Type hints removed for better Python version compatibility

Chapter09/netEval.py

@@ -11,20 +11,29 @@ import os
 import glob
 import skimage.io
 import time
-from collections.abc import Callable
 
 from sampleNetwork import netFunc
 
-def netEval(netFunc: Callable[np.array], dataPath: str, targetPath: str) -> tuple[float,float]:
-    """
+def netEval(netFunc, dataPath, targetPath):
+    '''
     Evaluates the accuracy of a classification model using provided dataset.
     
     Parameters
     ----------
-    netFunc - function of the network that takes an image and outputs a predicted class,\n
-    dataPath - path to a folder with data,\n
-    targetPath - path to a file with target labels (either .txt or .npy).
-    """
+    netFunc : function
+        Function of the network that takes an image and outputs a predicted class
+    dataPath: string
+        Path to a folder with data
+    targetPath: string 
+        Path to a file with target labels (either .txt or .npy)
+    
+    Returns
+    -------
+    accuracy: float
+        Accuracy of the network on provided dataset
+    execTime:
+        Network prediction execution time
+    '''
     
     assert callable(netFunc), "The first argument is not callable, it should be a network function."
     assert os.path.exists(dataPath), f"Provided path: {dataPath} does not exist."

Chapter09/sampleNetwork.py

@@ -5,9 +5,8 @@ Created on Tue Mar 28 14:31:20 2023
 @author: Pawel Pieta, papi@dtu.dk
 """
 import numpy as np
-from collections.abc import Iterable
 
-def netFunc(images: np.array) -> Iterable[int]:
+def netFunc(images):
     '''
     Loads a feed forward neural network, and predicts the labels of each image in an array.
 
@@ -16,8 +15,8 @@ def netFunc(images: np.array) -> Iterable[int]:
     images : numpy array
         An array with grayscale images loaded with "skimage.io.imread(fname,as_gray=True)",
         float datatype with pixel values between 0 and 1. The array has \n
-        dimension N x H x W x C where N is the number of images, H is the height of the images \n
-        , W is the width of the images and C is the number of channels in each image.
+        dimension N x H x W x C where N is the number of images, H is the height of the images, \n
+        W is the width of the images and C is the number of channels in each image.
 
     Returns
     -------