dataloader . dataset ) # 10000 print ( f ” s i z e :{ s i z e }” ) num_batches = len ( dataloader ) print ( f ”num_batches :{ num_batches}” ) test_loss , correct = 0 , 0 with torch . no_grad () : f o r correct += ( pred . argmax (1) == y) . type ( torch . f l o a t ) . sum() . item () test_loss /= num_batches cor rect /= s i z e print ( f ” Test␣ Error : ␣\n␣Accuracy : ␣ {(100∗ cor rec t ) : >0.1 f }%,␣Avg␣

Animator(xlabel='epoch', xlim=[1, num_epochs], legend=['train loss', 'train acc', 'test acc']) timer, num_batches = d2l.Timer(), len(train_iter) for epoch in range(num_epochs): # 训练损失之和，训练准确率之和，样本数 metric = d2l metric[2] if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: (continues on next page) 244 6. 卷积神经网络 (continued from previous page) animator.add(epoch + (i + 1) / num_batches, (train_l, train_acc 返回从pos位置开始的长度为num_steps的序列 return corpus[pos: pos + num_steps] num_batches = num_subseqs // batch_size for i in range(0, batch_size * num_batches, batch_size): # 在这里，initial_indices包含子序列的随机起始索引 initial_indices_per_batch

Dataloader: groups data in batches, enables multiprocessing ● dataset = MyDataset(file) ● dataloader = DataLoader(dataset, batch_size, shuffle=True) More info about batches and shuffling here. Dataset

TensorDataset Dataloader • Dataloader • What happens if we have a huge dataset? Have to train in 'batches' • Use PyTorch's Dataloader class! • We tell it which dataset to use, the desired mini-batch size

range(epochs): print('Epoch', e) batches = 0 for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32): model.fit(x_batch, y_batch) batches += 1 if batches >= len(x_train) / 32: # 我们需要手动打破循环，

function, or outside it. This is crucial for deep learning applications which frequently operate on batches of data. Using vectorized operations also speeds up the execution (and this book is about efficiency

[1., 1.]], dtype=float32) batch_dot It is used to perform the product of two data in batches. Input dimension must be 2 or higher. It is shown below: >>> a_batch = k.ones(shape=(2,3)) >>>