completed deep learning section

python/Deep Learning/Deep Learning with PyTorch/helper.py

@@ -0,0 +1,95 @@
+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()