add config and start with the layers

Author: Tinusf

assignment1/Config.py

@@ -0,0 +1,38 @@
+class Config:
+    config = {
+        "training": None,
+        "validation": None,
+        "layers": None,
+        "activations": None,
+        "loss_type": None,
+        "learning_rate": None,
+        "no_epochs": None,
+        "L2_regularization": None
+    }
+
+    def __init__(self, file_path):
+        lines = self.read_file(file_path)
+        self.parse_config(lines)
+
+    def read_file(self, file):
+        """
+        :param file: The file path
+        :return:
+        """
+        f = open(file)
+        lines = f.readlines()
+        f.close()
+        return lines
+
+    def parse_config(self, lines):
+        for line in lines:
+            line = line.strip()
+            # Skip empty lines and comments and square brackets.
+            if line == "" or line.startswith("#") or line.startswith("["):
+                continue
+            key, attr = line.split("=")
+            key, attr = key.strip(), attr.strip()
+            if key in self.config.keys():
+                self.config[key] = attr
+            else:
+                print("Unknown key in config file.")

assignment1/Layer.py

@@ -2,13 +2,18 @@
 
 
 class Layer:
-    def __init__(self, weights, activations):
+    def __init__(self, weights, activations, bias):
         self.weights = weights
         self.activations = activations
+        self.bias = bias
 
     def get_output(self):
+        # TODO: Remember to change it for the first layer without activations.
         return np.transpose(self.weights).dot(activations)
 
+    def activation(self):
+        pass
+
 
 if __name__ == '__main__':
     weights = np.array([[1, 2], [2, 3]])

assignment1/main.py

@@ -0,0 +1,24 @@
+from Config import Config
+from Layer import Layer
+import numpy as np
+
+
+def main():
+    config = Config("config.txt")
+
+    # Input layer
+    activations = None
+    weights = np.array([0.2, 0.2])
+    bias = 0.2
+    input_layer = Layer(weights, activations, bias)
+
+    # Output layer
+    activations = np.array([])
+
+    # Data. Simple AND example:
+    data_X = np.array([[1, 1], [0, 1], [1, 0], [0, 0]])
+    data_y = np.array([1, 0, 0, 0])
+
+
+if __name__ == '__main__':
+    main()