Question

functions loadMNISTImages and loadMNISTLabels to read in the training/testing set and label files Function loadMNISTImages will return an array of 784 rows, each column contains pixel values of an image. Use the following code to convert labels into a 10-row array, each column represents one digit: labels = loadMNISTLabels('training_label'); % initialize figure labels = labels'; labels(labels==0)=10; labels=dummyvar(labels); .

Answer 1

import tensorflow as tf

print(tf.__version__)

mnist = tf.keras.datasets.fashion_mnist

(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

import numpy as np

np.set_printoptions(linewidth=200)

import matplotlib.pyplot as plt

plt.imshow(training_images[0])

print(training_labels[0])

print(training_images[0])

training_images  = training_images / 255.0

test_images = test_images / 255.0

model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),

                                    tf.keras.layers.Dense(128, activation=tf.nn.relu),

                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])

model.compile(optimizer = tf.optimizers.Adam(),

              loss = 'sparse_categorical_crossentropy',

              metrics=['accuracy'])

model.fit(training_images, training_labels, epochs=5)

model.evaluate(test_images, test_labels)

classifications = model.predict(test_images)

print(classifications[0])

print(test_labels[0])