Apache Mxnet 简明教程

Apache MXNet - Python API gluon

正如我们在前几章中已经讨论的那样，MXNet Gluon 为 DL 项目提供了一个清晰、简洁且简单的 API。它使 Apache MXNet 能够对 DL 模型进行原型制作、构建和训练，而不会影响训练速度。

As we have already discussed in previous chapters that, MXNet Gluon provides a clear, concise, and simple API for DL projects. It enables Apache MXNet to prototype, build, and train DL models without forfeiting the training speed.

Core Modules

让我们了解 Apache MXNet Python 应用程序编程接口 (API) gluon 的核心模块。

Let us learn the core modules of Apache MXNet Python application programming interface (API) gluon.

gluon.nn

Gluon 在 gluon.nn 模块中提供了大量的内置 NN 层。这就是称其为核心模块的原因。

Gluon provides a large number of build-in NN layers in gluon.nn module. That is the reason it is called the core module.

Methods and their parameters

以下是一些 mxnet.gluon.nn 核心模块涵盖的重要方法及其参数——

Following are some of the important methods and their parameters covered by mxnet.gluon.nn core module −

Methods and its Parameters

Definition

Activation(activation, **kwargs)

As name implies, this method applies an activation function to input.

AvgPool1D([pool_size, strides, padding, …])

This is average pooling operation for temporal data.

AvgPool2D([pool_size, strides, padding, …])

This is average pooling operation for spatial data.

AvgPool3D([pool_size, strides, padding, …])

This is Average pooling operation for 3D data. The data can be spatial or spatio-temporal.

BatchNorm([axis, momentum, epsilon, center, …])

It represents batch normalisation layer.

BatchNormReLU([axis, momentum, epsilon, …])

It also represents batch normalisation layer but with Relu activation function.

Block([prefix, params])

It gives the base class for all neural network layers and models.

Conv1D(channels, kernel_size[, strides, …])

This method is used for 1-D convolution layer. For example, temporal convolution.

Conv1DTranspose(channels, kernel_size[, …])

This method is used for Transposed 1D convolution layer.

Conv2D(channels, kernel_size[, strides, …])

This method is used for 2D convolution layer. For example, spatial convolution over images).

Conv2DTranspose(channels, kernel_size[, …])

This method is used for Transposed 2D convolution layer.

Conv3D(channels, kernel_size[, strides, …])

This method is used for 3D convolution layer. For example, spatial convolution over volumes.

Conv3DTranspose(channels, kernel_size[, …])

This method is used for Transposed 3D convolution layer.

Dense(units[, activation, use_bias, …])

This method represents for your regular densely-connected NN layer.

Dropout(rate[, axes])

As name implies, the method applies Dropout to the input.

ELU([alpha])

This method is used for Exponential Linear Unit (ELU).

Embedding(input_dim, output_dim[, dtype, …])

It turns non-negative integers into dense vectors of fixed size.

Flatten(**kwargs)

This method flattens the input to 2-D.

GELU(**kwargs)

This method is used for Gaussian Exponential Linear Unit (GELU).

GlobalAvgPool1D([layout])

With the help of this method, we can do global average pooling operation for temporal data.

GlobalAvgPool2D([layout])

With the help of this method, we can do global average pooling operation for spatial data.

GlobalAvgPool3D([layout])

With the help of this method, we can do global average pooling operation for 3-D data.

GlobalMaxPool1D([layout])

With the help of this method, we can do global max pooling operation for 1-D data.

GlobalMaxPool2D([layout])

With the help of this method, we can do global max pooling operation for 2-D data.

GlobalMaxPool3D([layout])

With the help of this method, we can do global max pooling operation for 3-D data.

GroupNorm([num_groups, epsilon, center, …])

This method applies group normalization to the n-D input array.

HybridBlock([prefix, params])

This method supports forwarding with both Symbol and NDArray.

HybridLambda(function[, prefix])

With the help of this method we can wrap an operator or an expression as a HybridBlock object.

HybridSequential([prefix, params])

It stacks HybridBlocks sequentially.

InstanceNorm([axis, epsilon, center, scale, …])

This method applies instance normalisation to the n-D input array.

