import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from tensorflow import keras from tensorflow.keras import layers

fashion_mnist = tf.keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

X_train = train_images/255 X_test = test_images/255 y_train = train_labels y_test = test_labels

model = tf.keras.Sequential([ layers.Flatten(input_shape=(28,28)), layers.Dense(128, activation='relu'), layers.Dense(10) ])

opt = tf.optimizers.Adam() model.compile(optimizer=opt, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics= ['accuracy'])

model.fit(X_train, y_train, epochs=10)

model.evaluate(X_test, y_test, verbose=2)