bartubozkurt

public class LeapYearTests {

  private final LeapYear leapYear = new LeapYear();

  @Test
  public void divisibleBy4_notDivisibleBy100() {
    boolean leap = leapYear.isLeapYear(2016);
    assertTrue(leap);
  }

bartubozkurt

public class LeapYear {

  public boolean isLeapYear(int year) {
    if (year % 400 == 0)
      return true;
    if (year % 100 == 0)
      return false;

    return year % 4 == 0;
  }

bartubozkurt

plot_value_array(1, predictions_single[0], test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
plt.show()

bartubozkurt

predictions_single = probability_model.predict(img)

print(predictions_single
  '''
[[8.8914348e-05 1.3264636e-13 9.9108773e-01 1.2658383e-10 8.1463791e-03
  1.6905785e-08 6.7695131e-04 2.7492119e-17 5.1699739e-10 7.1339325e-17]]
  '''

bartubozkurt

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))

print(img.shape)

# (1, 28, 28)

bartubozkurt

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

print(img.shape)

# (28, 28)

bartubozkurt

# 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)

bartubozkurt

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

bartubozkurt

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

bartubozkurt

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: