Removing Conda install references.

Author: AlannaBurke

beginner_source/introyt/tensorboardyt_tutorial.py

@@ -24,13 +24,6 @@
 To run this tutorial, you’ll need to install PyTorch, TorchVision,
 Matplotlib, and TensorBoard.
 
-With ``conda``:
-
-.. code-block:: sh
-
-    conda install pytorch torchvision -c pytorch
-    conda install matplotlib tensorboard
-
 With ``pip``:
 
 .. code-block:: sh
@@ -43,14 +36,18 @@
 
 Introduction
 ------------
- 
+
 In this notebook, we’ll be training a variant of LeNet-5 against the
 Fashion-MNIST dataset. Fashion-MNIST is a set of image tiles depicting
 various garments, with ten class labels indicating the type of garment
-depicted. 
+depicted.
 
 """
 
+# Image display
+import matplotlib.pyplot as plt
+import numpy as np
+
 # PyTorch model and training necessities
 import torch
 import torch.nn as nn
@@ -61,10 +58,6 @@
 import torchvision
 import torchvision.transforms as transforms
 
-# Image display
-import matplotlib.pyplot as plt
-import numpy as np
-
 # PyTorch TensorBoard support
 from torch.utils.tensorboard import SummaryWriter
 
@@ -79,51 +72,59 @@
 ######################################################################
 # Showing Images in TensorBoard
 # -----------------------------
-# 
+#
 # Let’s start by adding sample images from our dataset to TensorBoard:
-# 
+#
 
 # Gather datasets and prepare them for consumption
 transform = transforms.Compose(
-    [transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))])
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
 # Store separate training and validations splits in ./data
-training_set = torchvision.datasets.FashionMNIST('./data',
-    download=True,
-    train=True,
-    transform=transform)
-validation_set = torchvision.datasets.FashionMNIST('./data',
-    download=True,
-    train=False,
-    transform=transform)
-
-training_loader = torch.utils.data.DataLoader(training_set,
-                                              batch_size=4,
-                                              shuffle=True,
-                                              num_workers=2)
-
-
-validation_loader = torch.utils.data.DataLoader(validation_set,
-                                                batch_size=4,
-                                                shuffle=False,
-                                                num_workers=2)
+training_set = torchvision.datasets.FashionMNIST(
+    "./data", download=True, train=True, transform=transform
+)
+validation_set = torchvision.datasets.FashionMNIST(
+    "./data", download=True, train=False, transform=transform
+)
+
+training_loader = torch.utils.data.DataLoader(
+    training_set, batch_size=4, shuffle=True, num_workers=2
+)
+
+
+validation_loader = torch.utils.data.DataLoader(
+    validation_set, batch_size=4, shuffle=False, num_workers=2
+)
 
 # Class labels
-classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
-        'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')
+classes = (
+    "T-shirt/top",
+    "Trouser",
+    "Pullover",
+    "Dress",
+    "Coat",
+    "Sandal",
+    "Shirt",
+    "Sneaker",
+    "Bag",
+    "Ankle Boot",
+)
+
 
 # Helper function for inline image display
 def matplotlib_imshow(img, one_channel=False):
     if one_channel:
         img = img.mean(dim=0)
-    img = img / 2 + 0.5     # unnormalize
+    img = img / 2 + 0.5  # unnormalize
     npimg = img.numpy()
     if one_channel:
         plt.imshow(npimg, cmap="Greys")
     else:
         plt.imshow(np.transpose(npimg, (1, 2, 0)))
 
+
 # Extract a batch of 4 images
 dataiter = iter(training_loader)
 images, labels = next(dataiter)
@@ -138,14 +139,14 @@ def matplotlib_imshow(img, one_channel=False):
 # minibatch of our input data. Below, we use the ``add_image()`` call on
 # ``SummaryWriter`` to log the image for consumption by TensorBoard, and
 # we also call ``flush()`` to make sure it’s written to disk right away.
-# 
+#
 
 # Default log_dir argument is "runs" - but it's good to be specific
 # torch.utils.tensorboard.SummaryWriter is imported above
-writer = SummaryWriter('runs/fashion_mnist_experiment_1')
+writer = SummaryWriter("runs/fashion_mnist_experiment_1")
 
 # Write image data to TensorBoard log dir
-writer.add_image('Four Fashion-MNIST Images', img_grid)
+writer.add_image("Four Fashion-MNIST Images", img_grid)
 writer.flush()
 
 # To view, start TensorBoard on the command line with:
@@ -157,17 +158,18 @@ def matplotlib_imshow(img, one_channel=False):
 # If you start TensorBoard at the command line and open it in a new
 # browser tab (usually at `localhost:6006 <localhost:6006>`__), you should
 # see the image grid under the IMAGES tab.
-# 
+#
 # Graphing Scalars to Visualize Training
 # --------------------------------------
-# 
+#
 # TensorBoard is useful for tracking the progress and efficacy of your
 # training. Below, we’ll run a training loop, track some metrics, and save
 # the data for TensorBoard’s consumption.
-# 
+#
 # Let’s define a model to categorize our image tiles, and an optimizer and
 # loss function for training:
-# 
+#
+
 
 class Net(nn.Module):
     def __init__(self):
@@ -187,7 +189,7 @@ def forward(self, x):
         x = F.relu(self.fc2(x))
         x = self.fc3(x)
         return x
-    
+
 
