from random import randint

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

'''
Label	Class
0	    T-shirt/top
1	    Trouser
2	    Pullover
3	    Dress
4	    Coat
5	    Sandal
6	    Shirt
7	    Sneaker
8	    Bag
9	    Ankle boot
'''

fashion_mnist = tf.keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.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

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    # tf.keras.layers.Dense(64, activation='relu'),
    # tf.keras.layers.Dense(32, activation='relu'),
    # tf.keras.layers.Dense(16, activation='relu'),
    # tf.keras.layers.Dropout(0.2),
    tf.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)


def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array, true_label[i], img[i]
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)

    predicted_label = np.argmax(predictions_array)
    if predicted_label == true_label:
        color = 'blue'
    else:
        color = 'red'

    plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                         100 * np.max(predictions_array),
                                         class_names[true_label]),
               color=color)


def plot_value_array(i, predictions_array, true_label):
    predictions_array, true_label = predictions_array, true_label[i]
    plt.grid(False)
    plt.xticks(range(10))
    plt.yticks([])
    thisplot = plt.bar(range(10), predictions_array, color="#777777")
    plt.ylim([0, 1])
    predicted_label = np.argmax(predictions_array)

    thisplot[predicted_label].set_color('red')
    thisplot[true_label].set_color('blue')


# num_rows = 5
# num_cols = 3
# num_images = num_rows*num_cols
# plt.figure(figsize=(2*2*num_cols, 2*num_rows))
# for i in range(num_images):
#   plt.subplot(num_rows, 2*num_cols, 2*i+1)
#   plot_image(i, predictions[i], test_labels, test_images)
#   plt.subplot(num_rows, 2*num_cols, 2*i+2)
#   plot_value_array(i, predictions[i], test_labels)
# plt.tight_layout()
# plt.show()

for _ in range(10):
    i = randint(1, 10000)
    img = test_images[i]
    img = (np.expand_dims(img, 0))
    predictions_single = probability_model.predict(img)
    plt.figure(figsize=(6, 3))
    plt.subplot(1, 2, 1)
    plot_image(i, predictions_single[0], test_labels, test_images)
    plt.subplot(1, 2, 2)
    plot_value_array(i, predictions_single[0], test_labels)
    plt.show()
