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

data=keras.datasets.fashion_mnist

(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
test_images = test_images/255.0

train_images.shape

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)

evaluate_images,evaluate_results=model.evaluate(test_images,test_labels)

print("Accuracy is:",evaluate_results)

predictions=model.predict(test_images)

print(predictions[5])

np.argmax(predictions[5])

plt.figure(figsize=(5,5))
for i in range(10):
    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()