TensorLayer3.0是一款兼容多种深度学习框架后端的深度学习库，支持TensorFlow, MindSpore, PaddlePaddle为后端计算引擎。TensorLayer3.0使用方式简单，并且在选定运算后端后能和该后端的算子混合使用。TensorLayer3.0提供了数据处理、模型构建、模型训练等深度学习全流程API，同一套代码可以通过一行代码切换后端，减少框架之间算法迁移需要重构代码的繁琐工作。

## 一、TensorLayer安装

TensorLayer安装前置条件包括TensorFlow, numpy, matplotlib等，如果你需要使用GPU加速还需要安装CUDA和cuDNN。

### 1.1 安装后端

TensorLayer支持多种后端，默认为TensorFlow，也支持MindSpore和PaddlePaddle，PaddlePaddle目前只支持少量Layer，后续新版本中会持续更新。

安装TensorFlow

```python
pip3 install tensorflow-gpu # GPU version
pip3 install tensorflow # CPU version
```
如果你想使用MindSpore后端还需要安装MindSpore1.2.0,下面给出了MindSpore1.2.0GPU版本的安装，如果需要安装CPU或者Ascend可以参考MindSpore官网。
```python
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.2.1/MindSpore/gpu/ubuntu_x86/cuda-10.1/mindspore_gpu-1.2.1-cp37-cp37m-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
```
如果想要使用PaddlePaddle后端，还需要安装PaddlePaddle2.0，下面给出了PaddlePaddle2.0GPU版本的安装，其他平台请参考PaddlePaddle官网。
```python
python -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
```
### 1.2 安装TensorLayer

通过PIP安装稳定版本

```plain
pip install tensorlayer3
pip install tensorlayer3 -i https://pypi.tuna.tsinghua.edu.cn/simple  (faster in China)
```
如果要获得最新开发版本可以通过下载源码安装
```plain
pip3 install git+https://git.openi.org.cn/TensorLayer/tensorlayer3.0.git
```
## 二、TensorLayer3.0特性

TensorLayer3.0版本主要设计目标如下，我们将会支持TensorFlow, Pytorch, MindSpore, PaddlePaddle作为计算引擎。在API层提供深度学习模型构建组件(Layers)，数据处理(DataFlow), 激活函数（Activations），参数初始化函数，代价函数，模型优化函数以及一些常用操作函数。在最上层我们利用TensorLayer开发了一些例子和预训练模型。

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)

### 2.1 可扩展性

TensorLayer3.0的相比于TensorLayer2.0之前的版本对后端进行了解耦。在之前的版本中我们设计的Layer直接使用了TensorFlow的算子，这为后续扩展后端带来不便。为此在新的版本中，我们将所有后端算子均封装在backend层，并且对不同框架之间的接口进行了统一，在构建Layer时均调用统一的接口来达到兼容多框架的目的。

### 2.2 简易性

TensorLayer3.0使用简单，我们设计了两种构建模型的方式，对于顺序连贯的模型我们提供了SequentialLayer来构建，对于复杂模型可以通过SubClass的方式继承Module来构建。在TensorLayer3.0中构建的网络模型可以当成Layer在__init__中初始化在forward中调用。TensorLayer3.0构建网络时可以无需计算上一层的输出（不用输入in_channels参数），通过最后init_build操作来完成参数初始化自动推断模型输出大小。

### 2.3 兼容性

TensorLayer3.0构建的模型能直接在TensorFlow, MindSpore, PaddlePaddle中使用，可以混合对应框架的算子进行使用。例如用TensorLayer搭建网络，使用TensorFlow后端，那么在数据处理和模型训练时可以直接用TensorFlow提供的算子完成。

## 三、数据集加载

TensorLayer内置了一些常见的数据集例如mnist, cifar10。这里加载手写数字识别数据集，用来模型训练和评估。

```python
import tensorlayer as tl
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
```
## 四、数据预处理

