def show_fashion_mnist(images, labels):
d2l.use_svg_display()
# Here _ means that we ignore (not use) variables
_, figs = d2l.plt.subplots(1, len(images), figsize=(12, 12))
for f, img, lbl in zip(figs, images, labels):
f.imshow(img.reshape((28, 28)).numpy())
f.set_title(lbl)
f.axes.get_xaxis().set_visible(False)
f.axes.get_yaxis().set_visible(False)
def __init__(self, xlabel=None, ylabel=None, legend=[], xlim=None,
ylim=None, xscale='linear', yscale='linear', fmts=None,
nrows=1, ncols=1, figsize=(3.5, 2.5)):
"""Incrementally plot multiple lines."""
d2l.use_svg_display()
self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize)
if nrows * ncols == 1: self.axes = [self.axes,]
# use a lambda to capture arguments
self.config_axes = lambda : d2l.set_axes(
self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
self.X, self.Y, self.fmts = None, None, fmts
import d2l
from matplotlib import pyplot as plt
from mxnet import npx, gluon
npx.set_np()
d2l.use_svg_display()
mnist_train = gluon.data.vision.FashionMNIST(train=True)
mnist_test = gluon.data.vision.FashionMNIST(train=False)
print("train length : {}, test length: {}".format(len(mnist_train),
len(mnist_test)))
X, y = mnist_train[:18]
# d2l.show_images(X.squeeze(axis=-1), 2, 9, titles=d2l.get_fashion_mnist_labels(y))
# plt.show()
batch_size = 256
transformer = gluon.data.vision.transforms.ToTensor()
train_iter = gluon.data.DataLoader(mnist_train.transform_first(transformer),
batch_size,
shuffle=True,
num_workers=d2l.get_dataloader_workers())
timer = d2l.Timer()
for X, y in train_iter:
continue
print("loading dada takes {:.2f} sec".format(timer.stop()))
train_iter, test_iter = d2l.load_data_fashion_mnist(32, (64, 64))