Implementation Examples

在下面的示例中，我们将使用 Block()，它为所有神经网络层和模型提供基本类。

In the example below, we are going to use Block() which gives the base class for all neural network layers and models.

from mxnet.gluon import Block, nn
class Model(Block):
   def __init__(self, **kwargs):
      super(Model, self).__init__(**kwargs)
      # use name_scope to give child Blocks appropriate names.
      with self.name_scope():
         self.dense0 = nn.Dense(20)
         self.dense1 = nn.Dense(20)
   def forward(self, x):

      x = mx.nd.relu(self.dense0(x))
      return mx.nd.relu(self.dense1(x))

model = Model()
model.initialize(ctx=mx.cpu(0))
model(mx.nd.zeros((5, 5), ctx=mx.cpu(0)))

Output

您将看到以下输出 −

You will see the following output −

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
<NDArray 5x20 @cpu(0)*gt;

在下面的示例中，我们将使用 HybridBlock()，它支持 Symbol 和 NDArray 进行正向传播。

In the example below, we are going to use HybridBlock() that supports forwarding with both Symbol and NDArray.

import mxnet as mx
from mxnet.gluon import HybridBlock, nn


class Model(HybridBlock):
   def __init__(self, **kwargs):
      super(Model, self).__init__(**kwargs)
      # use name_scope to give child Blocks appropriate names.
      with self.name_scope():
         self.dense0 = nn.Dense(20)
         self.dense1 = nn.Dense(20)

   def forward(self, x):
      x = nd.relu(self.dense0(x))
      return nd.relu(self.dense1(x))
model = Model()
model.initialize(ctx=mx.cpu(0))

model.hybridize()
model(mx.nd.zeros((5, 5), ctx=mx.cpu(0)))

Output

输出如下：

The output is mentioned below −

[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
<NDArray 5x20 @cpu(0)>

gluon.rnn

Gluon 在 gluon.rnn 模块中提供了大量内置 recurrent neural network （RNN）层。那是它被称作核心模块的原因。

Gluon provides a large number of build-in recurrent neural network (RNN) layers in gluon.rnn module. That is the reason, it is called the core module.

Methods and their parameters

以下是 mxnet.gluon.nn 核心模块涵盖的一些重要方法及其参数：

Following are some of the important methods and their parameters covered by mxnet.gluon.nn core module:

Methods and its Parameters

Definition

BidirectionalCell(l_cell, r_cell[, …])

It is used for Bidirectional Recurrent Neural Network (RNN) cell.

DropoutCell(rate[, axes, prefix, params])

This method will apply dropout on the given input.

GRU(hidden_size[, num_layers, layout, …])

It applies a multi-layer gated recurrent unit (GRU) RNN to a given input sequence.

GRUCell(hidden_size[, …])

It is used for Gated Rectified Unit (GRU) network cell.

HybridRecurrentCell([prefix, params])

This method supports hybridize.

HybridSequentialRNNCell([prefix, params])

With the help of this method we can sequentially stack multiple HybridRNN cells.

LSTM(hidden_size[, num_layers, layout, …])0

It applies a multi-layer long short-term memory (LSTM) RNN to a given input sequence.

LSTMCell(hidden_size[, …])

It is used for Long-Short Term Memory (LSTM) network cell.

ModifierCell(base_cell)

It is the Base class for modifier cells.

RNN(hidden_size[, num_layers, activation, …])

It applies a multi-layer Elman RNN with tanh or ReLU non-linearity to a given input sequence.

RNNCell(hidden_size[, activation, …])

It is used for Elman RNN recurrent neural network cell.

RecurrentCell([prefix, params])

It represents the abstract base class for RNN cells.

SequentialRNNCell([prefix, params])

With the help of this method we can sequentially stack multiple RNN cells.

ZoneoutCell(base_cell[, zoneout_outputs, …])

This method applies Zoneout on the base cell.

Implementation Examples

在下例中，我们将使用 GRU()，它对给定的输入序列应用多层门控循环单元 (GRU) RNN。

In the example below, we are going to use GRU() which applies a multi-layer gated recurrent unit (GRU) RNN to a given input sequence.

layer = mx.gluon.rnn.GRU(100, 3)
layer.initialize()
input_seq = mx.nd.random.uniform(shape=(5, 3, 10))
out_seq = layer(input_seq)
h0 = mx.nd.random.uniform(shape=(3, 3, 100))
out_seq, hn = layer(input_seq, h0)
out_seq

Output

生成以下输出：

This produces the following output −

[[[ 1.50152072e-01 5.19012511e-01 1.02390535e-01 ... 4.35803324e-01
1.30406499e-01 3.30152437e-02]
[ 2.91542172e-01 1.02243155e-01 1.73325196e-01 ... 5.65296151e-02
1.76546033e-02 1.66693389e-01]
[ 2.22257316e-01 3.76294643e-01 2.11277917e-01 ... 2.28903517e-01
3.43954474e-01 1.52770668e-01]]


