Create model_utils.py

sidra_chain_integration/src/space_exploration/project/Lynx/utils/model_utils.py

@@ -0,0 +1,28 @@
+import tensorflow as tf
+from tensorflow import keras
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+def create_model(input_shape, num_classes):
+    # Define the machine learning model architecture
+    model = keras.Sequential([
+        keras.layers.Dense(64, activation='relu', input_shape=input_shape),
+        keras.layers.Dense(32, activation='relu'),
+        keras.layers.Dense(num_classes, activation='softmax')
+    ])
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+def train_model(model, train_data, test_data, epochs=10):
+    # Train the machine learning model
+    model.fit(train_data, epochs=epochs, validation_data=test_data)
+    return model
+
+def evaluate_model(model, test_data):
+    # Evaluate the machine learning model
+    y_pred = model.predict(test_data)
+    y_pred_class = tf.argmax(y_pred, axis=1)
+    y_true = test_data['label']
+    accuracy = accuracy_score(y_true, y_pred_class)
+    report = classification_report(y_true, y_pred_class)
+    matrix = confusion_matrix(y_true, y_pred_class)
+    return accuracy, report, matrix