From 4ff7f3c26637491f718399e25668f0c9d9ce9ae8 Mon Sep 17 00:00:00 2001
From: Falguni Ghosh <falguni.ghosh@fau.de>
Date: Sun, 15 Oct 2023 21:06:19 +0000
Subject: [PATCH] Upload New File

---
 2_CNN/NeuralNetwork.py | 81 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 2_CNN/NeuralNetwork.py

diff --git a/2_CNN/NeuralNetwork.py b/2_CNN/NeuralNetwork.py
new file mode 100644
index 0000000..dbf136e
--- /dev/null
+++ b/2_CNN/NeuralNetwork.py
@@ -0,0 +1,81 @@
+import numpy as np
+import copy
+
+
+class NeuralNetwork:
+
+    input_tensor = None
+    label_tensor = None
+
+    def __init__(self, optimizer,weights_initializer,bias_initializer):
+        self.optimizer = optimizer
+        self.layers = []
+        self.loss = []
+        self.data_layer = None
+        self.loss_layer = None
+        self.weights_initializer = weights_initializer
+        self.bias_initializer = bias_initializer
+
+    def forward(self):
+
+        input_tensor,label_tensor = self.data_layer.next()
+        self.input_tensor = np.copy(input_tensor)
+        self.label_tensor = np.copy(label_tensor)
+
+        for i in self.layers:
+            input_tensor = i.forward(input_tensor)
+
+        return self.loss_layer.forward(input_tensor,label_tensor)
+
+    def backward(self):
+        error_tensor = self.loss_layer.backward(self.label_tensor)
+
+        for i in reversed(self.layers):
+            error_tensor = i.backward(error_tensor)
+
+    def append_layer(self,layer):
+        if layer.trainable:
+            layer.optimizer = copy.deepcopy(self.optimizer)
+            # layer.set_optimizer(copy.deepcopy(self.optimizer))
+            layer.initialize(self.weights_initializer, self.bias_initializer)
+
+        self.layers.append(layer)
+
+    def train(self,iterations):
+
+        for i in range(iterations):
+            intermed_loss = self.forward()
+            self.loss.append(intermed_loss)
+            self.backward()
+
+
+
+    def test(self,input_tensor):
+
+        for i in self.layers:
+            input_tensor = i.forward(input_tensor)
+
+        return input_tensor
+
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-- 
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