[[ 1.40634328e-01 2.93247789e-01 5.50393537e-02 ... 2.30207980e-01
6.61415309e-02 2.70989928e-02]
[ 1.11081995e-01 7.20834285e-02 1.08342394e-01 ... 2.28330195e-02
6.79589901e-03 1.25501186e-01]
[ 1.15944080e-01 2.41565228e-01 1.18612610e-01 ... 1.14908054e-01
1.61080107e-01 1.15969211e-01]]
………………………….

Example

hn

Output

生成以下输出：

This produces the following output −

[[[-6.08105101e-02 3.86217088e-02   6.64453954e-03 8.18805695e-02
3.85607071e-02 -1.36945639e-02 7.45836645e-03 -5.46515081e-03
9.49622393e-02 6.39371723e-02 -6.37890724e-03 3.82240303e-02
9.11015049e-02 -2.01375950e-02 -7.29381144e-02 6.93765879e-02
2.71829776e-02 -6.64435029e-02 -8.45306814e-02 -1.03075653e-01
6.72040805e-02 -7.06537142e-02 -3.93818803e-02 5.16211614e-03
-4.79770005e-02 1.10734522e-01 1.56721435e-02 -6.93409378e-03
1.16915874e-01 -7.95962065e-02 -3.06530762e-02 8.42394680e-02
7.60370195e-02 2.17055440e-01 9.85361822e-03 1.16660878e-01
4.08297703e-02 1.24978097e-02 8.25245082e-02 2.28673983e-02
-7.88266212e-02 -8.04114193e-02 9.28791538e-02 -5.70827350e-03
-4.46166918e-02 -6.41122833e-02 1.80885363e-02 -2.37745279e-03
4.37298454e-02 1.28888980e-01 -3.07202265e-02 2.50503756e-02
4.00907174e-02 3.37077095e-03 -1.78839862e-02 8.90695080e-02
6.30150884e-02 1.11416787e-01 2.12221760e-02 -1.13236710e-01
5.39616570e-02 7.80710578e-02 -2.28817668e-02 1.92073174e-02
………………………….

在下例中，我们将使用 LSTM()，它对给定的输入序列应用长短期记忆 (LSTM) RNN。

In the example below we are going to use LSTM() which applies a long-short term memory (LSTM) RNN to a given input sequence.

layer = mx.gluon.rnn.LSTM(100, 3)
layer.initialize()

input_seq = mx.nd.random.uniform(shape=(5, 3, 10))
out_seq = layer(input_seq)
h0 = mx.nd.random.uniform(shape=(3, 3, 100))
c0 = mx.nd.random.uniform(shape=(3, 3, 100))
out_seq, hn = layer(input_seq,[h0,c0])
out_seq

Output

输出如下：

The output is mentioned below −

[[[ 9.00025964e-02 3.96071747e-02 1.83841765e-01 ... 3.95872220e-02
1.25569820e-01 2.15555862e-01]
[ 1.55962542e-01 -3.10300849e-02 1.76772922e-01 ... 1.92474753e-01
2.30574399e-01 2.81707942e-02]
[ 7.83204585e-02 6.53361529e-03 1.27262697e-01 ... 9.97719541e-02
1.28254429e-01 7.55299702e-02]]
[[ 4.41036932e-02 1.35250352e-02 9.87644792e-02 ... 5.89378644e-03
5.23949116e-02 1.00922674e-01]
[ 8.59075040e-02 -1.67027581e-02 9.69351009e-02 ... 1.17763653e-01
9.71239135e-02 2.25218050e-02]
[ 4.34580036e-02 7.62207608e-04 6.37005866e-02 ... 6.14888743e-02
5.96345589e-02 4.72368896e-02]]
……………

