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