fashionMNIST.py

#This is classificataion example
import tensorflow as tf 
import tensorflow_datasets as tfds

# Helper libraries
import math
import numpy as np
import matplotlib.pyplot as plt

# Improve progress bar display
import tqdm
import tqdm.auto
tqdm.tqdm = tqdm.auto.tqdm

print(tf.__version__)

# This will go away in the future.
# If this gives an error, you might be running TensorFlow 2 or above
# If so, the just comment out this line and run this cell again

dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)
train_dataset, test_dataset = dataset['train'], dataset['test']

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

num_train_examples = metadata.splits['train'].num_examples
num_test_examples = metadata.splits['test'].num_examples
print("Number of training examples: {}".format(num_train_examples))
print("Number of test examples:     {}".format(num_test_examples))

def normalize(images, labels):
  images = tf.cast(images, tf.float32)
  images /= 255
  return images, labels

# The map function applies the normalize function to each element in the train
# and test datasets
train_dataset =  train_dataset.map(normalize)
test_dataset  =  test_dataset.map(normalize)


model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),   # 28 and 28 are width and height (pixels) . 1 is the colour channel (greyscale)
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(10,  activation=tf.nn.softmax)
])

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

BATCH_SIZE = 32
train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)

model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))