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given the following definition of a convolutional neural network ( comments about each line of Python code highlighted in yellow before the line ) ,

given the following definition of a convolutional neural network (comments about each line of Python code highlighted in yellow before the line),
model = Sequential()
#input as a (1x28x28) tensor, 24(3x3) kernels, stride=1, padding="same" to keep input size, relu activation
model.add(Conv2D (24, kernel_size=(3,3), padding="same", activation="relu", input_shape=(28,28,1))
# max pooling with pooling size (2x2) applied as in the lecture's example
model.add(MaxPooling2D (pool_size=(2,2)))
#input as previous layer's feature maps, no padded convolution here
model.add(Conv2D (48, kernel_size=(3,3), padding="valid", activation="relu"))
# max pooling with pooling size (2x2) applied as in the lecture's example
model.add(MaxPooling2D (pool_size=(2,2)))
# input as previous layer's feature maps, see description in the first layer for the rest
model.add(Conv2D (64, kernel_size=(3,3), padding="same", activation="relu"))
#max pooling with pooling size (2x2) applied as in the lecture's example
model.add(MaxPooling2D (pool_size=(2,2)))
#input as previous layer's feature maps, no impact on the number of the network's parameters model.add(Flatten())
# 128-wide dense layer with input as flattened vector from previous step
model.add(Dense (128, activation="relu"))
# no impact on the number of the network's parameters
model.add(Dropout (0.25))
# 10-wide output layer with input as hidden vector from previous step
model.add(Dense (10, activation="softmax"))
# no impact on the number of the network's parameters
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
describe the network in terms of its depth, its layers' width, what it is expected to eventually calculate and say how many parameters are trainable in total elaborating on how you calculate that number.

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