Add jittor

examples/basic_tutorials/cifar10_cnn_dist.py

@@ -2,7 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import os
-os.environ['TL_BACKEND'] = 'paddle'
+# os.environ['TL_BACKEND'] = 'paddle'
+os.environ['TL_BACKEND'] = 'jittor'
 # os.environ['TL_BACKEND'] = 'tensorflow'
 # os.environ['TL_BACKEND'] = 'mindspore'
 # os.environ['TL_BACKEND'] = 'torch'

examples/basic_tutorials/cifar10_cnn_train.py

@@ -4,11 +4,15 @@
 # TensorlayerX目前支持包括TensorFlow、Pytorch、PaddlePaddle、MindSpore作为计算后端，指定计算后端的方法也非常简单，只需要设置环境变量即可
 
 import os
-os.environ['TL_BACKEND'] = 'paddle'
+# os.environ['TL_BACKEND'] = 'paddle'
+
+os.environ['TL_BACKEND'] = 'jittor'
 # os.environ['TL_BACKEND'] = 'tensorflow'
 # os.environ['TL_BACKEND'] = 'mindspore'
 # os.environ['TL_BACKEND'] = 'torch'
 
+
+
 import tensorlayerx as tlx
 from tensorlayerx.nn import Module
 from tensorlayerx.nn import (Conv2d, Linear, Flatten, MaxPool2d, BatchNorm2d)
@@ -54,6 +58,7 @@ def forward(self, x):
         z = self.linear1(z)
         z = self.linear2(z)
         return z
+
 
 
 # get the network
@@ -70,7 +75,7 @@ def forward(self, x):
 
 # 定义损失函数、优化器等
 loss_fn=tlx.losses.softmax_cross_entropy_with_logits
-optimizer = tlx.optimizers.Adam(learning_rate)
+optimizer = tlx.optimizers.Adam(lr=learning_rate)
 metrics = tlx.metrics.Accuracy()
 
 

examples/basic_tutorials/gradient_clip_mixed_tensorflow.py

@@ -2,9 +2,10 @@
 # -*- coding: utf-8 -*-
 # The tensorlayerx and tensorflow operators can be mixed
 import os
-os.environ['TL_BACKEND'] = 'tensorflow'
+# os.environ['TL_BACKEND'] = 'tensorflow'
 # os.environ['TL_BACKEND'] = 'paddle'
 # os.environ['TL_BACKEND'] = 'torch'
+os.environ['TL_BACKEND'] = 'jittor'
 
 
 import time