Plot image classification results

flow.py

@@ -49,3 +49,56 @@ model.fit(train_images, train_labels, epochs=5)
 test_loss, test_acc = model.evaluate(test_images, test_labels)
 
 print("Test accuracy:", test_acc)
+
+predictions = model.predict(test_images)
+
+
+def plot_image(i, predictions_array, true_label, img):
+    predictions_array, true_label, img = predictions_array[i], 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):
+    predictions_array, true_label = predictions_array[i], true_label[i]
+    plt.grid(False)
+    plt.xticks([])
+    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")
+
+
+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):
+    image_idx = random.randint(0, len(test_images) - 1)
+    plt.subplot(num_rows, 2 * num_cols, 2 * i + 1)
+    plot_image(image_idx, predictions, test_labels, test_images)
+    plt.subplot(num_rows, 2 * num_cols, 2 * i + 2)
+    plot_value_array(image_idx, predictions, test_labels)
+
+plt.show()