How can I iterate over the test Dataset and show the image from the test dataset and then give its predictions

Question

I am relatively new to TensorFlow so I have made a model which is used to give predictions on different types of images of cars. I have made the Test Dataset from "tf.keras.utils.image_dataset_from_directory" function. I have used model.fit(test_dataset) to get the predictions. But what I want is to print the image from the test dataset and then give its predictions. (Image and then prediction). So that I can see which image is mapped to which prediction. Is there a way to do this?

Answer 1

For showing images of the test dataset and label and name of the class, you can show each image then from model.prdict() get a label and if you have the name of each label show name of each class like below: (I use this explanation in the example below code, the result of test images with 67% accuracy are getting):

import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

train, test = tfds.load(
    'cifar10',
    shuffle_files=True, 
    as_supervised=True, 
    split = ['train', 'test']
)
    
train = train.map(lambda x,y : (tf.cast(x, tf.float32) / 255.0, y) , num_parallel_calls=tf.data.AUTOTUNE)
test  = test.map(lambda x,y : (tf.cast(x, tf.float32) / 255.0, y) , num_parallel_calls=tf.data.AUTOTUNE)


train = train.batch(10).prefetch(tf.data.AUTOTUNE)
test = test.batch(10).prefetch(tf.data.AUTOTUNE)


model = tf.keras.Sequential([
  tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(32, 32, 3)),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(64, activation='relu'),
  tf.keras.layers.Dropout(0.4),
  tf.keras.layers.Dense(10)
])

model.compile(optimizer='adam', metrics=['accuracy'],
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))


model.fit(train,epochs=10)


class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
image ,label = next(iter(test))
fig, axes = plt.subplots(2,5,figsize=(15,6))
for idx, axe in enumerate(axes.flatten()):
    axe.axis('off')
    y_pred = np.argmax(model.predict(image[idx][None,...]))
    axe.imshow(image[idx])
    axe.set_title(f'label: {y_pred}, predict : {class_names[y_pred]}')

Output:

Epoch 1/10
5000/5000 [==============================] - 43s 5ms/step - loss: 1.5802 - accuracy: 0.4197
Epoch 2/10
5000/5000 [==============================] - 17s 3ms/step - loss: 1.2857 - accuracy: 0.5396
Epoch 3/10
5000/5000 [==============================] - 17s 3ms/step - loss: 1.1738 - accuracy: 0.5824
Epoch 4/10
5000/5000 [==============================] - 17s 3ms/step - loss: 1.1138 - accuracy: 0.6031
Epoch 5/10
5000/5000 [==============================] - 18s 4ms/step - loss: 1.0666 - accuracy: 0.6181
Epoch 6/10
5000/5000 [==============================] - 19s 4ms/step - loss: 1.0243 - accuracy: 0.6338
Epoch 7/10
5000/5000 [==============================] - 18s 4ms/step - loss: 0.9942 - accuracy: 0.6428
Epoch 8/10
5000/5000 [==============================] - 18s 4ms/step - loss: 0.9672 - accuracy: 0.6519
Epoch 9/10
5000/5000 [==============================] - 18s 4ms/step - loss: 0.9428 - accuracy: 0.6605
Epoch 10/10
5000/5000 [==============================] - 18s 4ms/step - loss: 0.9236 - accuracy: 0.6640