18.5. d2l API Document

class d2l.Accumulator(n)

Sum a list of numbers over time

class d2l.BPRLoss(weight=None, batch_axis=0, **kwargs)
forward(positive, negative)

Defines the forward computation. Arguments can be either NDArray or Symbol.

class d2l.CTRDataset(data_path, feat_mapper=None, defaults=None, min_threshold=4, num_feat=34)
class d2l.Decoder(**kwargs)

The base decoder interface for the encoder-decoder architecture.

forward(X, state)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.DotProductAttention(dropout, **kwargs)
forward(query, key, value, valid_length=None)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.Encoder(**kwargs)

The base encoder interface for the encoder-decoder architecture.

forward(X)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.EncoderDecoder(encoder, decoder, **kwargs)

The base class for the encoder-decoder architecture.

forward(enc_X, dec_X, *args)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.HingeLossbRec(weight=None, batch_axis=0, **kwargs)
forward(positive, negative, margin=1)

Defines the forward computation. Arguments can be either NDArray or Symbol.

class d2l.MLPAttention(units, dropout, **kwargs)
forward(query, key, value, valid_length)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.MaskedSoftmaxCELoss(axis=-1, sparse_label=True, from_logits=False, weight=None, batch_axis=0, **kwargs)
forward(pred, label, valid_length)

Defines the forward computation. Arguments can be either NDArray or Symbol.

class d2l.RNNModel(rnn_layer, vocab_size, **kwargs)
forward(inputs, state)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.RNNModelScratch(vocab_size, num_hiddens, ctx, get_params, init_state, forward)

A RNN Model based on scratch implementations

class d2l.RandomGenerator(sampling_weights)

Draw a random int in [0, n] according to n sampling weights

class d2l.Residual(num_channels, use_1x1conv=False, strides=1, **kwargs)
forward(X)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.Seq2SeqDecoder(vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs)
forward(X, state)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.Seq2SeqEncoder(vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs)
forward(X, *args)

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

*argslist of NDArray

Input tensors.

class d2l.SeqDataLoader(batch_size, num_steps, use_random_iter, max_tokens)

A iterator to load sequence data

class d2l.Timer

Record multiple running times.

class d2l.VOCSegDataset(is_train, crop_size, voc_dir)

A customized dataset to load VOC dataset.

filter(imgs)

Returns a new dataset with samples filtered by the filter function fn.

Note that if the Dataset is the result of a lazily transformed one with transform(lazy=False), the filter is eagerly applied to the transformed samples without materializing the transformed result. That is, the transformation will be applied again whenever a sample is retrieved after filter().

fncallable

A filter function that takes a sample as input and returns a boolean. Samples that return False are discarded.

Dataset

The filtered dataset.

d2l.bbox_to_rect(bbox, color)

Convert bounding box to matplotlib format.

d2l.build_colormap2label()

Build a RGB color to label mapping for segmentation.

d2l.corr2d(X, K)

Compute 2D cross-correlation.

class d2l.defaultdict

defaultdict(default_factory[, …]) –> dict with default factory

The default factory is called without arguments to produce a new value when a key is not present, in __getitem__ only. A defaultdict compares equal to a dict with the same items. All remaining arguments are treated the same as if they were passed to the dict constructor, including keyword arguments.

copy() → a shallow copy of D.
default_factory

Factory for default value called by __missing__().

d2l.download_voc_pascal(data_dir='../data')

Download the VOC2012 segmentation dataset.

d2l.evaluate_loss(net, data_iter, loss)

Evaluate the loss of a model on the given dataset

d2l.load_array(data_arrays, batch_size, is_train=True)

Construct a Gluon data loader

d2l.load_data_fashion_mnist(batch_size, resize=None)

Download the Fashion-MNIST dataset and then load into memory.

d2l.load_data_pikachu(batch_size, edge_size=256)

Load the pikachu dataset

d2l.load_data_voc(batch_size, crop_size)

Download and load the VOC2012 semantic dataset.

d2l.plot(X, Y=None, xlabel=None, ylabel=None, legend=[], xlim=None, ylim=None, xscale='linear', yscale='linear', fmts=['-', 'm--', 'g-.', 'r:'], figsize=(3.5, 2.5), axes=None)

Plot data points.

d2l.read_time_machine()

Load the time machine book into a list of sentences.

d2l.read_voc_images(root='../data/VOCdevkit/VOC2012', is_train=True)

Read all VOC feature and label images.

d2l.resnet18(num_classes)

A slightly modified ResNet-18 model.

d2l.set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend)

Set the axes for matplotlib.

d2l.set_figsize(figsize=(3.5, 2.5))

Set the figure size for matplotlib.

d2l.show_bboxes(axes, bboxes, labels=None, colors=None)

Show bounding boxes.

d2l.show_images(imgs, num_rows, num_cols, titles=None, scale=1.5)

Plot a list of images.

d2l.show_trace_2d(f, results)

Show the trace of 2D variables during optimization.

d2l.split_batch(X, y, ctx_list)

Split X and y into multiple devices specified by ctx.

d2l.split_data_ml100k(data, num_users, num_items, split_mode='random', test_ratio=0.1)

Split the dataset in random mode or seq-aware mode.

d2l.synthetic_data(w, b, num_examples)

generate y = X w + b + noise

d2l.tokenize(lines, token='word')

Split sentences into word or char tokens

d2l.train_2d(trainer, steps=20)

Optimize a 2-dim objective function with a customized trainer.

d2l.try_all_gpus()

Return all available GPUs, or [cpu(),] if no GPU exists.

d2l.try_gpu(i=0)

Return gpu(i) if exists, otherwise return cpu().

d2l.update_D(X, Z, net_D, net_G, loss, trainer_D)

Update discriminator

d2l.update_G(Z, net_D, net_G, loss, trainer_G)

Update generator

d2l.use_svg_display()

Use the svg format to display a plot in Jupyter.

d2l.voc_label_indices(colormap, colormap2label)

Map a RGB color to a label.

d2l.voc_rand_crop(feature, label, height, width)

Randomly crop for both feature and label images.