deep learning for computer vision figure the test cat picture in order to extract the feature maps you want to look atyou'll create keras model that takes batches of images as inputand outputs the activations of all convolution and pooling layers to do thisyou'll use the keras class model model is instantiated using two argumentsan input tensor (or list of input tensorsand an output tensor (or list of output tensorsthe resulting class is keras modeljust like the sequential models you're familiar withmapping the specified inputs to the specified outputs what sets the model class apart is that it allows for models with multiple outputsunlike sequential for more information about the model classsee section listing inst ant iat ing model from an input ensor and list of out put ensors from keras import models layer_outputs [layer output for layer in model layers[: ]activation_model models model(inputs=model inputoutputs=layer_outputsextracts the outputs of the top eight layers creates model that will return these outputsgiven the model input when fed an image inputthis model returns the values of the layer activations in the original model this is the first time you've encountered multi-output model in this bookuntil nowthe models you've seen have had exactly one input and one output in the general casea model can have any number of inputs and outputs this one has one input and eight outputsone output per layer activation licensed to