def show_fashion_mnist(images, labels):
d2l.use_svg_display()
_, 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)).asnumpy())
f.set_title(lbl)
f.axes.get_xaxis().set_visible(False)
f.axes.get_yaxis().set_visible(False)
def show_fashion_mnist(images, labels):
d2l.use_svg_display() # Use svg format to display plo
_, figs = d2l.plt.subplots(1, len(images), figsize=(12,12)) # _表示忽略 不使用该返回值
for f, img, lbl in zip(figs, images, labels): # 从压缩文件中直接读出数据（图像，标签）
f.imshow(img.reshape((28,28)).asnumpy())
f.set_title(lbl) # 显示标签
f.axes.get_xaxis().set_visible(False) # 不显示坐标刻度X
f.axes.get_yaxis().set_visible(False) # 不显示坐标刻度Y
d2l.plt.show() # Pycharm中没有show()不会出现图片的
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
from mxnet.gluon import data as gdata
import d2lzh as d2l
from mxnet.gluon import nn
import matplotlib
from mxnet import init, autograd
from mxnet import gluon
train_data = gdata.vision.FashionMNIST(train=True)
test_data = gdata.vision.FashionMNIST(train=False)
batch_size = 256
data_iter = gdata.DataLoader(train_data, batch_size=batch_size, shuffle=True)
d2l.use_svg_display()
images, labels = train_data[0:9]
def get_fashionmnist_label(labels):
texts = [
't-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt',
'sneaker', 'bag', 'ankle boot'
]
return [texts[int(i)] for i in labels]
def show_fashion_mnist(images, labels):
_, figs = d2l.plt.subplots(1, len(images), figsize=(12, 12))
for f, image, label in zip(figs, images, get_fashionmnist_label(labels)):
f.imshow(image.reshape((28, 28)).asnumpy())
f.set_title(label)
f.axes.get_xaxis().set_visible(False)
f.axes.get_yaxis().set_visible(False)
matplotlib.pyplot.show()