Keras Merge LayersAddThis layer adds a list of inputs by taking a similar shape of the tensors list as an input and returns a single tensor of the same shape. Example SubtractThis layer is used for subtracting two inputs by taking tensors list of size 2 as an input while mandating their shape to be similar and outputs a single tensor (inputs[0]  inputs[1]), that too of same shape. Examples MultiplyIt is the layer that performs elementwise multiplication operation on a list of inputs by taking the similar shape of the tensors list as an input and returns an individual tensor of the same shape. AverageThis layer computes the average of a list of inputs by taking the similar shape of the tensors list and returns the same shape of the single tensor. MaximumThis layer calculates the maximum of the inputs list (elementwise) by taking the similar shape of the tensors list and returns the same shape of the single tensor. MinimumThis layer calculates the minimum of inputs list (elementwise) by taking the similar shape of the tensors list and returns the same shape of the single tensor. ConcatenateThis layer is used to concatenate the inputs list by taking a similar shape of tensors list except for the concatenation axis and returns the same shape of a single tensor, which is actually the concatenation of all inputs. Arguments
DotThis is the layer that is used to calculate the dot product among the samples present in two tensors. To understand it more briefly, let's have a look at an example; suppose if we apply it to a list of any two tensors, i.e., q and r having a shape (batch_size, n), then, in that case, the output shape of the tensor will be (batch_size, 1), such that each entry i will relate to the dot product between q[i] and r[i]. Arguments
addIt can be defined as a functional interface to the Add layer. Arguments
Returns It returns a tensor that encompasses the calculated sum after the addition of inputs. Example subtractIt can be defined as a functional interface to the Subtract layer. Arguments
Returns It returns a tensor that encompasses the computed difference after the subtraction of inputs. Example multiplyIt can be defined as a functional interface to the Multiply layer. Arguments
Returns It returns a tensor, which is the elementwise computed product of inputs. averageIt can be defined as a functional interface to the Average layer. Arguments
Returns It returns a tensor, which is the computed average of inputs. maximumIt can be defined as a functional interface to the Maximum layer. Arguments
Returns It returns a tensor, which is the elementwise computed maximum of inputs. minimumIt can be defined as a functional interface to the Minimum layer. Arguments
Returns It returns a tensor, which is the elementwise computed minimum of inputs. concatenateIt can be defined as a functional interface to the Concatenate layer. Arguments
Returns It returns a tensor that encompasses the concatenation of inputs along the axis. dotIt can be defined as a functional interface to the Dot layer. Arguments
Returns It returns a tensor that encompasses the dot product after multiplying the samples of inputs.
Next TopicKeras Core Layers
