`Input`

function```
keras.Input(
shape=None,
batch_size=None,
dtype=None,
sparse=None,
batch_shape=None,
name=None,
tensor=None,
)
```

Used to instantiate a Keras tensor.

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 = Model(input=[a, b], output=c)`

**Arguments**

**shape**: A shape tuple (tuple of integers or`None`

objects), not including the batch size. For instance,`shape=(32,)`

indicates that the expected input will be batches of 32-dimensional vectors. Elements of this tuple can be`None`

;`None`

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`

.**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.

**Returns**

A Keras tensor.

**Example**

```
# This is a logistic regression in Keras
x = Input(shape=(32,))
y = Dense(16, activation='softmax')(x)
model = Model(x, y)
```