TensorLayer提供了大量数据处理操作，也可以直接使用对应框架数据处理操作完成你的数据构建。

Tensorlayer目前拥有完善的图像预处理操作。为了满足开发者习惯，集成以TensorFlow、MindSpore、PaddlePaddle为后端的图像算子。图像算子主要基于各框架本身tensor操作以及PIL、opencv库完成，并且能够自动根据全局后端环境变量将图像矩阵数据转换为后端框架对应的数据格式。为了图像算子在各框架后端保持一致，TensorLayer综合考虑TensorFlow、Mindspore、PaddlePaddle框架各自图像算子功能及参数，增加和调整不同后端框架源码扩展了图像处理功能。以PyTorch为后端的图像算子将在未来开发中更新。

TensorLayer的图像数据预处理例子如下：

```python
import tensorlayer as tl
import numpy as np
image=(np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
transform=tl.vision.transforms.Resize(size(100,100),interpolation='bilinear')
image=transform(image)
print(image.shape)
#image shape:(100, 100, 3)
image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
transform=tl.vision.transforms.Pad(padding=10,padding_value=0,mode='constant')
image = transform(image)
print(image.shape)
#image shape : (244, 244, 3)
```
## 五、模型构建

### 5.1 SequentialLayer构建

针对有顺序的线性网络结构，你可以通过SequentialLayer来快速构建模型，可以减少定义网络等代码编写，具体如下：我们构建一个多层感知机模型。

```python
import tensorlayer as tl
from tensorlayer.layers import Dense
layer_list = []
layer_list.append(Dense(n_units=800, act=tl.ReLU, in_channels=784, name='Dense1'))
layer_list.append(Dense(n_units=800, act=tl.ReLU, in_channels=800, name='Dense2'))
layer_list.append(Dense(n_units=10, act=tl.ReLU, in_channels=800, name='Dense3'))
MLP = SequentialLayer(layer_list)
```
### 5.2 继承基类Module构建

针对较为复杂的网络，可以使用Module子类定义的方式来进行模型构建，在__init__对Layer进行声明，在forward里使用声明的Layer进行前向计算。这种方式中声明的Layer可以进行复用，针对相同的Layer构造一次，在forward可以调用多次。同样我们构建一个多层感知机模型。

```python
import tensorlayer as tl
from tensorlayer.layers import Module, Dropout, Dense
class MLP(Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.dense1 = Dense(n_units=800, act=tl.ReLU, in_channels=784)
        self.dense2 = Dense(n_units=800, act=tl.ReLU, in_channels=800)
        self.dense3 = Dense(n_units=10, act=tl.ReLU, in_channels=800)
    def forward(self, x):
        z = self.dense1(z)
        z = self.dense2(z)
        out = self.dense3(z)
        return out
```
### 5.3 构建复杂网络结构

在构建网络时，我们经常遇到一些模块重复使用多次，可以通过循环来构建。

例如在网络中需要将感知机当成一个Block，并且使用三次，我们先定义要多次调用的Block

```python
import tensorlayer as tl
from tensorlayer.layers import Module, Dense, Elementwise
class Block(Module):
    def __init__(self, in_channels):
        super(Block, self).__init__()
        self.dense1 = Dense(in_channels=in_channels, n_units=256)
        self.dense2 = Dense(in_channels=256, n_units=384)
        self.dense3 = Dense(in_channels=in_channels, n_units=384)
        self.concat = Elementwise(combine_fn=tl.ops.add)
        
    def forward(self, inputs):
        z = self.dense1(inputs)
        z1 = self.dense2(z)
        z2 = self.dense3(inputs)
        out = self.concat([z1, z2])
        return out
```
定义好Block后我们通过SequentialLayer和Module构建网络
```python
class CNN(Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.flatten = Flatten(name='flatten')
        self.dense1 = Dense(384, act=tl.ReLU, in_channels=2304)
        self.dense_add = self.make_layer(in_channel=384)
        self.dense2 = Dense(192, act=tl.ReLU, n_channels=384)
        self.dense3 = Dense(10, act=None, in_channels=192)
        
    def forward(self, x):
        z = self.flatten(z)
        z = self.dense1(z)
        z = self.dense_add(z)
        z = self.dense2(z)
        z = self.dense3(z)
        return z
        
    def make_layer(self, in_channel):
        layers = []
        _block = Block(in_channel)
        layers.append(_block)
        for _ in range(1, 3):
            range_block = Block(in_channel)
            layers.append(range_block)
        return SequentialLayer(layers)
```
### 5.4 自动推断上一层输出大小