Example

hn

Output

运行代码后，你将看到以下输出 −

When you run the code, you will see the following output −

[
[[[ 2.21408084e-02 1.42750628e-02 9.53067932e-03 -1.22849066e-02
1.78788435e-02 5.99269159e-02 5.65306023e-02 6.42553642e-02
6.56616641e-03 9.80876666e-03 -1.15729487e-02 5.98640442e-02
-7.21173314e-03 -2.78371759e-02 -1.90690923e-02 2.21447181e-02
8.38765781e-03 -1.38521893e-02 -9.06938594e-03 1.21346042e-02
6.06449470e-02 -3.77471633e-02 5.65885007e-02 6.63008019e-02
-7.34188128e-03 6.46054149e-02 3.19911093e-02 4.11194898e-02
4.43960279e-02 4.92892228e-02 1.74766723e-02 3.40303481e-02
-5.23341820e-03 2.68163737e-02 -9.43402853e-03 -4.11836170e-02
1.55221792e-02 -5.05655073e-02 4.24557598e-03 -3.40388380e-02
……………………

Training Modules

Gluon 中的训练模块如下 −

The training modules in Gluon are as follows −

gluon.loss

在 mxnet.gluon.loss 模块中，Gluon 提供了预定义的损失函数。基本上，它具有用于训练神经网络的损失。这就是它称为训练模块的原因。

In mxnet.gluon.loss module, Gluon provides pre-defined loss function. Basically, it has the losses for training neural network. That is the reason it is called the training module.

Methods and their parameters

以下是 mxnet.gluon.loss 训练模块中涵盖的一些重要方法及其参数：

Following are some of the important methods and their parameters covered by mxnet.gluon.loss training module:

Methods and its Parameters

Definition

Loss(weight, batch_axis, **kwargs)

This acts as the base class for loss.

L2Loss([weight, batch_axis])

It calculates the mean squared error (MSE) between label and prediction(pred).

L1Loss([weight, batch_axis])

It calculates the mean absolute error (MAE) between label and pred.

SigmoidBinaryCrossEntropyLoss([…])

This method is used for the cross-entropy loss for binary classification.

SigmoidBCELoss

This method is used for the cross-entropy loss for binary classification.

SoftmaxCrossEntropyLoss([axis, …])

It computes the softmax cross-entropy loss (CEL).

SoftmaxCELoss

It also computes the softmax cross entropy loss.

KLDivLoss([from_logits, axis, weight, …])

It is used for the Kullback-Leibler divergence loss.

CTCLoss([layout, label_layout, weight])

It is used for connectionist Temporal Classification Loss (TCL).

HuberLoss([rho, weight, batch_axis])

It calculates smoothed L1 loss. The smoothed L1 loss will be equal to L1 loss if absolute error exceeds rho but is equal to L2 loss otherwise.

HingeLoss([margin, weight, batch_axis])

This method calculates the hinge loss function often used in SVMs:

SquaredHingeLoss([margin, weight, batch_axis])

This method calculates the soft-margin loss function used in SVMs:

LogisticLoss([weight, batch_axis, label_format])

This method calculates the logistic loss.

TripletLoss([margin, weight, batch_axis])

This method calculates triplet loss given three input tensors and a positive margin.

PoissonNLLLoss([weight, from_logits, …])

The function calculates the Negative Log likelihood loss.

CosineEmbeddingLoss([weight, batch_axis, margin])

The function computes the cosine distance between the vectors.

SDMLLoss([smoothing_parameter, weight, …])

This method calculates Batchwise Smoothed Deep Metric Learning (SDML) Loss given two input tensors and a smoothing weight SDM Loss. It learns similarity between paired samples by using unpaired samples in the minibatch as potential negative examples.

Example

我们知道 mxnet.gluon.loss.loss 将计算标签与预测 (pred) 之间的 MSE（均方误差）。通过使用以下公式执行：

As we know that mxnet.gluon.loss.loss will calculate the MSE(Mean Squared Error) between label and prediction (pred). It is done with the help of following formula:

mean squared error

gluon.parameter

mxnet.gluon.parameter 是一个包含参数的容器，即块的权重。

mxnet.gluon.parameter is a container that holds the parameters i.e. weights of the Blocks.

Methods and their parameters

mxnet.gluon.parameter 训练模块涵盖的一些重要方法及其参数如下 −

Following are some of the important methods and their parameters covered by mxnet.gluon.parameter training module −

Methods and its Parameters

Definition

cast(dtype)

This method will cast data and gradient of this Parameter to a new data type.

data([ctx])

This method will return a copy of this parameter on one context.

grad([ctx])

This method will return a gradient buffer for this parameter on one context.

initialize([init, ctx, default_init, …])

This method will initialize parameter and gradient arrays.

list_ctx()

This method will return a list of contexts this parameter is initialized on.

list_data()

This method will return copies of this parameter on all contexts. It will be done in the same order as creation.

list_grad()

This method will return gradient buffers on all contexts. This will be done in the same order as values().

list_row_sparse_data(row_id)

This method will return copies of the ‘row_sparse’ parameter on all contexts. This will be done in the same order as creation.

reset_ctx(ctx)

This method will re-assign Parameter to other contexts.

row_sparse_data(row_id)

This method will return a copy of the ‘row_sparse’ parameter on the same context as row_id’s.

set_data(data)

This method will set this parameter’s value on all contexts.

var()

This method will return a symbol representing this parameter.

zero_grad()

This method will set the gradient buffer on all contexts to 0.

