build model section updates, breadcrumb test, linear wording fix

beginner_source/quickstart/build_model_tutorial.py

@@ -10,22 +10,16 @@
 # The data has been loaded and transformed we can now build the model. 
 # We will leverage `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ predefined layers that PyTorch has that can simplify our code.
 # 
-# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` 
-# container from class `torch.nn. Sequential <https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html>`_ 
-# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` 
-# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined.
-# 
-# Another way to bulid this model is with a class 
-# using `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html)>`_ 
+# In the below example, for our FashionMNIT image dataset, we are using `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html)>`_ 
 # A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules.
 # This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. 
 # 
 # Lets break down the steps to build this model below
 # 
 
-##########################################
-# Inline nn.Sequential Example:
-# ----------------------------
+#############################################
+# Import the Packages
+# --------------------------
 #
 
 import os
@@ -35,27 +29,31 @@
 from torch.utils.data import DataLoader
 from torchvision import datasets, transforms
 
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-print('Using {} device'.format(device))
-
-# model
-model = nn.Sequential(
-        nn.Flatten(),
-        nn.Linear(28*28, 512),
-        nn.ReLU(),
-        nn.Linear(512, 512),
-        nn.ReLU(),
-        nn.Linear(512, len(classes)),
-        nn.Softmax(dim=1)
-    ).to(device)
-
-print(model)
 
 #############################################
-# Class nn.Module Example:
-# --------------------------
+# Get Device for Training
+# -----------------------
+# Here we check to see if `torch.cuda <https://pytorch.org/docs/stable/notes/cuda.html>`_ 
+# is available to use the GPU, else we will use the CPU. 
+#
+# Example:
 #
 
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print('Using {} device'.format(device))
+
+##############################################
+# Define the Class
+# -------------------------
+#
+# Here we define the `NeuralNetwork` class which inherits from ``nn.Module`` which is the base class for 
+# building neural network modules. The ``init`` function defines the layers in the neural network
+# then it initializes the modules to be called in the ``forward`` function.
+# Then we call the ``NeuralNetwork`` class and assign the device. When training 
+# the model we will call ``model`` and pass the data (x) into the forward function and 
+# through each layer of our network.
+#
+#
 
 class NeuralNetwork(nn.Module):
     def __init__(self):
@@ -66,7 +64,6 @@ def __init__(self):
         self.output = nn.Linear(512, 10)
 
     def forward(self, x):
-
         x = self.flatten(x)
         x = F.relu(self.layer1(x))
         x = F.relu(self.layer2(x))
@@ -76,32 +73,14 @@ def forward(self, x):
 
 print(model)
 
-
-#############################################
-# Get Device for Training
-# -----------------------
-# Here we check to see if `torch.cuda <https://pytorch.org/docs/stable/notes/cuda.html>`_ is available to use the GPU, else we will use the CPU. 
-#
-# Example:
-#
-
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-print('Using {} device'.format(device))
-
-##############################################
-# __init__
-# -------------------------
-#
-# The ``init`` function inherits from ``nn.Module`` which is the base class for 
-# building neural network modules. This function defines the layers in your neural network
-# then it initializes the modules to be called in the ``forward`` function.
-# 
-
 ##############################################
 # The Model Module Layers
 # -------------------------
 #
-# Lets break down each model layer in the FashionMNIST model.
+# Lets break down each model layer in the FashionMNIST model. In the below example we are using a `Sequential` 
+# container from class `torch.nn. Sequential <https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html>`_ 
+# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` 
+# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined.
 #
 
 ##################################################
@@ -127,8 +106,8 @@ def forward(self, x):
 # `nn.Linear <https://pytorch.org/docs/stable/generated/torch.nn.Linear.html>`_ to add a linear layer to the model.
 # -------------------------------
 #
-# Now that we have flattened our tensor dimension we will apply a linear layer 
-# transform that will calculate/learn the weights and the bias.
+# Now that we have flattened our tensor dimension we will apply a linear layer. The linear layer is 
+# a module that applies a linear transformation on the input using it's stored weights and biases.
 #
 # From the docs:
 # 
@@ -166,24 +145,6 @@ def forward(self, x):
 print(model)
 
 
-###################################################
-# Forward Function
-# --------------------------------
-#
-# In the class implementation of the neural network we define a ``forward`` function.  
-# Then call the ``NeuralNetwork`` class and assign the device. When training the model we will call ``model``
-# and pass the data (x) into the forward function and through each layer of our network.
-#
-#
-def forward(self, x):
-    x = self.flatten(x)
-    x = F.relu(self.layer1(x))
-    x = F.relu(self.layer2(x))
-    x = self.output(x)
-    return F.softmax(x, dim=1)
-model = NeuralNetwork().to(device)
-
-
 ################################################
 # In the next section you will learn about how to train the model and the optimization loop for this example.
 #

beginner_source/quickstart/data_quickstart_tutorial.py

@@ -1,4 +1,13 @@
 """
+.. raw:: html
+
+    <div>
+        <a href="https://torchtutorialstaging.z5.web.core.windows.net/beginner/quickstart/tensor_tutorial.html">Tensors &gt; </a> 
+        <a href="https://torchtutorialstaging.z5.web.core.windows.net/beginner/quickstart/data_quickstart_tutorial.html">Datasets & Dataloaders</a>
+    </div>
+
+.. raw:: html
+
 Datasets & Dataloaders
 ===================
 """

beginner_source/quickstart/tensor_tutorial.py

@@ -1,4 +1,13 @@
 """
+.. raw:: html
+
+    <div>
+        <a href="https://torchtutorialstaging.z5.web.core.windows.net/beginner/quickstart/tensor_tutorial.html">Tensors</a>
+    </div>
+
+.. raw:: html
+
+
 Tensors and Operations
 ----------------------
 **Tensor** is the basic computational unit in PyTorch. It is very