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__init__.py

@@ -1,53 +0,0 @@
-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
-
-
-    
-    
-    
- 

dnn_main.py

@@ -0,0 +1,38 @@
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Embedding, Flatten
+from tensorflow.keras.datasets import imdb
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+top_words = 5000
+(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
+
+max_review_length = 500
+X_train = pad_sequences(X_train, maxlen=max_review_length)
+X_test = pad_sequences(X_test, maxlen=max_review_length)
+
+# Modelling a sample DNN
+model = Sequential()
+model.add(Embedding(input_dim=top_words, output_dim=24, input_length=max_review_length))
+model.add(Flatten())
+model.add(Dense(64, activation='relu'))
+model.add(Dense(32, activation='relu'))
+model.add(Dense(16, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+
+# opt=Adam(learning_rate=0.001)
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+model.summary()
+
+print("Training Started.")
+history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=20)
+
+loss, acc = model.evaluate(X_test, y_test)
+print("Training Finished.")
+
+print(f'Test Accuracy: {round(acc * 100)}')
+
+model.save(r'C:\Users\shahi\Desktop\My Projects\DeepPredictorHub\DP.keras')
+
+
+
+