masterclass1.py

import pandas as pd

pd.__version__

import keras

from keras.layers import Flatten, Dense

from keras.models import Sequential

model = Sequential([Flatten(input_shape=(28,28)), Dense(128, activation='relu'), Dense(10, activation='softmax')])

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['t-shirt','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle_boot']

train_images = train_images/255

test_imags = test_images/255

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print(f'\n Test Accuracy {test_acc}')