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docs/tutorials/gluon/fit_api_tutorial.md

@@ -24,7 +24,6 @@ With the Fit API, you can train a deep learning model with miminal amount of cod
 
 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:
@@ -41,7 +40,8 @@ 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()
+gpu_count = mx.context.num_gpus()
+ctx = [mx.gpu(i) for i in range(gpu_count)] if gpu_count > 0 else mx.cpu()
 mx.random.seed(7) # Set a fixed seed
 ```
 
@@ -84,8 +84,10 @@ fashion_mnist_val = fashion_mnist_val.transform_first(transforms)
 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)
+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
@@ -107,6 +109,7 @@ loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
 
 Let's define the trainer object for training the model.
 
+
 ```python
 learning_rate = 0.04 # You can experiment with your own learning rate here
 num_epochs = 2 # You can run training for more epochs
@@ -128,23 +131,23 @@ 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)
+                          loss=loss_fn, 
+                          metrics=train_acc, 
+                          trainer=trainer, 
+                          context=ctx)
 
 # Magic line
 est.fit(train_data=train_data_loader,
-              epochs=num_epochs, 
-              batch_size=batch_size)
+        epochs=num_epochs)
 ```
 
-    [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-->
+    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
+    Train for 2 epochs. <!--notebook-skip-line-->
+
+    [Epoch 0] finished in 25.110s: train_accuracy : 0.7877 train_softmaxcrossentropyloss0 : 0.5905 <!--notebook-skip-line-->
+
+    [Epoch 1] finished in 23.595s: train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
+    Train finished using total 48s at epoch 1. train_accuracy : 0.8823 train_softmaxcrossentropyloss0 : 0.3197 <!--notebook-skip-line-->
 
 
 ### Advanced Usage
@@ -161,41 +164,47 @@ 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)
+                          loss=loss_fn,
+                          metrics=train_acc,
+                          trainer=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
+checkpoint_handler = event_handler.CheckpointHandler(filepath='./my_best_model.params',
+                                                     monitor='val_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)
+        val_data=val_data_loader,
+        epochs=num_epochs,
+        event_handlers=checkpoint_handler) # Add the event handlers
 ```
 
-    [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-->
+    Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->
+    Train for 2 epochs. <!--notebook-skip-line-->
+
+    [Epoch 0] finished in 25.236s: train_accuracy : 0.7917 train_softmaxcrossentropyloss0 : 0.5741 val_accuracy : 0.6612 val_softmaxcrossentropyloss0 : 0.8627 <!--notebook-skip-line-->
+
+    [Epoch 1] finished in 24.892s: train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
+
+    Train finished using total 50s at epoch 1. train_accuracy : 0.8826 train_softmaxcrossentropyloss0 : 0.3229 val_accuracy : 0.8474 val_softmaxcrossentropyloss0 : 0.4262 <!--notebook-skip-line-->
+
 
+You can load the saved model, by using ```load_parameters``` API in Gluon. For more details refer to the [Loding model parameters from file tutorial](http://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html#saving-model-parameters-to-file)
 
-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)
+
+```python
+resnet_18_v1 = vision.resnet18_v1(pretrained=False, classes = 10)
+resnet_18_v1.load_parameters('./my_best_model.params', ctx=ctx)
+```
 
 ## Summary