Implementation Example

在以下示例中，我们将使用 initialize() 方法初始化参数和梯度数组，如下所示：-

In the example below, we will initialize parameters and the gradients arrays by using initialize() method as follows −

weight = mx.gluon.Parameter('weight', shape=(2, 2))
weight.initialize(ctx=mx.cpu(0))
weight.data()

Output

输出如下：

The output is mentioned below −

[[-0.0256899 0.06511251]
[-0.00243821 -0.00123186]]
<NDArray 2x2 @cpu(0)>

Example

weight.grad()

Output

输出如下 −

The output is given below −

[[0. 0.]
[0. 0.]]
<NDArray 2x2 @cpu(0)>

Example

weight.initialize(ctx=[mx.gpu(0), mx.gpu(1)])
weight.data(mx.gpu(0))

Output

您将看到以下输出 −

You will see the following output −

[[-0.00873779 -0.02834515]
 [ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(0)>

Example

weight.data(mx.gpu(1))

Output

执行以上代码时，应该看到以下输出 −

When you execute the above code, you should see the following output −

[[-0.00873779 -0.02834515]
 [ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(1)>

gluon.trainer

mxnet.gluon.trainer 对一组参数应用了一个优化器。它应该和 autograd 一起使用。

mxnet.gluon.trainer applies an Optimizer on a set of parameters. It should be used together with autograd.

Methods and their parameters

以下是 mxnet.gluon.trainer 训练模块涵盖的一些重要的方法及参数：-

Following are some of the important methods and their parameters covered by mxnet.gluon.trainer training module −

Methods and its Parameters

Definition

allreduce_grads()

This method will reduce the gradients from different contexts for each parameter (weight).

load_states(fname)

As name implies, this method will load trainer states.

save_states(fname)

As name implies, this method will save trainer states.

set_learning_rate(lr)

This method will set a new learning rate of the optimizer.

step(batch_size[, ignore_stale_grad])

This method will make one step of parameter update. It should be called after autograd.backward() and outside of record() scope.

update(batch_size[, ignore_stale_grad])

This method will also make one step of parameter update. It should be called after autograd.backward() and outside of record() scope and after trainer.update().

Data Modules

Gluon 的数据模块在下面有说明：-

The data modules of Gluon are explained below −

gluon.data

Gluon 在 gluon.data 模块中提供大量内置的数据集实用程序。这就是它被称为数据模块的原因。

Gluon provides a large number of build-in dataset utilities in gluon.data module. That is the reason it is called the data module.

Classes and their parameters

以下是 mxnet.gluon.data 核心模块涵盖的一些重要的方法及参数。这些方法通常与数据集、采样和 DataLoader 相关。

Following are some of the important methods and their parameters covered by mxnet.gluon.data core module. These methods are typically related to Datasets, Sampling, and DataLoader.

Methods and its Parameters

Definition

ArrayDataset(*args)

This method represents a dataset which combines two or more than two dataset-like objects. For example, Datasets, lists, arrays, etc.

BatchSampler(sampler, batch_size[, last_batch])

This method wraps over another Sampler. Once wrapped it returns the mini batches of samples.

DataLoader(dataset[, batch_size, shuffle, …])

Similar to BatchSampler but this method loads data from a dataset. Once loaded it returns the mini batches of data.

This represents the abstract dataset class.

FilterSampler(fn, dataset)

This method represents the samples elements from a Dataset for which fn (function) returns True.

RandomSampler(length)

This method represents samples elements from [0, length) randomly without replacement.

RecordFileDataset(filename)

It represents a dataset wrapping over a RecordIO file. The extension of the file is .rec.

Sampler

This is the base class for samplers.

SequentialSampler(length[, start])

It represents the sample elements from the set [start, start+length) sequentially.

It represents the sample elements from the set [start, start+length) sequentially.

