From a216ff912da6dbd082e1b0a12013d2834487f22e Mon Sep 17 00:00:00 2001
From: KOSASIH <kosasihg88@gmail.com>
Date: Mon, 15 Jul 2024 09:54:18 +0700
Subject: [PATCH] Create ai_core.py

---
 ai_core/ai_core.py | 74 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 ai_core/ai_core.py

diff --git a/ai_core/ai_core.py b/ai_core/ai_core.py
new file mode 100644
index 0000000..01792a2
--- /dev/null
+++ b/ai_core/ai_core.py
@@ -0,0 +1,74 @@
+import numpy as np
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Dense, LSTM
+
+class AICore:
+    def __init__(self, num_inputs, num_outputs, hidden_layers):
+        self.num_inputs = num_inputs
+        self.num_outputs = num_outputs
+        self.hidden_layers = hidden_layers
+
+    def create_neural_network(self):
+        """
+        Create a neural network using Keras.
+
+        Returns:
+            Model: The created neural network.
+        """
+        inputs = Input(shape=(self.num_inputs,))
+        x = Dense(self.hidden_layers[0], activation="relu")(inputs)
+        for i in range(1, len(self.hidden_layers)):
+            x = Dense(self.hidden_layers[i], activation="relu")(x)
+        outputs = Dense(self.num_outputs, activation="softmax")(x)
+        model = Model(inputs=inputs, outputs=outputs)
+        return model
+
+    def train_neural_network(self, model, X_train, y_train, X_test, y_test):
+        """
+        Train a neural network using Keras.
+
+        Args:
+            model (Model): The neural network to train.
+            X_train (numpy array): The training input data.
+            y_train (numpy array): The training output data.
+            X_test (numpy array): The testing input data.
+            y_test (numpy array): The testing output data.
+        """
+        model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+        model.fit(X_train, y_train, epochs=100, batch_size=128, validation_data=(X_test, y_test))
+
+    def use_neural_network(self, model, input_data):
+        """
+        Use a trained neural network to make predictions.
+
+        Args:
+            model (Model): The trained neural network.
+            input_data (numpy array): The input data to make predictions on.
+
+        Returns:
+            numpy array: The predicted output data.
+        """
+        predictions = model.predict(input_data)
+        return predictions
+
+    def optimize_neural_network(self, model, X_train, y_train, X_test, y_test):
+        """
+        Optimize a neural network using a genetic algorithm.
+
+        Args:
+            model (Model): The neural network to optimize.
+            X_train (numpy array): The training input data.
+            y_train (numpy array): The training output data.
+            X_test (numpy array): The testing input data.
+            y_test (numpy array): The testing output data.
+        """
+        from scipy.optimize import differential_evolution
+        def objective_function(params):
+            model.set_weights(params)
+            loss = model.evaluate(X_test, y_test)
+            return loss
+        bounds = [(0, 1) for _ in range(model.count_params())]
+        result = differential_evolution(objective_function, bounds)
+        optimized_weights = result.x
+        model.set_weights(optimized_weights)
+        return model