tr_loss.result().numpy(), "val_loss:",
va_loss.result().numpy())
# Save the model for each epoch
model.save_weights(filepath=model_name, save_format='tf')
elif sys.argv[1] == "predict":
# Step 3: Loads the weights
model.load_weights(model_name)
my_model = tf.keras.Sequential([model])
# Step 4: Prepare the input
img = cv2.imread(sys.argv[2])
image = cv2.resize(img, (height, width), interpolation=cv2.INTER_AREA)
images = np.array([image])
images = loader.scaling_tech(images, method="normalization")
# Step 5: Predict the class
preds = my_model.predict(images)
preds = (preds[0] - preds.min()) / (preds.max() - preds.min())
images = np.hstack((images[0], preds))
images = cv2.resize(images, (width * 4, height * 2))
cv2.imshow("imgs", images)
cv2.waitKey(0)
elif sys.argv[1] == "predict_all":
# Step 3: Loads the weights
model.load_weights(model_name)
my_model = tf.keras.Sequential([model])
# Step 4: Prepare the input
print(epoch, "/", epochs, step, steps, "loss:",
tr_loss.result().numpy(), "val_loss:",
va_loss.result().numpy())
# Save the model for each epoch
model.save_weights(filepath=model_name, save_format='tf')
elif sys.argv[1] == "predict":
# Step 3: Loads the weights
model.load_weights(model_name)
my_model = tf.keras.Sequential([model])
# Step 4: Prepare the input
img = cv2.imread(sys.argv[2])
image = cv2.resize(img, (height, width), interpolation=cv2.INTER_AREA)
image = loader.scaling_tech(image, method="normalization")
images = []
origin = []
for _ in range(5):
k = np.random.choice([3, 5, 7, 9, 11, 13, 15, 17])
j = np.random.randint(1, 90)
res = cv2.GaussianBlur(image, (k, k), j)
images.append(res)
origin.append(image)
# True images
images = np.array(images)
origin = np.array(origin)
# Step 5: Predict the class
preds = my_model.predict(images)
