fashion-mnist-test-check

main.py

import numpy as np
np.random.seed(1000)

from matplotlib import pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
sns.set_palette((sns.color_palette('colorblind', 8)))
dims = (11.7, 8.27)
%matplotlib inline

import math
import random

def int_to_desc(i):
    conv = {0: 'T-shirt/top', 1: 'Trouser', 2: 'Pullover', 3: 'Dress', 4: 'Coat', 5: 'Sandal',
            6: 'Shirt', 7: 'Sneaker', 8: 'Bag', 9: 'Ankle boot'}
    try:
        ret = conv[i]
    except:
        ret = 'Unknown'
    return ret

def check_random(n, x, y, p):
    rows = math.ceil(n/5)
    fig, ax = plt.subplots(nrows=rows, ncols=5, sharex=True, sharey=True,)
    ax = ax.flatten()
    for i in range(n):
        j = random.randint(0,len(p)-1)
        img = x[j].reshape(28, 28)
        ax[i].imshow(img, cmap='Greys', interpolation='nearest')
        predicted = int_to_desc(p[j])
        actual = int_to_desc(y[j])
        ax[i].set_title('P: {}\n A: {}'.format(predicted,actual))
    ax[0].set_xticks([])
    ax[0].set_yticks([])
    plt.tight_layout()
    plt.show()


## N = number of random items to pull
## x = your x test set
## y = the y test set
## p = the results from the prediction function
check_random(n=15, x=X_test, y=ytest_flat, p=pred_flat)

Sample output: