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BackPropogation.py

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+import numpy as np
+from tqdm import tqdm
+
+
+class BackPropogation:
+    def __init__(self,learning_rate=0.01, epochs=100,activation_function='step'):
+        self.bias = 0
+        self.learning_rate = learning_rate
+        self.max_epochs = epochs
+        self.activation_function = activation_function
+
+
+    def activate(self, x):
+        if self.activation_function == 'step':
+            return 1 if x >= 0 else 0
+        elif self.activation_function == 'sigmoid':
+            return 1 if (1 / (1 + np.exp(-x)))>=0.5 else 0
+        elif self.activation_function == 'relu':
+            return 1 if max(0,x)>=0.5 else 0
+
+    def fit(self, X, y):
+        error_sum=0
+        n_features = X.shape[1]
+        self.weights = np.zeros((n_features))
+        for epoch in tqdm(range(self.max_epochs)):
+            for i in range(len(X)):
+                inputs = X[i]
+                target = y[i]
+                weighted_sum = np.dot(inputs, self.weights) + self.bias
+                prediction = self.activate(weighted_sum)
+                
+                # Calculating loss and updating weights.
+                error = target - prediction
+                self.weights += self.learning_rate * error * inputs
+                self.bias += self.learning_rate * error
+                
+            print(f"Updated Weights after epoch {epoch} with {self.weights}")
+        print("Training Completed")
+
+    def predict(self, X):
+        predictions = []
+        for i in range(len(X)):
+            inputs = X[i]
+            weighted_sum = np.dot(inputs, self.weights) + self.bias
+            prediction = self.activate(weighted_sum)
+            predictions.append(prediction)
+        return predictions
+
+
+    
+    
+    
+ 

Perceptron.py

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+import numpy as np
+from tqdm import tqdm
+
+
+class Perceptron:
+    
+    def __init__(self,learning_rate=0.01, epochs=100,activation_function='step'):
+        self.bias = 0
+        self.learning_rate = learning_rate
+        self.max_epochs = epochs
+        self.activation_function = activation_function
+
+
+    def activate(self, x):
+        if self.activation_function == 'step':
+            return 1 if x >= 0 else 0
+        elif self.activation_function == 'sigmoid':
+            return 1 if (1 / (1 + np.exp(-x)))>=0.5 else 0
+        elif self.activation_function == 'relu':
+            return 1 if max(0,x)>=0.5 else 0
+
+    def fit(self, X, y):
+        n_features = X.shape[1]
+        self.weights = np.random.randint(n_features, size=(n_features))
+        for epoch in tqdm(range(self.max_epochs)):
+            for i in range(len(X)):
+                inputs = X[i]
+                target = y[i]
+                weighted_sum = np.dot(inputs, self.weights) + self.bias
+                prediction = self.activate(weighted_sum)
+        print("Training Completed")
+
+    def predict(self, X):
+        predictions = []
+        for i in range(len(X)):
+            inputs = X[i]
+            weighted_sum = np.dot(inputs, self.weights) + self.bias
+            prediction = self.activate(weighted_sum)
+            predictions.append(prediction)
+        return predictions
+
+
+    
+    
+    
+ 

cnn_tumor_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0f23e5dd30e1b7b5d76fdeb33bdbd3f7b762cd641541ffb99f32e97820bd747
+size 391811360

gru_movie_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b0fa91a9f80f4388c147f3b4a638fbbb30a6f99834dec25060420fde323afac
+size 43126776

iris_backprop_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fab088872c073d72e358cb47f7c881045fd816657901ab1cae79d3e0bb98782
+size 309

iris_dnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78da3a8bf14f537b5cc9f117bee53142d8deb556e2ed4d235d76f6735687dc2e
+size 77096

iris_perceptron_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a008932324739f92bb66a0bac74ee9d09cff37af63cee45ab5bd97853c56c03
+size 268

lstm_imdb_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b03fc488fed00a614e9c9d85b4bfc4c3de4bf51f950ab3fdbc959cc8736f456c
+size 2594296

mnist_cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f155240105c9ff88de0783908008834e4dad493757a0fd0aa2c0d8e3fa5ee19f
+size 8405920

requirements.txt

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+streamlit
+Pillow
+tensorflow
+numpy

sms_sentiment_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee684cae96a01d6a7304de0eed0df6ace4b9b354375ad0c81da81f61412bcda8
+size 1775224

spammodel.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:260ffcd8ce899bd9ea90ec4ed7c7d6c04311c8fac596824219736918a1b4782d
+size 2269016