import tensorflow as tf
print 'clothes exercise 4'
print(tf.__version__)

mnist = tf.keras.datasets.fashion_mnist

(training_images, training_labels) ,  (test_images, test_labels) = mnist.load_data()

training_images = training_images/255.0
test_images = test_images/255.0

model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
                                    tf.keras.layers.Dense(64, activation=tf.nn.relu),
                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])

# Replace the above model definiton with this one to see the network with 5 output layers
# And you'll see errors as a result!
# model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
#                                    tf.keras.layers.Dense(64, activation=tf.nn.relu),
#                                    tf.keras.layers.Dense(5, activation=tf.nn.softmax)])

model.compile(optimizer = 'adam',
              loss = 'sparse_categorical_crossentropy')

model.fit(training_images, training_labels, epochs=5)

model.evaluate(test_images, test_labels)

classifications = model.predict(test_images)

print(classifications[0])
print(test_labels[0])