Minval represent the lower bound of the random values to generate My_init = initializers.RandomUniform(minval = -0.05, maxval = 0.05, seed = None) Generates value using uniform distribution of input data. Seed represent the values to generate random number Stddev represent the standard deviation of the random values to generate Mean represent the mean of the random values to generate My_init = initializers.RandomNormal(mean=0.0, Generates value using normal distribution of input data. Where, value represent the constant value RandomNormal My_init = initializers.Constant(value = 0) model.add(ĭense(512, activation = 'relu', input_shape = (784,), kernel_initializer = my_init) Generates a constant value (say, 5) specified by the user for all input data. Where, kernel_initializer represent the initializer for kernel of the model. Some of the Keras Initializer function are as follows − Zeros Initializers module provides different functions to set these initial weight. In Machine Learning, weight will be assigned to all input data. To create the first layer of the model (or input layer of the model), shape of the input data should be specified. Similarly, (3,4,2) three dimensional matrix having three collections of 4x2 matrix (two rows and four columns). For example, (4,2) represent matrix with four rows and two columns. We can specify the dimensional information using shape, a tuple of integers. Input numbers may be single dimensional array, two dimensional array (matrix) or multi-dimensional array. In machine learning, all type of input data like text, images or videos will be first converted into array of numbers and then feed into the algorithm. Let us understand the basic concept of layer as well as how Keras supports each concept. Line 11 creates final Dense layer with 8 units. Line 10 creates second Dense layer with 16 units and set relu as the activation function. MaxNorm function is set as value.Īctivation represent activation to be used. Kernel_constraint represent constraint to be used. Kernel_regularizer represent regularizer to be used. Kernel_initializer represent initializer to be used. Input_shape represent the shape of input data. All other parameters are optional.įirst parameter represents the number of units (neurons). Otherwise, the output of the previous layer will be used as input of the next layer. 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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