# sequential model with two dense layers,
# A Sequential model in TensorFlow is a linear stack of neural network
# layers, where each layer is added one at a time.
# Different types of layers, such as Dense layers (also called fully connected
# layers), Convolutional layers, Pooling layers, and Recurrent layers can be
# added. Also the activation function and other parameters for each layer can
# be configured.

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

# Load the MNIST Fashion dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()

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

# print the shape of the numpy arrays
print ('Print shape of pixel data')
print(x_train.shape)
print ('Print shape of labels')
print(y_train.shape)



# Normalize pixel values to between 0 and 1
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# choose an image num  to display and print
num = 20

image = x_train[num]
label = y_train[num]

print ('Print normailzed pixel data of image ',num, ' :')
print(x_train[num])
print ('Print label of image ',num ,  ' :' )
print(y_train[num])


plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[y_train[i]])
plt.show()