#!/usr/bin/python3

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

import random

print("Running TensorFlow", tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist

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

train_images = train_images / 255.0
test_images = test_images / 255.0

class_names = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]

model = keras.Sequential(
    [
        keras.layers.Flatten(input_shape=(28, 28)),
        keras.layers.Dense(256, activation=tf.nn.relu),
        keras.layers.Dense(10, activation=tf.nn.softmax),
    ]
)

model.compile(
    optimizer=tf.train.AdamOptimizer(),
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"],
)


def plot_training(history):
    acc = history.history["acc"]
    val_acc = history.history["val_acc"]

    epochs = range(1, len(acc) + 1)

    plt.plot(epochs, acc, "bo", label="Training acc")
    plt.plot(epochs, val_acc, "b", label="Validation acc")
    plt.title("Training and validation accuracy")
    plt.xlabel("Epochs")
    plt.ylabel("Accuracy")
    plt.legend()

    plt.show()


early_stop = keras.callbacks.EarlyStopping(monitor="val_loss", patience=5)

history = model.fit(
    train_images,
    train_labels,
    epochs=64,
    batch_size=512,
    validation_data=(test_images, test_labels),
    callbacks=[early_stop],
)

plot_training(history)

predictions = model.predict(test_images)


def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array[i], 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[i], true_label[i]
    plt.grid(False)
    plt.xticks([])
    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 = 5
num_images = num_rows * num_cols
plt.figure(figsize=(2 * 2 * num_cols, 2 * num_rows))
for i in range(num_images):
    image_idx = random.randint(0, len(test_images) - 1)
    plt.subplot(num_rows, 2 * num_cols, 2 * i + 1)
    plot_image(image_idx, predictions, test_labels, test_images)
    plt.subplot(num_rows, 2 * num_cols, 2 * i + 2)
    plot_value_array(image_idx, predictions, test_labels)

plt.show()