = torch.nn.Conv2d(64, 64, kernel_size=(3, 3))
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self.flatten = torch.nn.Flatten()
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# 28x28过第一轮卷积后变为26x26，过第一轮池化后变为13x13
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# 过第二轮卷积后变为11x11，过第二轮池化后变为5x5
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# 过第三轮卷积后变为3x3。
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# 最后一轮卷积核个数为64。
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self.fc1 = torch.nn.Linear(64 * 3 * 3, 64)
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self.fc2 = torch.nn.Linear(64, 10)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = self.pool1(x)
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x = F.relu(self.conv2(x))
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x = self.pool2(x)
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x = F.relu(self.conv3(x))
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x = self.flatten(x)
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return F.softmax(x, dim=1)
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class MnistDataset(Dataset):
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"""用于加载Mnist的自定义数据集"""
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shape: int
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x_data: torch.Tensor
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y_data: torch.Tensor
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def __init__(self, x_data: torch.Tensor, y_data: torch.Tensor):
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x_len = x_data.shape[0]
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y_len = y_data.shape[0]
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assert (x_len == y_len)
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self.shape = x_len
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self.x_data = x_data
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self.y_data = y_data
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def __getitem__(self, index):
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return self.x_data[index], self.y_data[index]
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def __len__(self):
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return self.shape
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class DataSource:
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"""用于读取MNIST数据的数据读取器"""
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train_data: DataLoader
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test_data: DataLoader
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def __init__(self, batch_size: int):
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datasets_path = Path(
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__file__).resolve().parent.parent / 'datasets' / 'mnist.npz'
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datasets = numpy.load(datasets_path)
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# 6万张训练图片：60000x28x28。标签只有第一维。
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train_images = torch.from_numpy(datasets['x_train'])
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train_label = torch.from_numpy(datasets['y_train'])
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# 1万张测试图片：10000x28x28。标签只有第一维。
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test_images = torch.from_numpy(datasets['x_test'])
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test_label = torch.from_numpy(datasets['y_test'])
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# 为了符合后面图像的输入颜色通道条件，要在最后挤出一个新的维度
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train_images.unsqueeze(-1)
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test_images.unsqueeze(-1)
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# 像素值归一化
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train_images /= 255.0
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test_images /= 255.0
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# 创建数据集
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train_dataset = MnistDataset(train_images, train_label)
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test_dataset = MnistDataset(test_images, test_label)
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# 赋值到自身
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self.train_data = DataLoader(dataset=train_dataset,
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batch_size=batch_size,
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shuffle=True)
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self.test_data = DataLoader(dataset=test_dataset,
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batch_size=batch_size,
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shuffle=False)
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def main():
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n_epoch = 5
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n_batch_size = 25
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device = gpu_utils.get_gpu_device()
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data_source = DataSource(n_batch_size)
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cnn = CNN().to(device)
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optimizer = torch.optim.Adam(cnn.parameters())
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loss_func = torch.nn.CrossEntropyLoss()
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for epoch in range(n_epoch):
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cnn.train()
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batch_images: torch.Tensor
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batch_labels: torch.Tensor
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for batch_index, (batch_images, batch_labels) in enumerate(data_source.train_data):
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gpu_images = batch_images.to(device)
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gpu_labels = batch_labels.to(device)
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optimizer.zero_grad()
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prediction: torch.Tensor = cnn(gpu_images)
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loss: torch.Tensor = loss_func(prediction, gpu_labels)
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loss.backward()
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optimizer.step()
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loss_showcase = loss.item()
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print(f'Epoch: {epoch+1}, Batch: {batch_index}, Loss: {loss.item():.4f}')
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if __name__ == "__main__":
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gpu_utils.print_gpu_availability()
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main()
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