我们构建网络时经常需要手动输入上一层的输出大小，作为下一层的输入，也就是每个Layer中的in_channels参数。在TensoLayer中也可以无需输入in_channels，构建网络后给定网络的输入调用一次参数初始化即可。

```python
import tensorlayer as tl
from tensorlayer.layers import Module, Dense
class CustomModel(Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.dense1 = Dense(n_units=800)
        self.dense2 = Dense(n_units=800, act=tl.ReLU)
        self.dense3 = Dense(n_units=10, act=tl.ReLU)
        
    def forward(self, x):
        z = self.dense1(z)
        z = self.dense2(z)
        out = self.dense3(z)
        return out
MLP = CustomModel()
input = tl.layers.Input(shape=(1, 784))
MLP.init_build(input)
```
## 六、模型训练

TensorLayer提供了模型训练模块，可以直接调用进行训练。TensorLayer构建的模型也能支持在其他框架中直接使用，如用TensorLayer构建MLP模型，使用的是TensorFlow后端，那么可以使用TensoFlow的算子完成模型训练。

### 6.1 调用模型训练模块训练

调用封装好的models模块进行训练。

```python
import tensorlayer as tl
optimizer = tl.optimizers.Momentum(0.05, 0.9)
model = tl.models.Model(network=MLP, loss_fn=tl.cost.softmax_cross_entropy_with_logits, optimizer=optimizer)
model.train(n_epoch=500, train_dataset=train_ds, print_freq=2)
```
### 6.2 混合对应框架算子进行训练

混合TensorFlow进行训练。下面例子中optimizer和loss均可以使用TensorFlow的算子

```python
import tensorlayer as tl
import tensorflow as tf
optimizer = tl.optimizers.Momentum(0.05, 0.9)
# optimizer = tf.optimizers.Momentum(0.05, 0.9)
for epoch in range(n_epoch):
    for X_batch, y_batch in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
        MLP.set_train()
        with tf.GradientTape() as tape:
            _logits = MLP(X_batch)
            _loss = tl.cost.softmax_cross_entropy_with_logits(_logits, y_batch)
        grad = tape.gradient(_loss, train_weights)
        optimizer.apply_gradients(zip(grad, train_weights))
```

