Question: Consider the network: model = models.Sequential () model.add(layers.Dense (16, activation = 'relu', input_shape =(10000)),)#layer1 model.add(layers.Dense (16, activation = 'relu')) #layer 2 model.add(layers.Dense (1, activation =

Consider the network: model = models.Sequential () model.add(layers.Dense (16, activation =

Consider the network: model = models.Sequential () model.add(layers.Dense (16, activation = 'relu', input_shape =(10000)),)#layer1 model.add(layers.Dense (16, activation = 'relu')) \#layer 2 model.add(layers.Dense (1, activation = 'sigmoid' ) ) \#layer 3 The layer 2 weight tensor has Select one: a. 1616 trainable parameters b. 1000016 trainable parameters c. 16 trainable parameters d. 1616+16 trainable parameters

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