(a). Without running the code in Python, describe the architecture for the following deep learning model, including number of layers, dimension of images in each layer, number of parameters in each...

(a). Without running the code in Python, describe the architecture for the following
deep learning model, including number of layers, dimension of images in each layer,
number of parameters in each layer. Show details of all you calculations.


inputs = keras.Input(shape=(36, 36, 1))
x = layers.Conv2D(filters=16, kernel_size=5, activation="relu")(inputs)
x = layers.MaxPooling2D(pool_size=3)(x)
x = layers.Conv2D(filters=64, kernel_size=3, activation="relu")(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=128, kernel_size=2, activation="relu")(x)
x = layers.Flatten()(x)
x = layers.Dense(128, activation="relu")(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(10, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)