一、二分类训练MNIST数据集练习
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata("MNIST original", data_home='MNIST_data/')
X = mnist['data']
y = mnist['target']
digit = X[36000]
digit_image = digit.reshape(28,28)
def plot_digit(data):
image = data.reshape(28, 28)
plt.imshow(image, cmap = matplotlib.cm.binary, interpolation="nearest")
plt.axis("off")
def plot_digits(instances, images_per_row=10, **options):
size = 28
images_per_row = min(len(instances), images_per_row)
images = [instance.reshape(size,size) for instance in instances]
n_rows = (len(instances) - 1) // images_per_row + 1
row_images = []
n_empty = n_rows * images_per_row - len(instances)
init_image = np.zeros((size, size * n_empty))
images.append(init_image)
for row in range(n_rows):
rimages = images[row * images_per_row : (row + 1) * images_per_row]
row_images.append(np.concatenate(rimages, axis=1))
image = np.concatenate(row_images, axis=0)
plt.imshow(image, cmap = matplotlib.cm.binary, **options)
plt.axis("off")
plt.figure(figsize=(9,9))
example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]
plot_digits(example_images, images_per_row=10)
