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helper.py
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()
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