examples.rst

Examples

We list some examples here, but more tutorials and applications can be found in Github examples.

Basics

• Multi-layer perceptron (MNIST), simple usage and supports multiple backends. Classification task, see mnist_mlp.py.
• Generative Adversarial Networks (MNIST), simple usage and supports multiple backends. See mnist_gan.py.
• Convolutional Network (CIFAR-10), simple usage and supports multiple backends. Classification task, see cifar10_cnn.py.
• Recurrent Neural Network (IMDB), simple usage and supports multiple backends. Text classification task, see imdb_lstm_simple.py.
• Using tensorlayerx to automatic inference input shape. See automatic_inference_input _shape.py.
• Using ModuleList in tensorlayerx. See module_contaniner.py.
• Using Sequential in tensorlayerx. See mnist_sequential.py.
• Using Dataflow in tensorlayerx. See mnist_dataflow.py.
• Using nested layer in tensorlayerx. See nested_usage_of_layer.py.
• Using tensorlayerx to save tensorflow model to pb. See tensorflow_model_save_to_pb.py.
• Using tensorlayerx to load model from npz. See tensorlayer_model_load.py.
• Using tensorlayerx model training monitoring. See using_tensorboardx.py.
• Load the Paddle model parameters using tensorlayerx. See load_paddle_parameters_to_tensorlayerx.py.
• Load the PyTorch model parameters using tensorlayerx. See load_pytorch_parameters_to_tensorlayerx.py.

Commonly used dataset and pretrained models

• MNIST, see OpenI. or MNIST.
• CIFAR10, see OpenI. or CIFAR10.
• YOLOv4 Pretrained Model, see OpenI.
• VGG16 Pretrained Model, see OpenI.
• VGG19 Pretrained Model, see OpenI.
• ResNet50 Pretrained Model, see OpenI.