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

data = keras.datasets.fashion_mnist

# split in train and test data

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

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

train_images = train_images/255.0 #normalization of the values
test_images = test_images/255.0

# print(train_images[7])

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"])

model.fit(train_images,train_labels,epochs=5)

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

# print("Tested Acc: ",test_acc)

predictions = model.predict(test_images)

plt.figure(figsize=(5,5))
for i in range(5):
    plt.grid(False)
    plt.imshow(test_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[test_labels[i]])
    plt.title(class_names[np.argmax(predictions[i])])
    plt.show()