test

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‘]
# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.show()

train_images = train_images / 255.0
test_images = test_images / 255.0

print("---------------------------------------------------------------------")
# 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(train_images[i], cmap=plt.cm.binary)
#     plt.xlabel(class_names[train_labels[i]])
# plt.show()


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


model.compile(optimizer=‘adam‘,
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=[‘accuracy‘])


model.fit(train_images, train_labels, epochs=10)


test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print(‘\nTest accuracy:‘, test_acc)

probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])

predictions = probability_model.predict(test_images)

num = 2

print(predictions[num])

print(np.argmax(predictions[num]))

print("actual: ", class_names[test_labels[num]])
print("prediction: ", class_names[np.argmax(predictions[num])])

plt.figure()
plt.imshow(test_images[num], cmap=plt.cm.binary)
plt.show()