If the layer is first layer, then we need to provide Input Shape, (16,) as well. Dense is an entry level layer provided by Keras, which accepts the number of neurons or units (32) as its required parameter. Line 9 creates a new Dense layer and add it into the model. Line 7 creates a new model using Sequential API. Model.add(Dense(16, activation = 'relu')) Kernel_regularizer = None, kernel_constraint = 'MaxNorm', activation = 'relu')) Model.add(Dense(32, input_shape=(16,), kernel_initializer = 'he_uniform', Before understanding the basic concept, let us create a simple Keras layer using Sequential model API to get the idea of how Keras model and layer works.įrom keras.layers import Activation, Dense Let us understand the basic concept in the next chapter. To summarise, Keras layer requires below minimum details to create a complete layer. In between, constraints restricts and specify the range in which the weight of input data to be generated and regularizer will try to optimize the layer (and the model) by dynamically applying the penalties on the weights during optimization process. IntroductionĪ Keras layer requires shape of the input (input_shape) to understand the structure of the input data, initializer to set the weight for each input and finally activators to transform the output to make it non-linear. Let us learn complete details about layers in this chapter. The output of one layer will flow into the next layer as its input. Each layer receives input information, do some computation and finally output the transformed information. As learned earlier, Keras layers are the primary building block of Keras models.
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