piyushghai commented on a change in pull request #14462: [MXNET-1358][Fit API] Fit api tutorial URL: https://github.com/apache/incubator-mxnet/pull/14462#discussion_r272694298
########## File path: docs/tutorials/gluon/fit_api_tutorial.md ########## @@ -0,0 +1,208 @@ +<!--- Licensed to the Apache Software Foundation (ASF) under one --> +<!--- or more contributor license agreements. See the NOTICE file --> +<!--- distributed with this work for additional information --> +<!--- regarding copyright ownership. The ASF licenses this file --> +<!--- to you under the Apache License, Version 2.0 (the --> +<!--- "License"); you may not use this file except in compliance --> +<!--- with the License. You may obtain a copy of the License at --> + +<!--- http://www.apache.org/licenses/LICENSE-2.0 --> + +<!--- Unless required by applicable law or agreed to in writing, --> +<!--- software distributed under the License is distributed on an --> +<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> +<!--- KIND, either express or implied. See the License for the --> +<!--- specific language governing permissions and limitations --> +<!--- under the License. --> + + +# Gluon Fit API + +In this tutorial, we will see how to use the [Gluon Fit API](https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design) which is a simple and flexible way to train deep learning models using the [Gluon APIs](http://mxnet.incubator.apache.org/versions/master/gluon/index.html) in Apache MXNet. + +Prior to Fit API, training using Gluon required one to write a custom ["Gluon training loop"](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/logistic_regression_explained.html#defining-and-training-the-model). Fit API reduces the complexity and amount of boiler plate code required to train a model, provides an easy to use and a powerful API. + +To demonstrate the Fit API, this tutorial will train an Image Classification model using the [ResNet-18](https://arxiv.org/abs/1512.03385) architecture for the neural network. The model will be trained using the [Fashion-MNIST dataset](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/). + + +## Prerequisites + +To complete this tutorial, you will need: + +- [MXNet](https://mxnet.incubator.apache.org/install/#overview) (The version of MXNet will be >= 1.5.0) +- [Jupyter Notebook](https://jupyter.org/index.html) (For interactively running the provided .ipynb file) + + + + +```python +import mxnet as mx +from mxnet import gluon +from mxnet.gluon.model_zoo import vision +from mxnet.gluon.estimator import estimator, event_handler + +ctx = mx.gpu() if mx.context.num_gpus() > 0 else mx.cpu() +mx.random.seed(7) # Set a fixed seed +``` + +## Dataset + +[Fashion-MNIST](https://research.zalando.com/welcome/mission/research-projects/fashion-mnist/) dataset consists of fashion items divided into ten categories : t-shirt/top, trouser, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot. + +- It has 60,000 gray scale images of size 28 * 28 for training. +- It has 10,000 gray scale images os size 28 * 28 for testing/validation. + +We will use ```gluon.data.vision``` package to directly import the Fashion-MNIST dataset and perform pre-processing on it. + + +```python +# Get the training data +fashion_mnist_train = gluon.data.vision.FashionMNIST(train=True) + +# Get the validation data +fashion_mnist_val = gluon.data.vision.FashionMNIST(train=False) +``` + + +```python +transforms = [gluon.data.vision.transforms.Resize(224), # We pick 224 as the model we use takes an input of size 224. + gluon.data.vision.transforms.ToTensor()] + +# Now we will stack all these together. +transforms = gluon.data.vision.transforms.Compose(transforms) +``` + + +```python +# Apply the transformations +fashion_mnist_train = fashion_mnist_train.transform_first(transforms) +fashion_mnist_val = fashion_mnist_val.transform_first(transforms) +``` + + +```python +batch_size = 256 # Batch size of the images +num_workers = 4 # The number of parallel workers for loading the data using Data Loaders. + +train_data_loader = gluon.data.DataLoader(fashion_mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers) +val_data_loader = gluon.data.DataLoader(fashion_mnist_val, batch_size=batch_size, shuffle=False, num_workers=num_workers) +``` + +## Model and Optimizers + +Let's load the resnet-18 model architecture from [Gluon Model Zoo](http://mxnet.apache.org/api/python/gluon/model_zoo.html) and initialize it's parameters. + + +```python +resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes = 10, ctx=ctx) +resnet_18_v1.initialize(force_reinit=True, init = mx.init.Xavier(), ctx=ctx) +``` + +After defining the model, let's setup the trainer object for training. + +We will be using ```SoftmaxCrossEntropyLoss``` as the loss function since this is a multi-class classification problem. We will be using ```sgd``` (Stochastic Gradient Descent) as the optimizer. You can experiment with a different optimizer as well. + + +```python +loss_fn = gluon.loss.SoftmaxCrossEntropyLoss() +learning_rate = 0.04 # You can experiment with your own learning rate here + +num_epochs = 2 # You can run training for more epochs +trainer = gluon.Trainer(resnet_18_v1.collect_params(), + 'sgd', {'learning_rate': learning_rate}) +``` + +## Train using Fit API + +As stated earlier, Fit API greatly simplifies the boiler plate code and complexity for training using MXNet Gluon. + +In the basic usage example, with just 2 lines of code, we will set up our model for training. + +### Basic Usage + + +```python +train_acc = mx.metric.Accuracy() # Metric to monitor + +# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context +est = estimator.Estimator(net=resnet_18_v1, + loss=loss_fn, + metrics=train_acc, + trainers=trainer, + context=ctx) + +# Magic line +est.fit(train_data=train_data_loader, + epochs=num_epochs, + batch_size=batch_size) +``` + + [Epoch 0] [Step 256/60000] time/step: 1.420s accuracy: 0.0938 softmaxcrossentropyloss0: 2.9419 <!--notebook-skip-line--> + .... <!--notebook-skip-line--> + [Epoch 0] finished in 51.375s: train_accuracy: 0.7916 train_softmaxcrossentropyloss0: 0.5750 <!--notebook-skip-line--> + [Epoch 1] [Step 256/60000] time/step: 0.414s accuracy: 0.8555 softmaxcrossentropyloss0: 0.3621 <!--notebook-skip-line--> + .... <!--notebook-skip-line--> + [Epoch 1] finished in 49.889s: train_accuracy: 0.8854 train_softmaxcrossentropyloss0: 0.3157 <!--notebook-skip-line--> + + +### Advanced Usage + +Fit API is also customizable with several `Event Handlers` which give a fine grained control over the steps in training and exposes callback methods for : `train_begin`, `train_end`, `batch_begin`, `batch_end`, `epoch_begin` and `epoch_end`. + +One can use built-in event handlers such as ```LoggingHandler```, ```CheckpointHandler``` or ```EarlyStoppingHandler``` or to create a custom handler, one can create a new class by inherinting [```EventHandler```](https://github.com/apache/incubator-mxnet/blob/fit-api/python/mxnet/gluon/estimator/event_handler.py#L31). + + +```python +# Let's reset the model, trainer and accuracy objects from above + +resnet_18_v1.initialize(force_reinit=True, init = mx.init.Xavier(), ctx=ctx) +trainer = gluon.Trainer(resnet_18_v1.collect_params(), + 'sgd', {'learning_rate': learning_rate}) +train_acc = mx.metric.Accuracy() + +``` + + +```python +# Define the estimator, by passing to it the model, loss function, metrics, trainer object and context +est = estimator.Estimator(net=resnet_18_v1, + loss=loss_fn, + metrics=train_acc, + trainers=trainer, + context=ctx) + +# Define the handlers, let's say Checkpointhandler +checkpoint_handler = event_handler.CheckpointHandler(estimator=est, + filepath='./my_best_model.params', + monitor='train_accuracy', # Monitors a metric + save_best_only=True) # Save the best model in terms of + # training accuracy + +# Magic line +est.fit(train_data=train_data_loader, + epochs=num_epochs, + event_handlers=[checkpoint_handler], # Add the event handlers + batch_size=batch_size) +``` + + [Epoch 0] [Step 256/60000] time/step: 0.426s accuracy: 0.1211 softmaxcrossentropyloss0: 2.6261 + .... <!--notebook-skip-line--> + [Epoch 0] finished in 50.390s: train_accuracy: 0.7936 train_softmaxcrossentropyloss0: 0.5639 <!--notebook-skip-line--> + [Epoch 1] [Step 256/60000] time/step: 0.414s accuracy: 0.8984 softmaxcrossentropyloss0: 0.2958 <!--notebook-skip-line--> + .... <!--notebook-skip-line--> + [Epoch 1] finished in 50.474s: train_accuracy: 0.8871 train_softmaxcrossentropyloss0: 0.3101 <!--notebook-skip-line--> + + +You can load the saved model, by using ```load_parameters``` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](http://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html#saving-model-parameters-to-file) + +## Summary + +In this tutorial, we learnt how to use ```Gluon Fit APIs``` for training a deep learning model and also saw an option to customize it with the use of Event Handlers. +For more references on the Fit API and advanced usage details, checkout its [documentation](http://mxnet.apache.org/api/python/gluon/gluon.html). Review comment: Done. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
