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.