satishgunjal

i = 49
plt.figure(figsize=(12,6))
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.xticks(range(10), class_names, rotation=45)
plt.show()

satishgunjal

predictions_single = probability_model.predict(img)
# Remember that if we do "predictions = probability_model.predict(test_images)" then we get predictions for all test data"
print(f'Probabilty for all classes: {predictions_single}, \nBest confidence score for class: {np.argmax(predictions_single)}')

satishgunjal

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

satishgunjal

# Grab an image from the test dataset.
img = test_images[49]

print(img.shape)

satishgunjal

# Plot the test images from 'test_list', their predicted labels, and the true labels.
# Color correct predictions in green and incorrect predictions in red.

test_list= [16, 17, 22, 23, 24, 25, 39, 40, 41, 42, 48, 49, 50, 51]
num_rows = 7
num_cols = 2
num_images = num_rows * num_cols
#plt.figure(figsize=(2*2*num_cols, 2*num_rows))
plt.figure(figsize=(10,14))
for i in range(num_images):

satishgunjal

i = 12
plt.figure(figsize=(12,6))
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.xticks(range(10), class_names, rotation=45)
plt.show()

satishgunjal

def plot_value_array(i, predictions_array, true_label):
    """
    This method will plot the percentage confidence score of each class prediction.
    
    Input:
        i: Index of the prediction to test
        predictions_array: Every prediction contain array of 10 number
        true_label: Correct image labels. In case of test data they are test_labels
    """
    true_label = true_label[i]

satishgunjal

def plot_image(i, predictions_array, true_label, img):
    """
    This method will plot the true image and also compare the prediction with true values if matcing write the caption in green color else in red color.
    Format is : predicted class %confidence score (true class)
    
    Input:
        i: Index of the prediction to test
        predictions_array: Every prediction contain array of 10 number
        true_label: Correct image labels. In case of test data they are test_labels
        img: Test images. In case of test data they are test_images.

satishgunjal

test_labels[0]

satishgunjal

np.argmax(predictions[0])