fashion.py

import numpy as np
import matplotlib.pyplot as plt

import tensorflow as tf

# print(tf.__version__)

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']

# print(train_images.shape)

# 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

# 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 = tf.keras.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  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)
print(predictions[0])

np.argmax(predictions[0])

def plot_image(i, predictions_array, true_label, img):
  true_label, img = 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):
  true_label = 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')

# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and incorrect predictions in red.
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()

# Grab an image from the test dataset.
img = test_images[1]

print(img.shape)