init weights and biases

nn/nn.py

@@ -24,6 +24,8 @@ class NN:
         self.target = target
         self.data = data
 
+        self.input_size = len(features)
+
         self.wh: np.array = None
         self.wo: np.array = None
         self.bh: np.array = None
@@ -48,6 +50,18 @@ class NN:
         assert isinstance(f, Callable)
         self.func_prime = f
 
+    def set_bh(self, bh: np.array) -> None:
+        self.bh = bh
+
+    def set_wh(self, wh: np.array) -> None:
+        self.wh = wh
+
+    def set_bo(self, bo: np.array) -> None:
+        self.bo = bo
+
+    def set_wo(self, wo: np.array) -> None:
+        self.wo = wo
+
     @classmethod
     def from_dict(cls, dct):
         """ Creates an instance of NN given a dictionary

nn/train.py

@@ -4,7 +4,20 @@ import pandas as pd
 import numpy as np
 
 
+def init_weights_biases(nn: NN) -> None:
+    np.random.seed(88)
+    bh = np.zeros((1, 1))
+    bo = np.zeros((1, 1))
+    wh = np.random.randn(1, nn.input_size) * np.sqrt(2 / nn.input_size)
+    wo = np.random.randn(1, nn.hidden_size) * np.sqrt(2 / nn.hidden_size)
+    nn.set_bh(bh)
+    nn.set_bo(bo)
+    nn.set_wh(wh)
+    nn.set_wo(wo)
+
+
 def train(nn: NN) -> dict:
+    init_weights_biases(nn=nn)
     X_train, X_test, y_train, y_test = train_test_split(
         nn.X,
         nn.y,