import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split [] from keras.datasets import fashion_mnist (train_X, train_y), (test_X, test y) = fashion_mnist.load_data() fig = plt.figure(figsize=(10,7)) for i in range (15): ax fig.add_subplot (3, 5, i+1) ax.imshow (train_X[i], cmap= 'viridis') ax.set_title('Label (y): {y}'.format(y=train_y[i])) plt.axis ('off') Label (y): 9 Label (y): 2 Label (y): 0 Label (y): 3 TIXX Label (y): 0 Label (y): 7 Label (y): 2 Label (y): 5 Label (y): 0 Label (y): 5

import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split [] from keras.datasets import fashion_mnist (train_X, train_y), (test_X, test y) = fashion_mnist.load_data() fig = plt.figure(figsize=(10,7)) for i in range (15): ax fig.add_subplot (3, 5, i+1) ax.imshow (train_X[i], cmap= 'viridis') ax.set_title('Label (y): {y}'.format(y=train_y[i])) plt.axis ('off') Label (y): 9 Label (y): 2 Label (y): 0 Label (y): 3 TIXX Label (y): 0 Label (y): 7 Label (y): 2 Label (y): 5 Label (y): 0 Label (y): 5

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

Representation of Polynomial

It is necessary for the polynomial expression that each expression consists of two parts:

Question

Please implement Multinomial Logistic Regression on the following data. Please continue from the given code:

E
R
x}
>
Label (y): 2
Label (y): 0
[ ] train_X.shape
(60000, 28, 28)
Label (y): 7
Label (y): 9
A
Label (y): 2
Label (y): 5
Label (y): 5
Label (y): 5
A A
Label (y): 7
Label (y): 5

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
[] from keras.datasets import fashion_mnist
(train_X, train_y), (test_X, test_y) = fashion_mnist.load_data()
fig = plt.figure(figsize=(10,7))
for i in range (15):
ax fig.add_subplot (3, 5, i+1)
ax.imshow (train_X[i], cmap= 'viridis')
ax.set_title('Label (y): {y}'.format(y=train_y[i]))
plt.axis ('off')
Label (y): 9
Label (y): 2
Label (y): 0
Label (y): 7
Label (y): 0
Label (y): 2
Label (y): 3
Label (y): 5
Label (y): 0
Label (y): 5

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