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