MNIST_FASION_ANN.py

import tensorflow as tf
from tensorflow import keras

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

print(tf.__version__)

data = keras.datasets.fashion_mnist

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

train_images= train_images/255.0
test_images= test_images/255.0

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
#creating the neural network:-

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

])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics =['accuracy'])
# epochs influences the order of clothes the program sees
#it randomly pics an image and feeds

model.fit(train_images, train_labels, epochs=2);

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

print('Test Acc:', test_acc)

prediction = model.predict(test_images)
for i in range (5):
     plt.grid(False)
     plt.imshow(test_images[i],cmap=plt.cm.binary)
     plt.xlabel('Actual : ' + class_names[test_labels[i]])
     plt.title('Prediction '+ class_names[np.argmax(prediction[i])])
     plt.show()