Implementation Examples

在以下示例中，我们将使用 gluon.data.BatchSampler() API，它包装另一个采样器。它返回批量迷你采样。

In the example below, we are going to use gluon.data.BatchSampler() API, which wraps over another sampler. It returns the mini batches of samples.

import mxnet as mx
from mxnet.gluon import data
sampler = mx.gluon.data.SequentialSampler(15)
batch_sampler = mx.gluon.data.BatchSampler(sampler, 4, 'keep')
list(batch_sampler)

Output

输出如下：

The output is mentioned below −

[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14]]

gluon.data.vision.datasets

Gluon 在 gluon.data.vision.datasets 模块中提供了大量的预定义视觉数据集函数。

Gluon provides a large number of pre-defined vision dataset functions in gluon.data.vision.datasets module.

Classes and their parameters

MXNet 为我们提供了有用且重要的数据集，其类和参数如下所示——

MXNet provides us useful and important datasets, whose classes and parameters are given below −

Classes and its Parameters

Definition

MNIST([root, train, transform])

This is a useful dataset providing us the handwritten digits. The url for MNIST dataset is [role="bare"]http://yann.lecun.com/exdb/mnist

FashionMNIST([root, train, transform])

This dataset consists of Zalando’s article images consisting of fashion products. It is a drop-in replacement of original MNIST dataset. You can get this dataset from [role="bare"]https://github.com/zalandoresearch/fashion-mnist

CIFAR10([root, train, transform])

This is an image classification dataset from [role="bare"]https://www.cs.toronto.edu/~kriz/cifar.html. In this dataset each sample is an image with shape (32, 32, 3).

CIFAR100([root, fine_label, train, transform])

This is CIFAR100 image classification dataset from [role="bare"]https://www.cs.toronto.edu/~kriz/cifar.html. It also has each sample is an image with shape (32, 32, 3).

ImageRecordDataset (filename[, flag, transform])

This dataset is wrapping over a RecordIO file that contains images. In this each sample is an image with its corresponding label.

ImageFolderDataset (root[, flag, transform])

This is a dataset for loading image files that are stored in a folder structure.

ImageListDataset ([root, imglist, flag])

This is a dataset for loading image files that are specified by a list of entries.

Example

在以下示例中，我们将展示 ImageListDataset() 的用法，它用于加载由条目列表指定图像文件——

In the example below, we are going to show the use of ImageListDataset(), which is used for loading image files that are specified by a list of entries −

# written to text file *.lst

0 0 root/cat/0001.jpg
1 0 root/cat/xxxa.jpg
2 0 root/cat/yyyb.jpg
3 1 root/dog/123.jpg
4 1 root/dog/023.jpg
5 1 root/dog/wwww.jpg

# A pure list, each item is a list [imagelabel: float or list of float, imgpath]

[[0, root/cat/0001.jpg]
[0, root/cat/xxxa.jpg]
[0, root/cat/yyyb.jpg]
[1, root/dog/123.jpg]
[1, root/dog/023.jpg]
[1, root/dog/wwww.jpg]]

Utility Modules

Gluon 中的实用程序模块如下——

The utility modules in Gluon are as follows −

gluon.utils

Gluon 在 gluon.utils 模块中提供了大量的内置并行化实用程序优化器。它提供了用于训练的各种实用程序。这就是它被称为实用程序模块的原因。

Gluon provides a large number of build-in parallelisation utility optimiser in gluon.utils module. It provides variety of utilities for training. That is the reason it is called the utility module.

Functions and their parameters

以下是该实用程序模块中包含的功能及其参数，该模块名为 gluon.utils −

Following are the functions and their parameters consisting in this utility module named gluon.utils −

Functions and its Parameters

Definition

split_data(data, num_slice[, batch_axis, …])

This function is usually use for data parallelism and each slice is sent to one device i.e. GPU. It splits an NDArray into num_slice *slices along *batch_axis.

split_and_load(data, ctx_list[, batch_axis, …])

This function splits an NDArray into len(ctx_list) slices along batch_axis. The only difference from above split_data () function is that, it also loads each slice to one context in ctx_list.

clip_global_norm(arrays, max_norm[, …])

The job of this function is to rescale NDArrays in such a way that the sum of their 2-norm is smaller than max_norm.

check_sha1(filename, sha1_hash)

This function will check whether the sha1 hash of the file content matches the expected hash or not.

download(url[, path, overwrite, sha1_hash, …])

As name specifies, this function will download a given URL.

replace_file(src, dst)

This function will implement atomic os.replace. it will be done with Linux and OSX.