Assignment for Machine Learning Here is the example code for chapter 3 from Jupy

Question

Assignment for Machine Learning
Here is the example code for chapter 3 from Jupyter notebook.
*******code********
from sklearn.datasets import fetch_mldata mnist = fetch_mldata('MNIST original') mnist
X, y = mnist["data"], mnist["target"] X.shape
y.shape
28*28
%matplotlib inline import matplotlib import matplotlib.pyplot as plt some_digit = X[36000] some_digit_image = some_digit.reshape(28, 28) plt.imshow(some_digit_image, cmap = matplotlib.cm.binary, interpolation="nearest") plt.axis("off") save_fig("some_digit_plot") plt.show()

def plot_digit(data): image = data.reshape(28, 28) plt.imshow(image, cmap = matplotlib.cm.binary, interpolation="nearest") plt.axis("off")

# EXTRA 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) images.append(np.zeros((size, size * n_empty))) 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) save_fig("more_digits_plot") plt.show()

y[36000]

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

import numpy as np shuffle_index = np.random.permutation(60000) X_train, y_train = X_train[shuffle_index], y_train[shuffle_index] from sklearn.datasets import fetch_mldata mnist = fetch_mldata('MNIST original') mnist
X, y = mnist["data"], mnist["target"] X.shape
y.shape
28*28
%matplotlib inline import matplotlib import matplotlib.pyplot as plt some_digit = X[36000] some_digit_image = some_digit.reshape(28, 28) plt.imshow(some_digit_image, cmap = matplotlib.cm.binary, interpolation="nearest") plt.axis("off") save_fig("some_digit_plot") plt.show()

def plot_digit(data): image = data.reshape(28, 28) plt.imshow(image, cmap = matplotlib.cm.binary, interpolation="nearest") plt.axis("off")

# EXTRA 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) images.append(np.zeros((size, size * n_empty))) 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) save_fig("more_digits_plot") plt.show()

y[36000]

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

import numpy as np shuffle_index = np.random.permutation(60000) X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]

Explanation / Answer

Answer :

For image shifting by one pixel in all directions

import scipy.ndimage.interpolation as ndi

# for shifting the image by one pixel down

ndi.shift(image,[1,0],cval=0)

#for shifting the image by one pixel in right

ndi.shift(image,[0,1],cval=0)

#for shifting the image by one pixel in the top

ndi.shift(image, [-1,0],cval=0)

#for shifting the image by one pixel towards left

ndi.shift(image,[0,-1],cval=0)

Small changes in the pixel value can reduce he accuracy of the prediction model by a large extent although the shifted images may be difficult to distinguish by eye.The testing set will be 4 times larger.The accuracy of the clasification algorithm will decrease .

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