refactor: Reorganize repository structure for better learning flow

intro-to-pytorch/helper.py → 1-Introduction to Neural Networks/intro-to-pytorch/helper.py

@@ -1,94 +1,94 @@
-import matplotlib.pyplot as plt
-import numpy as np
-from torch import nn, optim
-from torch.autograd import Variable
-
-
-def test_network(net, trainloader):
-
-    criterion = nn.MSELoss()
-    optimizer = optim.Adam(net.parameters(), lr=0.001)
-
-    dataiter = iter(trainloader)
-    images, labels = dataiter.next()
-
-    # Create Variables for the inputs and targets
-    inputs = Variable(images)
-    targets = Variable(images)
-
-    # Clear the gradients from all Variables
-    optimizer.zero_grad()
-
-    # Forward pass, then backward pass, then update weights
-    output = net.forward(inputs)
-    loss = criterion(output, targets)
-    loss.backward()
-    optimizer.step()
-
-    return True
-
-
-def imshow(image, ax=None, title=None, normalize=True):
-    """Imshow for Tensor."""
-    if ax is None:
-        fig, ax = plt.subplots()
-    image = image.numpy().transpose((1, 2, 0))
-
-    if normalize:
-        mean = np.array([0.485, 0.456, 0.406])
-        std = np.array([0.229, 0.224, 0.225])
-        image = std * image + mean
-        image = np.clip(image, 0, 1)
-
-    ax.imshow(image)
-    ax.spines['top'].set_visible(False)
-    ax.spines['right'].set_visible(False)
-    ax.spines['left'].set_visible(False)
-    ax.spines['bottom'].set_visible(False)
-    ax.tick_params(axis='both', length=0)
-    ax.set_xticklabels('')
-    ax.set_yticklabels('')
-
-    return ax
-
-
-def view_recon(img, recon):
-    ''' Function for displaying an image (as a PyTorch Tensor) and its
-        reconstruction also a PyTorch Tensor
-    '''
-
-    fig, axes = plt.subplots(ncols=2, sharex=True, sharey=True)
-    axes[0].imshow(img.numpy().squeeze())
-    axes[1].imshow(recon.data.numpy().squeeze())
-    for ax in axes:
-        ax.axis('off')
-        ax.set_adjustable('box-forced')
-
-def view_classify(img, ps, version="MNIST"):
-    ''' Function for viewing an image and it's predicted classes.
-    '''
-    ps = ps.data.numpy().squeeze()
-
-    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)
-    ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())
-    ax1.axis('off')
-    ax2.barh(np.arange(10), ps)
-    ax2.set_aspect(0.1)
-    ax2.set_yticks(np.arange(10))
-    if version == "MNIST":
-        ax2.set_yticklabels(np.arange(10))
-    elif version == "Fashion":
-        ax2.set_yticklabels(['T-shirt/top',
-                            'Trouser',
-                            'Pullover',
-                            'Dress',
-                            'Coat',
-                            'Sandal',
-                            'Shirt',
-                            'Sneaker',
-                            'Bag',
-                            'Ankle Boot'], size='small');
-    ax2.set_title('Class Probability')
-    ax2.set_xlim(0, 1.1)
-
-    plt.tight_layout()
+import matplotlib.pyplot as plt
+import numpy as np
+from torch import nn, optim
+from torch.autograd import Variable
+
+
+def test_network(net, trainloader):
+
+    criterion = nn.MSELoss()
+    optimizer = optim.Adam(net.parameters(), lr=0.001)
+
+    dataiter = iter(trainloader)
+    images, labels = dataiter.next()
+
+    # Create Variables for the inputs and targets
+    inputs = Variable(images)
+    targets = Variable(images)
+
+    # Clear the gradients from all Variables
+    optimizer.zero_grad()
+
+    # Forward pass, then backward pass, then update weights
+    output = net.forward(inputs)
+    loss = criterion(output, targets)
+    loss.backward()
+    optimizer.step()
+
+    return True
+
+
+def imshow(image, ax=None, title=None, normalize=True):
+    """Imshow for Tensor."""
+    if ax is None:
+        fig, ax = plt.subplots()
+    image = image.numpy().transpose((1, 2, 0))
+
+    if normalize:
+        mean = np.array([0.485, 0.456, 0.406])
+        std = np.array([0.229, 0.224, 0.225])
+        image = std * image + mean
+        image = np.clip(image, 0, 1)
+
+    ax.imshow(image)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    ax.spines['bottom'].set_visible(False)
+    ax.tick_params(axis='both', length=0)
+    ax.set_xticklabels('')
+    ax.set_yticklabels('')
+
+    return ax
+
+
+def view_recon(img, recon):
+    ''' Function for displaying an image (as a PyTorch Tensor) and its
+        reconstruction also a PyTorch Tensor
+    '''
+
+    fig, axes = plt.subplots(ncols=2, sharex=True, sharey=True)
+    axes[0].imshow(img.numpy().squeeze())
+    axes[1].imshow(recon.data.numpy().squeeze())
+    for ax in axes:
+        ax.axis('off')
+        ax.set_adjustable('box-forced')
+
+def view_classify(img, ps, version="MNIST"):
+    ''' Function for viewing an image and it's predicted classes.
+    '''
+    ps = ps.data.numpy().squeeze()
+
+    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)
+    ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())
+    ax1.axis('off')
+    ax2.barh(np.arange(10), ps)
+    ax2.set_aspect(0.1)
+    ax2.set_yticks(np.arange(10))
+    if version == "MNIST":
+        ax2.set_yticklabels(np.arange(10))
+    elif version == "Fashion":
+        ax2.set_yticklabels(['T-shirt/top',
+                            'Trouser',
+                            'Pullover',
+                            'Dress',
+                            'Coat',
+                            'Sandal',
+                            'Shirt',
+                            'Sneaker',
+                            'Bag',
+                            'Ankle Boot'], size='small');
+    ax2.set_title('Class Probability')
+    ax2.set_xlim(0, 1.1)
+
+    plt.tight_layout()