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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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原文链接：https://blog.csdn.net/qq_28368377/article/details/105588250