add TrainClassifierCNN class to perform multiclass classification for…

… Danielle/Kristin project

TelescopeML/DeepTrainer.py

@@ -326,3 +326,176 @@ def fit_cnn_model(self,
 #         test_score  = model.evaluate(x = [self.X1_test, self.X2_test],
 #                                      y = [self.y1_test, self.y2_test, self.y3_test, self.y4_test],
 #                                      verbose=0)
+
+
+class TrainClassifierCNN:
+    """
+    Train Convolutional Neural Networks model using classification approach.
+
+    Parameters
+    -----------
+    X1_train : array
+        Row-StandardScaled input spectra for training.
+    X1_val : array
+        Row-StandardScaled input spectra for validation.
+    X1_test : array
+        Row-StandardScaled input spectra for testing.
+
+    X2_train : array
+        Col-StandardScaled Mix Max of all rows of input spectra for training.
+    X2_val : array
+        Col-StandardScaled Mix Max of all rows of input spectra for validation.
+    X2_test : array
+        Col-StandardScaled Mix Max of all rows of input spectra for testing.
+
+    y_train : array
+        One-hot encoded or integer labels for multiclass classification for training.
+    y_val : array
+        One-hot encoded or integer labels for multiclass classification for validation.
+    y_test : array
+        One-hot encoded or integer labels for multiclass classification for testing.
+    """
+    def __init__(self,
+                 X1_train, X1_val, X1_test,  # Row-StandardScaled input spectra
+                 X2_train, X2_val, X2_test,  # Col-StandardScaled Mix Max of all rows of input spectra
+                 y_train, y_val, y_test  # Labels for classification
+                 ):
+
+        self.X1_train, self.X1_val, self.X1_test = X1_train, X1_val, X1_test
+        self.X2_train, self.X2_val, self.X2_test = X2_train, X2_val, X2_test
+        self.y_train, self.y_val, self.y_test = y_train, y_val, y_test
+
+    def build_model(self, config):
+        """
+        Build a CNN model for multiclass classification with the given hyperparameters.
+
+        Parameters
+        ----------
+        config : dict
+            Dictionary containing hyperparameter settings for the CNN and FC layers.
+
+        Returns
+        -------
+        object
+            Pre-built tf.keras.Model CNN model for multiclass classification.
+        """
+
+        Conv__filters = config['Conv__filters']
+        Conv__kernel_size = config['Conv__kernel_size']
+        Conv__MaxPooling1D = config['Conv__MaxPooling1D']
+        Conv__NumberLayers = config['Conv__NumberLayers']
+        Conv__NumberBlocks = config['Conv__NumberBlocks']
+
+        FC1__units = config['FC1__units']
+        FC1__dropout = config['FC1__dropout']
+        FC1__NumberLayers = config['FC1__NumberLayers']
+
+        FC2__units = config['FC2__units']
+        FC2__NumberLayers = config['FC2__NumberLayers']
+        FC2__dropout = config['FC2__dropout']
+        FC2__NumberBlocks = config['FC2__NumberBlocks']
+
+        lr = config['lr']
+        self.lr = lr
+
+        # Shape of the inputs
+        input_1 = tf.keras.layers.Input(shape=(104, 1))
+        input_2 = tf.keras.layers.Input(shape=(2,))
+
+        # Convolutional Blocks
+        model = input_1
+        for b in range(Conv__NumberBlocks):
+            for l in range(Conv__NumberLayers):
+                model = Conv1D(filters=Conv__filters * (b + l + 1) ** 2,
+                               kernel_size=Conv__kernel_size,
+                               padding='same',
+                               activation='relu',
+                               kernel_initializer='he_normal')(model)
+            model = MaxPooling1D(pool_size=(Conv__MaxPooling1D))(model)
+
+        # Flatten Layer
+        model = Flatten()(model)
+
+        # Fully Connected Layers (before concatenation)
+        for l in range(FC1__NumberLayers):
+            model = Dense(FC1__units * (l + 1) ** 2, activation='relu',
+                          kernel_initializer='he_normal')(model)
+        model = Dropout(FC1__dropout)(model)
+
+        # Concatenation Layer
+        model = tf.keras.layers.concatenate([model, input_2])
+
+        # Fully Connected Layers after concatenation
+        for b in range(FC2__NumberBlocks):
+            for l in range(FC2__NumberLayers):
+                model = Dense(FC2__units * (b + l + 1) ** 2, activation='relu',
+                              kernel_initializer='he_normal')(model)
+            model = Dropout(FC2__dropout)(model)
+
+        # Output Layer for Multiclass Classification
+        output = Dense(units=config['output_units'], activation='softmax', name='output')(model)
+
+        # Create the model
+        model = tf.keras.Model(inputs=[input_1, input_2], outputs=[output])
+
+        self.model = model
+
+        # Print model summary
+        print(model.summary())
+
+    def fit_cnn_model(self, batch_size=32, epochs=3):
+        """
+        Fit the CNN model for multiclass classification.
+
+        Parameters
+        ----------
+        batch_size : int, default 32
+            The number of samples per gradient update.
+        epochs : int, default 3
+            Number of epochs to train the model.
+
+        Returns
+        -------
+        history : object
+            Training history (Loss values for train and validation).
+        """
+        # Compile the model with categorical crossentropy loss and Adam optimizer
+        self.model.compile(loss='categorical_crossentropy',
+                           optimizer=Adam(learning_rate=self.lr),
+                           metrics=['accuracy'])
+
+        # Early stopping to prevent overfitting
+        early_stop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
+
+        # Train the model
+        history = self.model.fit(x=[self.X1_train, self.X2_train],
+                                 y=self.y_train,
+                                 batch_size=batch_size,
+                                 validation_data=([self.X1_val, self.X2_val], self.y_val),
+                                 epochs=epochs,
+                                 verbose=1,
+                                 callbacks=[early_stop])
+
+        self.history = history
+        return history
+
+# Example usage with hyperparameter configuration:
+# config = {
+#     'Conv__filters': 32,
+#     'Conv__kernel_size': 3,
+#     'Conv__MaxPooling1D': 2,
+#     'Conv__NumberLayers': 2,
+#     'Conv__NumberBlocks': 2,
+#     'FC1__units': 64,
+#     'FC1__dropout': 0.5,
+#     'FC1__NumberLayers': 2,
+#     'FC2__units': 32,
+#     'FC2__dropout': 0.5,
+#     'FC2__NumberLayers': 2,
+#     'FC2__NumberBlocks': 2,
+#     'lr': 0.001,
+#     'output_units': 10  # For 10-class classification
+# }
+# model = TrainClassifierCNN(X1_train, X1_val, X1_test, X2_train, X2_val, X2_test, y_train, y_val, y_test)
+# model.build_model(config)
+# history = model.fit_cnn_model(batch_size=64, epochs=50)