## 七、完整实例

同一套代码通过设置后端进行切换不同后端训练，无需修改代码。在os.environ['TL_BACKEND']中可以设置为'tensorflow’,'mindspore', 'paddle'。

```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
os.environ['TL_BACKEND'] = 'tensorflow'
# os.environ['TL_BACKEND'] = 'mindspore'
# os.environ['TL_BACKEND'] = 'paddle'
import numpy as np
import tensorlayer as tl
from tensorlayer.layers import Module
from tensorlayer.layers import Dense, Dropout
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
class CustomModel(Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.dropout1 = Dropout(keep=0.8)
        self.dense1 = Dense(n_units=800, act=tl.ReLU, in_channels=784)
        self.dropout2 = Dropout(keep=0.8)
        self.dense2 = Dense(n_units=800, act=tl.ReLU, in_channels=800)
        self.dropout3 = Dropout(keep=0.8)
        self.dense3 = Dense(n_units=10, act=tl.ReLU, in_channels=800)
    def forward(self, x, foo=None):
        z = self.dropout1(x)
        z = self.dense1(z)
        # z = self.bn(z)
        z = self.dropout2(z)
        z = self.dense2(z)
        z = self.dropout3(z)
        out = self.dense3(z)
        if foo is not None:
            out = tl.ops.relu(out)
        return out
def generator_train():
    inputs = X_train
    targets = y_train
    if len(inputs) != len(targets):
        raise AssertionError("The length of inputs and targets should be equal")
    for _input, _target in zip(inputs, targets):
        yield (_input, np.array(_target))
MLP = CustomModel()
n_epoch = 50
batch_size = 128
print_freq = 2
shuffle_buffer_size = 128
train_weights = MLP.trainable_weights
optimizer = tl.optimizers.Momentum(0.05, 0.9)
train_ds = tl.dataflow.FromGenerator(
    generator_train, output_types=(tl.float32, tl.int32) , column_names=['data', 'label']
)
train_ds = tl.dataflow.Shuffle(train_ds,shuffle_buffer_size)
train_ds = tl.dataflow.Batch(train_ds,batch_size)
model = tl.models.Model(network=MLP, loss_fn=tl.cost.softmax_cross_entropy_with_logits, optimizer=optimizer)
model.train(n_epoch=n_epoch, train_dataset=train_ds, print_freq=print_freq, print_train_batch=False)
model.save_weights('./model.npz', format='npz_dict')
model.load_weights('./model.npz', format='npz_dict')
```
## 八、预训练模型

在TensorLayer中我们将持续提供了丰富的预训练模型，和应用。例如VGG16, VGG19, ResNet50, YOLOv4.下面例子展示了在MS-COCO数据集中利用YOLOv4进行目标检测，对应预训练模型和数据可以从examples/model_zoo中找到。

```python
import numpy as np
import cv2
from PIL import Image
from examples.model_zoo.common import yolo4_input_processing, yolo4_output_processing, \
    result_to_json, read_class_names, draw_boxes_and_labels_to_image_with_json
from examples.model_zoo.yolo import YOLOv4
import tensorlayer as tl
tl.logging.set_verbosity(tl.logging.DEBUG)
INPUT_SIZE = 416
image_path = './data/kite.jpg'
class_names = read_class_names('./model/coco.names')
original_image = cv2.imread(image_path)
image = cv2.cvtColor(np.array(original_image), cv2.COLOR_BGR2RGB)
model = YOLOv4(NUM_CLASS=80, pretrained=True)
model.set_eval()
batch_data = yolo4_input_processing(original_image)
feature_maps = model(batch_data)
pred_bbox = yolo4_output_processing(feature_maps)
json_result = result_to_json(image, pred_bbox)
image = draw_boxes_and_labels_to_image_with_json(image, json_result, class_names)
image = Image.fromarray(image.astype(np.uint8))
image.show()
```
## 九、自定义Layer

在TensorLayer中自定以Layer需要继承Module，在build中我们对训练参数进行定义，在forward中我们定义前向计算。下面给出用TensorFlow后端时，定义全连接层$$a=f(x*W + b)$$如果你想定义其他后端的Dense需要将算子换成对应后端。

如果要定义一个通用的Layer则要把算子接口进行统一后封装在backend中，具体可以参考tensorlayer/layers中的Layer。

```python
from tensorlayer.layers import Module
class Dense(Module):
  def __init__(
          self,
          n_units,
          act=None,  
          name=None, 
          in_channels = None
  ):
      super(Dense, self).__init__(name, act=act)
      self.n_units = n_units
      self.in_channels = in_channels
      self.build()
      self._built = True
  def build(self): # initialize the model weights here
      shape = [self.in_channels, self.n_units]
      self.W = self._get_weights("weights", shape=tuple(shape), init=self.W_init)
      self.b = self._get_weights("biases", shape=(self.n_units, ), init=self.b_init)
  def forward(self, inputs): # call function
      z = tf.matmul(inputs, self.W) + self.b
      if self.act: # is not None
          z = self.act(z)
      return z
```