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Build a ConvNet using TensorFlow Keras Functional API
The TensorFlow Keras Sequential API can be used to build simple models with layer operations that proceed in a sequential order. If the model is non-linear or contains layers with multiple inputs or outputs, a Sequential model is not a good choice.
model = tf.keras.Sequential([ tensorflow.keras.layers.layer_name_1(arguments), tensorflow.keras.layers.layer_name_2(arguments), ... ])
Also, we can use .add() method to add layers incrementally to a Sequential model. To remove them, we can use .pop() method.
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Dimensions of Output
Sliding window
Different CNN Methods
Filter matrices
Which of the following do you typically see as you move to deeper layers in a ConvNet?
In order to be able to build very deep networks, we usually only use pooling layers to downsize the height/width of the activation volumes while convolutions are used with “valid” padding. Otherwise, we would downsize the input of the model too quickly.
Training a deeper network (for example, adding additional layers to the network) allows the network to fit more complex functions and thus almost always results in lower training error. For this question, assume we’re referring to “plain” networks.
Suppose you have an input volume of dimension 64x64x16. How many parameters would a single 1x1 convolutional filter have (including the bias)?
Build a ConvNet using TensorFlow Keras Functional API