طيب هي بتكبت في اي جزاء في الكود ده
# The 'deep_hit_model' is a Sequential model in Keras, meaning the layers are stacked in a linear fashion.
deep_hit_model = keras.models.Sequential([
# - The first layer is a Dense layer with 8 units and 'tanh' activation function. This layer is responsible for transforming the input into a higher-dimensional space.
keras.layers.Dense(8),
keras.layers.BatchNormalization(),
keras.layers.Activation('tanh'),
keras.layers.Dropout(0.1),
# - The second layer is a Dense layer with 128 units and 'tanh' activation function, allowing the model to learn more complex patterns.
keras.layers.Dense(128),
keras.layers.BatchNormalization(),
keras.layers.Activation('tanh'),
keras.layers.Dropout(0.3),
# - The third layer is a Dense layer with 64 units and 'tanh' activation function, further processing the data with non-linearities.
keras.layers.Dense(64),
keras.layers.BatchNormalization(),
keras.layers.Activation('tanh'),
# - The fourth layer is a Dense layer with 32 units and 'tanh' activation function, continuing to refine the representation of the data.
keras.layers.Dense(32),
keras.layers.BatchNormalization(),
keras.layers.Activation('tanh'),
# - The final layer is a Dense layer with 1 unit and 'sigmoid' activation function, producing an output between 0 and 1, suitable for binary classification.
keras.layers.Dense(1 , activation='sigmoid'),
])