 net = Net()
 criterion = nn.CrossEntropyLoss()
@@ -197,7 +199,7 @@ def forward(self, x):
 ##########################################################################
 # Now let’s train a single epoch, and evaluate the training vs. validation
 # set losses every 1000 batches:
-# 
+#
 
 print(len(validation_loader))
 for epoch in range(1):  # loop over the dataset multiple times
@@ -213,44 +215,50 @@ def forward(self, x):
         optimizer.step()
 
         running_loss += loss.item()
-        if i % 1000 == 999:    # Every 1000 mini-batches...
-            print('Batch {}'.format(i + 1))
+        if i % 1000 == 999:  # Every 1000 mini-batches...
+            print("Batch {}".format(i + 1))
             # Check against the validation set
             running_vloss = 0.0
-            
+
             # In evaluation mode some model specific operations can be omitted eg. dropout layer
-            net.train(False) # Switching to evaluation mode, eg. turning off regularisation
+            net.train(
+                False
+            )  # Switching to evaluation mode, eg. turning off regularisation
             for j, vdata in enumerate(validation_loader, 0):
                 vinputs, vlabels = vdata
                 voutputs = net(vinputs)
                 vloss = criterion(voutputs, vlabels)
                 running_vloss += vloss.item()
-            net.train(True) # Switching back to training mode, eg. turning on regularisation
-            
+            net.train(
+                True
+            )  # Switching back to training mode, eg. turning on regularisation
+
             avg_loss = running_loss / 1000
             avg_vloss = running_vloss / len(validation_loader)
-            
+
             # Log the running loss averaged per batch
-            writer.add_scalars('Training vs. Validation Loss',
-                            { 'Training' : avg_loss, 'Validation' : avg_vloss },
-                            epoch * len(training_loader) + i)
+            writer.add_scalars(
+                "Training vs. Validation Loss",
+                {"Training": avg_loss, "Validation": avg_vloss},
+                epoch * len(training_loader) + i,
+            )
 
             running_loss = 0.0
-print('Finished Training')
+print("Finished Training")
 
 writer.flush()
 
 
 #########################################################################
 # Switch to your open TensorBoard and have a look at the SCALARS tab.
-# 
+#
 # Visualizing Your Model
 # ----------------------
-# 
+#
 # TensorBoard can also be used to examine the data flow within your model.
 # To do this, call the ``add_graph()`` method with a model and sample
 # input:
-# 
+#
 
 # Again, grab a single mini-batch of images
 dataiter = iter(training_loader)
@@ -266,20 +274,21 @@ def forward(self, x):
 # When you switch over to TensorBoard, you should see a GRAPHS tab.
 # Double-click the “NET” node to see the layers and data flow within your
 # model.
-# 
+#
 # Visualizing Your Dataset with Embeddings
 # ----------------------------------------
-# 
+#
 # The 28-by-28 image tiles we’re using can be modeled as 784-dimensional
 # vectors (28 \* 28 = 784). It can be instructive to project this to a
 # lower-dimensional representation. The ``add_embedding()`` method will
 # project a set of data onto the three dimensions with highest variance,
 # and display them as an interactive 3D chart. The ``add_embedding()``
 # method does this automatically by projecting to the three dimensions
 # with highest variance.
-# 
+#
 # Below, we’ll take a sample of our data, and generate such an embedding:
-# 
+#
+
 
 # Select a random subset of data and corresponding labels
 def select_n_random(data, labels, n=100):
@@ -288,6 +297,7 @@ def select_n_random(data, labels, n=100):
     perm = torch.randperm(len(data))
     return data[perm][:n], labels[perm][:n]
 
+
 # Extract a random subset of data
 images, labels = select_n_random(training_set.data, training_set.targets)
 
@@ -296,9 +306,7 @@ def select_n_random(data, labels, n=100):
 
 # log embeddings
 features = images.view(-1, 28 * 28)
-writer.add_embedding(features,
-                    metadata=class_labels,
-                    label_img=images.unsqueeze(1))
+writer.add_embedding(features, metadata=class_labels, label_img=images.unsqueeze(1))
 writer.flush()
 writer.close()
 
@@ -309,19 +317,19 @@ def select_n_random(data, labels, n=100):
 # zoom the model. Examine it at large and small scales, and see whether
 # you can spot patterns in the projected data and the clustering of
 # labels.
-# 
+#
 # For better visibility, it’s recommended to:
-# 
+#
 # - Select “label” from the “Color by” drop-down on the left.
 # - Toggle the Night Mode icon along the top to place the
 #   light-colored images on a dark background.
-# 
+#
 # Other Resources
 # ---------------
-# 
+#
 # For more information, have a look at:
-# 
+#
 # - PyTorch documentation on `torch.utils.tensorboard.SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html?highlight=summarywriter>`__
-# - Tensorboard tutorial content in the `PyTorch.org Tutorials <https://pytorch.org/tutorials/>`__ 
+# - Tensorboard tutorial content in the `PyTorch.org Tutorials <https://pytorch.org/tutorials/>`__
 # - For more information about TensorBoard, see the `TensorBoard
 #   documentation <https://www.tensorflow.org/tensorboard>`__