MNIST(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) # 定义神经网络模型 class SimpleNN(nn.Module): def __init__(self): super(SimpleNN, self).__init__() self.fc1 = nn.Linear(28 *...
test_data = torchvision.datasets.CIFAR10("./dataset",train=False, transform =torchvision.transforms.ToTensor()) test_loader = DataLoader(dataset=test_data, batch_size=4, shuffle=True, num_workers=0, drop_last=True) # 测试batch_size=4: 每次从测试数据集中取4个数据进行打包 ...
num_batches = int(data_loader.num_train_data // batch_size * num_epochs) for batch_index in range(num_batches): X, y = data_loader.get_batch(batch_size) with tf.GradientTape() as tape: y_pred = model(X)枣庄人流医院哪家好 http://mobile.0632-3679999.com/ loss = tf.keras.losses...
先进行预备工作,实现一个简单的MNISTLoader类来读取 MNIST 数据集数据。这里使用了tf.keras.datasets快速载入 MNIST 数据集。 AI检测代码解析 class MNISTLoader(): def __init__(self): mnist = tf.keras.datasets.mnist (self.train_data, self.train_label), (self.test_data, self.test_label) = mnist...
data_loader = MNISTLoader() optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate) num_batches =int(data_loader.num_train_data // batch_size * num_epochs)forbatch_indexinrange(num_batches): X, y = data_loader.get_batch(batch_size)withtf.GradientTape()astape: ...
data_loader=DataLoader(IMG_SIZE,BATCH_SIZE)plt.figure(figsize=(10,8))i=0forimg,labelindata_loader.get_random_raw_images(20):plt.subplot(4,5,i+1)plt.imshow(img)plt.title("{} - {}".format(data_loader.get_label_name(label),img.shape))plt.xticks([])plt.yticks([])i+=1plt.tight...
img = self.loader(path) if self.transform is not None: img = self.transform(img) return img, target def __len__(self): n, _ = self.df.shape return n # what transformations should be done with our images data_transforms = tv.transforms.Compose([ ...
train_dataset = datasets.MNIST(root='./data/', train=True, transform=transforms.ToTensor(), download=True) test_dataset = datasets.MNIST(root='./data/', train=False, transform=transforms.ToTensor()) # Data Loader (Input Pipeline) train_loader = torch.utils.data.DataLoader(dataset=train_datas...
使用torch.utils.data.DataLoader结合上面定义的Dataset子类来创建一个数据生成器 为了不占用过多内存,我们需要将图片的所有地址(并不是所有数字化图片)加载到内存中,需要多少图片数据的时候就从内存中解析多少图片地址,这样有效且合理地使用内存,也不会耽误时间。
data_loader = MNISTLoader() optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate) num_batches = int(data_loader.num_train_data // batch_size * num_epochs) forbatch_indexinrange(num_batches): X, y = data_loader.get_batch(batch_size) withtf.GradientTape()astape: y_pred = ...