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

`NULL`

objects), 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 be`NULL`

or`NA`

;`NULL`

/`NA`

elements 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

`sparse`

is`FALSE`

, 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 to`FALSE`

.- batch_shape
Shape, including the batch dim.

- 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

`Input`

layer. 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

`NULL`

values.

## 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()`