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vizualization.py
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vizualization.py
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# import torch
# import torch.nn as nn
# import torchvision.datasets as dsets
# import torchvision.transforms as transforms
# from torch.autograd import Variable
#
# from cnn import CNN
# from matplotlib import pyplot as plt
# import torchvision
#
# # Hyper Parameters
# batch_size = 25
#
# # MNIST Dataset
# test_dataset = dsets.MNIST(root='./data/',
# train=False,
# transform=transforms.ToTensor())
#
# # Data Loader (Input Pipeline)
# test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
# batch_size=batch_size,
# shuffle=False)
#
#
# def visualize_model(model, num_images):
# images_so_far = 0
# fig = plt.figure()
#
# for i, data in enumerate(test_loader):
# images, labels = data
#
# inputs = Variable(images)
# labels = Variable(labels)
#
# outputs = model(inputs)
# _, preds = torch.max(outputs.data, 1)
#
# for j in range(num_images):
# ax = plt.subplot(num_images//5, 5, j+1)
# ax.axis('off')
# ax.set_title('predicted: {}'.format(str(preds[j])))
# img = inputs.data[j,0].numpy()
# ax.imshow(img)
#
# plt.show()
#
# cnn = CNN()
# cnn.load_state_dict(torch.load('cnn.pth'))
# cnn.eval()
#
# visualize_model(cnn, 25)