"""
Created on Mon Jun 29 13:30:00 2201

@author: prbpedro
"""

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

print(tf.__version__)

fashion_mnist = 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.shape

len(train_labels)

train_labels

test_images.shape

len(test_images)

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

print(train_images.max())
print(train_images.min())

train_images = train_images/ 255
test_images = test_images/ 255

print(train_images.max())
print(train_images.min())

plt.figure(figsize=(10,10))
for i in range(25):
  plt.subplot(5,5,i+1)
  plt.xticks([])
  plt.yticks([])
  plt.imshow(train_images[i], cmap=plt.cm.binary)
  plt.xlabel(class_names[train_labels[i]])
  plt.grid(False)
plt.show()

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=10)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('Test accuracy:', test_acc)
print('Test loss:', test_loss)

predictions = model.predict(test_images)

np.argmax(predictions[0])

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

  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.xticks([x for x in range(10)])
  plt.yticks([0, predictions_array.max()])
  plt.grid(False)
  bar_plot = plt.bar(range(10), predictions_array, color='#777777')
  plt.ylim([0,1])
  predicted_label = np.argmax(predictions_array)
  bar_plot[predicted_label].set_color('red')
  bar_plot[true_label].set_color('blue')

i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.tight_layout()
plt.show()

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.tight_layout()
plt.show()

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.figure(figsize=(6,3))
  plt.subplot(1,2,1)
  plot_image(i, predictions, test_labels, test_images)
  plt.subplot(1,2,2)
  plot_value_array(i, predictions, test_labels)
  plt.tight_layout()
  plt.show()

img = test_images[0]
print(img.shape)

img = (np.expand_dims(img,0))
print(img.shape)

prediction_single = model.predict(img)

plot_value_array(0, prediction_single, test_labels)

np.argmax(prediction_single[0])

plot_image(0, prediction_single, test_labels, img)