learning for both nn working

functions.py

@@ -238,8 +238,9 @@ class Network:
         :param dw: (array) Partial derivatives
         :param delta: (array) Delta error.
         """
-        self.w[index] -= self.learning_rate * np.mean(dw, 1)
-        self.b[index] -= self.learning_rate * np.mean(np.mean(delta, 1), 0)
+
+        self.w[index] -= self.learning_rate * dw
+        self.b[index] -= self.learning_rate * np.mean(delta, 0)
 
     def fit(self, x, y_true, loss, epochs, batch_size, learning_rate=2e-2):
         """
@@ -265,7 +266,8 @@ class Network:
                 self.back_prop(z, a, y_[k:l])
 
             if (i + 1) % 10 == 0:
-                print("Loss:", self.loss.loss(y_true, z[self.n_layers]))
+                _, a = self.feed_forward(x)
+                print("Loss:", self.loss.loss(y_true, a[self.n_layers]))
 
     def predict(self, x):
         _, a = self.feed_forward(x)
@@ -273,27 +275,37 @@ class Network:
 
 if __name__ == "__main__":
     from sklearn import datasets
-    #import sklearn.metrics
+    import sklearn.metrics
     np.random.seed(1)
     # Load data
     data = datasets.load_iris()
     x = data["data"]
     x = (x - x.mean()) / x.std()
-    y = np.expand_dims(data["target"], 1)
+    y = data["target"]
+    #y = np.expand_dims(data["target"], 1)
 
     # one hot encoding
     y = np.eye(3)[y]
 
-    nn = Network((4, 8, 2, 3), (Relu, Relu, Sigmoid))
+    nn = Network((4, 8, 3), (Relu, Relu, Sigmoid))
 
     #nn.fit(x[:2], y[:2], MSE, 1, batch_size=2)
     nn.fit(x, y, MSE, 1000, 16)
+
+    # data = datasets.load_digits()
+    #
+    # x = data["data"]
+    # y = data["target"]
+    # y = np.eye(10)[y]
+    #
+    # nn = Network((64, 32, 10), (Relu, Sigmoid))
+    # nn.fit(x, y, MSE, 100, 2)
+    #
     y_ = nn.predict(x)
     a = np.argmax(y_, 1)
 
-    for i in range(a.size):
-        print(a[i], y[i])
-
+    # for i in range(a.size):
+    #     print(a[i], y[i], "\t", np.round(y_[i], 3))
 
     # y_true = []
     # y_pred = []

simple_mlp.py

@@ -140,10 +140,10 @@ class NeuralNetwork:
         dc_dw1 = np.dot(self.x.T, delta2)
 
         # update weights and biases
-        self.w[2] -= self.learning_rate * np.mean(dc_dw2, 1)
-        self.b[2] -= self.learning_rate * np.mean(np.mean(delta3, 1), 0)
-        self.w[1] -= self.learning_rate * np.mean(dc_dw1, 1)
-        self.b[1] -= self.learning_rate * np.mean(np.mean(delta2, 1), 0)
+        self.w[2] -= self.learning_rate * dc_dw2
+        self.b[2] -= self.learning_rate * np.mean(delta3, 0)
+        self.w[1] -= self.learning_rate * dc_dw1
+        self.b[1] -= self.learning_rate * np.mean(delta2, 0)
 
     def stats(self):
         """
@@ -188,24 +188,24 @@ if __name__ == "__main__":
     data = datasets.load_iris()
     x = data["data"]
     x = (x - x.mean()) / x.std()
-    y = np.expand_dims(data["target"], 1)
+    y = data["target"]
 
     # one hot encoding
     y = np.eye(3)[y]
 
-    nn = NeuralNetwork(4, 8, 3, 2e-2)
+    nn = NeuralNetwork(4, 4, 3, 1e-2)
     #nn.fit(x[:2], y[:2], 2, 1)
-    nn.fit(x, y, 16, 1000)
+    nn.fit(x, y, 8, 1000)
     _, y_ = feed_forward(x, nn.w, nn.b)
-    print(y_)
+    print(y_[3])
 
     # # result
     # _, y_ = feed_forward(x, nn.w, nn.b)
-    # y_true = []
-    # y_pred = []
-    # for i in range(len(y)):
-    #     y_pred.append(np.argmax(y_[3][i]))
-    #     y_true.append(np.argmax(y[i]))
-    #
-    # print(sklearn.metrics.classification_report(y_true, y_pred))
-    #
+    y_true = []
+    y_pred = []
+    for i in range(len(y)):
+        y_pred.append(np.argmax(y_[3][i]))
+        y_true.append(np.argmax(y[i]))
+
+    print(sklearn.metrics.classification_report(y_true, y_pred))
+