diff --git a/docs/trains_examples.md b/docs/trains_examples.md deleted file mode 100644 index 2495892f..00000000 --- a/docs/trains_examples.md +++ /dev/null @@ -1,247 +0,0 @@ -# TRAINS Usage Examples - -## Introduction -TRAINS includes usage examples for the *Keras*, *PyTorch*, and *TensorFlow* deep learning frameworks, -as well as *Jupyter Notebook* integration and custom examples for reporting metrics, configuring models. -You can run these examples and view their results on the TRAINS Web-App. - -The examples are described below, including a link for the source code -and expected results for each run. -* For each example, only two lines of TRAINS integration code, were added - - from trains import Task - task = Task.init(project_name=”examples”, task_name=”description”) - -## Viewing experiment results - -In order to view an experiment's results (or other details) you can either: - -1. Open the TRAINS Web-App in your browser and login. -2. On the Home page, in the *recent project* section, click the card for the project containing the experiment -(example experiments can be found under the *examples* project card). -3. In the *Experiments* tab, click your experiment. The details panel slides open. -4. Choose the experiment details by clicking one of the information tabs. - -OR - -1. While running the experiment, a direct link for a dedicated results page is printed. - - -# Keras Examples - -### Keras with TensorBoard - MNIST Training - -[keras_tensorboard.py](https://github.com/allegroai/trains/blob/master/examples/keras_tensorboard.py) -is an example of training a small convolutional NN on the MNIST DataSet. - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **MODEL** - * Input model weights, if executed for the second time (loaded from the previous checkpoint) - * Input model’s creator experiment (a link to the experiment details in the *EXPERIMENTS* page) - * Output model + Configuration -* **RESULTS** - * **SCALARS**: Accuracy/loss scalar metric graphs - * **PLOTS**: Convolution weights histograms - * **LOG**: Console standard output/error - -# Pytorch Examples - -### PyTorch - MNIST Training - -[pytorch_mnist.py](https://github.com/allegroai/trains/blob/master/examples/pytorch_mnist.py) is an example -of PyTorch MNIST training integration. - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **MODEL** - * Input model weights, if executed for the second time (loaded from the previous checkpoint) - * Input model’s creator experiment (a link to the experiment details in the *EXPERIMENTS* page) - * Output model (a link to the output model details in the *MODELS* page) -* **RESULTS** - * **LOG**: Console standard output/error - -### PyTorch and Matplotlib - Testing Style Transfer - -[pytorch_matplotlib.py](https://github.com/allegroai/trains/blob/master/examples/pytorch_matplotlib.py) -is an example of -connecting the neural style transfer from the official PyTorch tutorial to TRAINS. -Neural-Style, or Neural-Transfer, allows you to take an image and -reproduce it with a new artistic style. The algorithm takes three images -(an input image, a content-image, and a style-image) and change the input -to resemble the content of the content-image and the artistic style of the style-image. - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **MODEL** - * Input model (a link to the input model details in the *MODELS* page) - * Output model (a link to the output model details in the *MODELS* page) -* **RESULTS** - * **DEBUG IMAGES**: Input image, input style images, an output transferred style image - * **LOG**: Console standard output/error - -### PyTorch with Tensorboard - MNIST Train - -[pytorch_tensorboard.py](https://github.com/allegroai/trains/blob/master/examples/pytorch_tensorboard.py) -is an example of PyTorch MNIST training running with Tensorboard - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **MODEL** - * Input model, if executed for the second time (a link to the input model details in the *MODELS* page) - * Input model’s creator experiment (a link to the experiment details in the *EXPERIMENTS* page) - * Output model (a link to the output model details in the *MODELS* page) -* **RESULTS** - * **SCALARS**: Train and test loss scalars - * **LOG**: Console standard output/error - -### PyTorch with tensorboardX - MNIST Train - -[pytorch_tensorboardX.py](https://github.com/allegroai/trains/blob/master/examples/pytorch_tensorboardX.py) -is an example of PyTorch MNIST training running with tensorboardX - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **MODEL** - * Input model, if executed for the second time (a link to the input model details in the *MODELS* page) - * Input model’s creator experiment (a link to the experiment details in the *EXPERIMENTS* page) - * Output model (a link to the output model details in the *MODELS* page) -* **RESULTS** - * **SCALARS**: Train and test loss scalars - * **LOG**: Console standard output/error - -# TensorFlow Examples - -### TensorBoard with TensorFlow (without Training) - -[tensorboard_toy.py](https://github.com/allegroai/trains/blob/master/examples/tensorboard_toy.py) -is a toy example of TensorBoard. - -**View Example Output** - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **RESULTS** - * **SCALARS**: Random variable samples scalars - * **PLOTS**: Random variable samples histograms - * **DEBUG IMAGES**: Test images - * **LOG**: Console standard output/error - -### TensorFlow in Eager Mode - -[tensorflow_eager.py](https://github.com/allegroai/trains/blob/master/examples/tensorflow_eager.py) -is an example of running Tensorflow in eager mode - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **RESULTS** - * **SCALARS**: Generator and discriminator loss - * **DEBUG IMAGES**: Generated images - * **LOG**: Console standard output/error - -### TensorBoard Plugin - Precision Recall Curves - -[tensorboard_pr_curve.py](https://github.com/allegroai/trains/blob/master/examples/tensorboard_pr_curve.py) -is an example of TensorBoard precision recall curves - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **RESULTS** - * **PLOTS**: Precision recall curves - * **DEBUG IMAGES**: Generated images - * **LOG**: Console standard output/error - -### Hyper Parameters / Tensorflow Flags / absl -##### Hyper Parameters / Toy Tensorflow FLAGS logging with absl - -[hyper_parameters_example.py](https://github.com/allegroai/trains/blob/master/examples/hyper_parameters_example.py) -is an example of toy Tensorflow FLAGS logging with absl package (*absl-py*) coupled with parameters dictionary - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Tensorflow flags (with 'TF_DEFINE/' prefix) -* **RESULTS** - * **LOG**: Console standard output/error - -### TensorFlow MNIST Classifier with TensorBoard Reports - -[tensorflow_mnist_with_summaries.py](https://github.com/allegroai/trains/blob/master/examples/tensorflow_mnist_with_summaries.py) -is an example of Tensorflow MNIST with TensorBoard summary, model storage, and logging. - -Relevant outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Command line arguments -* **MODEL** - * Output model (a link to the output model details in the *MODELS* page) -* **RESULTS** - * **SCALARS**: Network statistics across the training steps (e.g., cross entropy, dropout, and specific layer statistics) - * **PLOTS**: Convolutional layer histogram - * **DEBUG IMAGES**: Sample of the network input images - * **LOG**: Console standard output/error - - -# *Jupyter Notebook* Example - -[jupyter.ipynb](https://github.com/allegroai/trains/blob/master/examples/jupyter.ipynb) -is an example of integrating matplotlib and training with keras on -*Jupyter Notebook*. -This example connects a parameters dictionary, prints simple graphs and trains an MNIST classifier using Keras. - -Relevant Outputs - -* **EXECUTION** - * **HYPER PARAMETERS**: Parameter dictionary -* **MODEL** - * Output model (a link to the output model details in the *MODELS* page) - * Model Configuration -* **RESULTS** - * **SCALARS**: Training loss across iterations - * **PLOTS**: Sine and circles plots, convolution weights histogram - * **LOG**: Console standard output/error - - -# Custom Examples - -### Manual Reporting - -[manual_reporting.py](https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py) -is an example of manually reporting graphs and statistics. - -Relevant outputs - -* **RESULTS** - * **SCALARS**: Scalar graphs - * **PLOTS**: Confusion matrix, histogram, 2D scatter plot, 3D scatter plot - * **DEBUG IMAGES**: Uploaded example images - * **LOG**: Console standard output/error - -### Manual Model Configuration - -[manual_model_config.py](https://github.com/allegroai/trains/blob/master/examples/manual_model_config.py) -is an example of manually configuring a model, model storage, label enumeration values, and logging. - -Relevant Outputs - -* **MODEL** - * Output model (a link to the output model details in the *MODELS* page, including **label enumeration** values) - * Model Configuration -* **RESULTS** - * **LOG**: Console standard output/error