Create model_utils.py

src/cosmic_pi_network/utils/model_utils.py

@@ -0,0 +1,37 @@
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+
+class ModelUtils:
+    def __init__(self):
+        pass
+
+    def create_model(self, input_shape, num_classes):
+        model = Sequential()
+        model.add(Dense(64, activation='relu', input_shape=input_shape))
+        model.add(Dense(32, activation='relu'))
+        model.add(Dense(num_classes, activation='softmax'))
+        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+        return model
+
+    def train_model(self, model, X_train, y_train, epochs=10):
+        model.fit(X_train, y_train, epochs=epochs, batch_size=32, verbose=1)
+
+    def evaluate_model(self, model, X_test, y_test):
+        loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
+        return loss, accuracy
+
+    def save_model(self, model, file_path):
+        model.save(file_path)
+
+    def load_model(self, file_path):
+        return tf.keras.models.load_model(file_path)
+
+# Example usage
+model_utils = ModelUtils()
+model = model_utils.create_model((10,), 2)
+model_utils.train_model(model, X_train, y_train)
+loss, accuracy = model_utils.evaluate_model(model, X_test, y_test)
+print("Loss:", loss, "Accuracy:", accuracy)
+model_utils.save_model(model, "model.h5")
+loaded_model = model_utils.load_model("model.h5")