predict_anomaly.autoencoder.py

%matplotlib inline
from keras.preprocessing import image
# if the img.png is not one of the MNIST dataset that the model was trained on, the error will be very high.
img = image.load_img("./img.png", target_size=(28, 28), color_mode = "grayscale")
input_img = image.img_to_array(img)
inputs = input_img.reshape(1,784)
target_data = autoencoder.predict(inputs)
dist = np.linalg.norm(inputs - target_data, axis=-1)
print(dist)