A Keras tensor is a symbolic tensor-like object, which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model.
For instance, if a, b and c are Keras tensors,
it becomes possible to do:
model <- keras_model(input = c(a, b), output = c)
Usage
keras_input(
shape = NULL,
batch_size = NULL,
dtype = NULL,
sparse = NULL,
batch_shape = NULL,
name = NULL,
tensor = NULL,
optional = FALSE
)Arguments
- shape
A shape list (list of integers or
NULLobjects), not including the batch size. For instance,shape = c(32)indicates that the expected input will be batches of 32-dimensional vectors. Elements of this list can beNULLorNA;NULL/NAelements represent dimensions where the shape is not known and may vary (e.g. sequence length).- batch_size
Optional static batch size (integer).
- dtype
The data type expected by the input, as a string (e.g.
"float32","int32"...)- sparse
A boolean specifying whether the expected input will be sparse tensors. Note that, if
sparseisFALSE, sparse tensors can still be passed into the input - they will be densified with a default value of 0. This feature is only supported with the TensorFlow backend. Defaults toFALSE.- batch_shape
Optional shape list (list of integers or
NULLobjects), including the batch size.- name
Optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
- tensor
Optional existing tensor to wrap into the
Inputlayer. If set, the layer will use this tensor rather than creating a new placeholder tensor.- optional
Boolean, whether the input is optional or not. An optional input can accept
NULLvalues.
Value
A Keras tensor,
which can passed to the inputs argument of (keras_model()).
Examples
# This is a logistic regression in Keras
input <- layer_input(shape=c(32))
output <- input |> layer_dense(16, activation='softmax')
model <- keras_model(input, output)See also
Other model creation: keras_model() keras_model_sequential()