Question: Consider the following code creating a simple convolutional neural network: 1 0 0 2 0 1 0 1 M=0 0 1 2, 1 model=Sequential() model.add(layers.Conv2D(filters=6,kernel_size=5,

Consider the following code creating a simple convolutional neural network: 1 0 0 2 0 1 0 1 M=0 0 1 2, 1 model=Sequential() model.add(layers.Conv2D(filters=6,kernel_size=5, strides=2,padding=valid ,input_shape=(21,21,3))) model.add(layers.MaxPooling2D(pool_size=3, strides=2, padding=valid)) model.add(layers.Conv2D(filters=10,kernel_size=3, strides=1,padding=same)) model.add(layers.Flatten()) model.add(layers.Dense(10)) For each of the 5 layers calculate the following: The dimensions of the output The number of trainable parameters (weights and biases)

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