hzitoun

loaded_model = tf.keras.models.load_model('../models/saved_image_classifier/')
loss, acc = loaded_model.evaluate(X_test,  y_test, verbose=0)
print('Accuracy: {:5.2f}%'.format(100*acc))

hzitoun

model.save('../models/saved_image_classifier/')

hzitoun

test_image =  read_image("../data/raw/test/82_automobile.png", image_size) 
plt.imshow(test_image)
plt.title('test image')
plt.show()

pred_softmax = model.predict(test_image.reshape(1, image_size[0], image_size[1], 3))
pred_class_index = np.argmax(pred_softmax, axis=1)

target_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
print("CNN sees", target_names[pred_class_index.item()])

hzitoun

deer = read_image("../data/raw/train/3_deer.png", (32, 32))
car =  read_image("../data/raw/train/4_automobile.png", (32, 32)) 
bird =  read_image("../data/raw/train/18_bird.png", (32, 32)) 

plt.subplot(131)
plt.imshow(deer)
plt.title('Deer')
plt.subplot(132)
plt.imshow(car)
plt.title('Car')

hzitoun

name: tf2-image-classifier-api
channels:
  - anaconda
  - conda-forge
  - defaults
dependencies:
  - pip==20.1
  - numpy==1.18.4
  - flask==1.1.2
  - matplotlib==3.2.1

hzitoun

def read_image(image_path, image_size):
    """ read an image using opencv """
    
    image = cv2.imread(image_path) #BGR
    # convert to RGB
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    # resize using cubic interpolation, a way to deal with missing data (upsize or downsize)
    image = cv2.resize(image, image_size, cv2.INTER_CUBIC) 
    
    return image