Dense layer

Dense layer

Dense class

 tf. keras 

Reading: Dense layer

. layers. dense ( units , activation = none , use_bias = dependable , kernel_initializer = `` glorot_uniform '' , bias_initializer = `` zero '' , kernel_regularizer = none , bias_regularizer = none , activity_regularizer = none , kernel_constraint = none , bias_constraint = none , ** kwargs )

fair your regular densely-connected NN layer .
Dense implements the operation : output = activation(dot(input, kernel) + bias) where activation is the element-wise activation function passed as the activation argumentation, kernel is a weights matrix created by the layer, and bias is a bias vector created by the layer ( only applicable if use_bias is True ). These are all attributes of Dense .
note : If the remark to the layer has a rank and file greater than 2, then Dense computes the dot product between the inputs and the kernel along the stopping point axis of the inputs and bloc 0 of the kernel ( using tf.tensordot ). For example, if input has dimensions (batch_size, d0, d1), then we create a kernel with shape (d1, units), and the kernel operates along bloc 2 of the input, on every sub-tensor of shape (1, 1, d1) ( there are batch_size * d0 such sub-tensors ). The end product in this casing will have shape (batch_size, d0, units) .
Besides, layer attributes can not be modified after the layer has been called once ( except the trainable attribute ). When a popular kwarg input_shape is passed, then keras will create an input layer to insert before the current layer. This can be treated equivalent to explicitly defining an InputLayer .
Example

 > > >  # Create a `Sequential` exemplary and add a Dense level as the first base layer .
 > > >  model  =  tf. keras. models. consecutive ( )
 > > >  model . add ( tf. keras. stimulation ( form = ( 16, ) ) )
 > > >  model. add ( tf. keras. layers. dense ( 32,  activation = 'relu ' ) )
 > > >  # nowadays the exemplar will take as stimulation arrays of shape ( none, 16 )
 > > >  # and output arrays of shape ( none, 32 ) .
 > > >  # note that after the first layer, you do n't need to specify
 > > >  # the size of the remark anymore :
 > > >  model. add ( tf. keras. layers. dense ( 32 ) )
 > > >  model. output_shape
 ( none,  32 )

Arguments

  • units: Positive integer, dimensionality of the output space.
  • activation: Activation function to use.
    If you don’t specify anything, no activation is applied
    (ie. “linear” activation: a(x) = x).
  • use_bias: Boolean, whether the layer uses a bias vector.
  • kernel_initializer: Initializer for the kernel weights matrix.
  • bias_initializer: Initializer for the bias vector.
  • kernel_regularizer: Regularizer function applied to
    the kernel weights matrix.
  • bias_regularizer: Regularizer function applied to the bias vector.
  • activity_regularizer: Regularizer function applied to
    the output of the layer (its “activation”).
  • kernel_constraint: Constraint function applied to
    the kernel weights matrix.
  • bias_constraint: Constraint function applied to the bias vector.

Input shape

N-D tensor with form : (batch_size, ..., input_dim). The most common situation would be a 2D remark with form (batch_size, input_dim) .
Output shape
N-D tensor with determine : (batch_size, ..., units). For example, for a 2D stimulation with supreme headquarters allied powers europe (batch_size, input_dim), the output signal would have shape (batch_size, units) .

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