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Progress Update 3

Workshop Resources
Pre-requisite: Python Basics: Functions- Built in Functions, Functions from libraries; Data Types- Strings, Numbers, Reading from Console; Data Structures- Lists, Tuples, Sets; Loops- For Loops
Difficulty: Intermediate

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To verify that your code is correct up to this checkpoint, please compare your code against the example code provided below.

The outputs of the code may vary. You only need to copy the code blocks, and not the output blocks, as they will be generated automatically in your notebook.

Compare Your Code

Before moving on, please check your Google Colab notebook against the code below:

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')

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()

Plotting Figure 1

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()

Plotting Figure 2

#Plotting 6 images
num_rows = 3
num_cols = 2
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()

Plotting 6 images

#Plotting 25 images
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):
  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()

Plotting 25 images

# Grab an image from the test dataset. This shows the resolution of the image. 

#NOTE: This index will be changed and its corresponding plot would be displayed in the next few steps
img = test_images[7]

print(img.shape)

(28, 28)

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

print(img.shape)

(1, 28, 28)

predictions_single = probability_model.predict(img)

print(predictions_single)

[[3.5166083e-06 5.8611553e-12 7.3947426e-04 3.9665074e-06 2.8206140e-03
  9.7541879e-08 9.9643230e-01 8.2940162e-11 1.2411914e-07 2.7266043e-09]]

plot_value_array(7, predictions_single[0], test_labels)  #plot the graph containing all the class names
_ = plt.xticks(range(10), class_names, rotation=45)

Classification Plot

np.argmax(predictions_single[0]) #Verifying the index value with highest probability

6

i = 7   #We can see that this image detects the right class name for the image
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()

Image and Graph

If you would like to test the code above, visit this link

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