AutoWeightLogger1/train_mnist_model.py

train_mnist_model.py

@@ -1,47 +0,0 @@
-import tensorflow as tf
-from tensorflow.keras import layers, models
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Load and preprocess MNIST dataset
-def load_and_preprocess_data():
-    try:
-        (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
-        x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0
-        x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0
-        logger.info("MNIST dataset loaded and preprocessed successfully")
-        return x_train, y_train, x_test, y_test
-    except Exception as e:
-        logger.error(f"Error loading MNIST data: {e}")
-        return None, None, None, None
-
-# Build and train CNN model
-def train_model():
-    x_train, y_train, x_test, y_test = load_and_preprocess_data()
-    if x_train is None:
-        return
-
-    model = models.Sequential([
-        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
-        layers.MaxPooling2D((2, 2)),
-        layers.Conv2D(64, (3, 3), activation='relu'),
-        layers.MaxPooling2D((2, 2)),
-        layers.Flatten(),
-        layers.Dense(128, activation='relu'),
-        layers.Dense(10, activation='softmax')
-    ])
-
-    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-
-    try:
-        model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))
-        model.save('mnist_cnn.h5')
-        logger.info("Model trained and saved as mnist_cnn.h5")
-    except Exception as e:
-        logger.error(f"Error training model: {e}")
-
-if __name__ == "__main__":
-    train_model()