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 = ...
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...
batch_size :批次的大小; validation_data :验证数据,可用于在训练过程中监控模型的性能。 model.fit(data_loader.train_data,#训练集数据data_loader.train_label,#训练集标签epochs=num_epochs,#训练轮次batch_size=batch_size#批量大小) 2.2.4 模型评估与测试 evaluate:该函数将对所有输入和输出对预测,并且收集...
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。 class MNISTLoader(): def __init__(self): mnist = tf.keras.datasets.mnist (self.train_data, self.train_label), (self.test_data, self.test_label) = mnist.load_data() #...
model = MLP() data_loader = MNISTLoader() optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate) 然后迭代进行以下步骤: 从DataLoader 中随机取一批训练数据; 将这批数据送入模型,计算出模型的预测值; 将模型预测值与真实值进行比较,计算损失函数(loss)。这里使用 tf.keras.losses 中的交叉熵函...
...该类继承自torch.utils.data.Dataset类,并包含以下方法:init:加载数据文件(假定是CSV格式),将数据分为特征(x_data)和标签(y_data),并存储数据集的长度(len...getitem:用于获取数据集中特定索引位置的样本。len:返回数据集的总长度。创建数据集实例dataset,并使用DataLoader创建数据加载器train_loader。...在...
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([ ...
data = json.dumps({ "signature_name": "call", "instances": data_loader.test_data[0:10].tolist() }) 1. 2. 3. 4. 参考资料 TensorFlow Serving:深度学习模型在生产环境的部署&上线 TensorFlow Serving
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...
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...