def reshape(tensor, shape, copy=True, name=None):
"""Change the dimensions of input.
Examples:
```python
# Provide a determined value for each dimension if possible
x = tf.ones(shape=(1, 2, 3, 4))
print(tf.reshape(x, shape=[6, 4]).shape) # [6, 4]
# Set the existing dimensions to ``0`` if it unchanged
print(tf.reshape(x, shape=[0, 0, 12]).shape) # [1, 2, 12]
print(tf.reshape(x, shape=[0, 0, 0, 0]).shape) # [1, 2, 3, 4]
print(tf.reshape(x, shape=[0, 0, 0, 0, 0]).shape) # Wrong
# You can also set ``-1`` once to infer the value
print(tf.reshape(x, shape=[-1, 4]).shape) # [6, 4]
print(tf.reshape(x, shape=[-1, -1]).shape) # Wrong
```
Parameters
----------
tensor : dragon.Tensor
The input tensor.
shape : Union[Sequence[int], dragon.Tensor]
The output shape.
copy : bool, optional, default=True
Return a new tensor or call in-place.
name : str, optional
The operation name.
"""
return array_ops.reshape(tensor, shape=shape, copy=copy, name=name)
def reshape(self, shape):
"""Return a tensor containing the same data with new shape.
Parameters
----------
shape : Sequence[int]
The new shape.
Returns
-------
dragon.EagerTensor
The output tensor.
See Also
--------
`dragon.reshape(...)`_
"""
with context.eager_mode():
return array_ops.reshape(self, shape=shape)
def reshape(self, shape, copy=True):
"""Return a tensor containing the same data with new shape.
Parameters
----------
shape : Union[Sequence[int], dragon.Tensor]
The output shape.
copy : bool, optional, default=True
Return a new tensor or reshape in-place.
Returns
-------
dragon.Tensor
The output tensor.
See Also
--------
`dragon.reshape(...)`_
"""
return array_ops.reshape(self, shape=shape, copy=copy)
def forward(self, inputs, **kwargs):
return array_ops.reshape(inputs, shape=self.shape)
def call(self, inputs):
return array_ops.reshape(inputs, shape=[0] + self.target_shape)
def __call__(self, bottom):
return array_ops.reshape(bottom, **self.arguments)
def __call__(self, bottom):
return array_ops.reshape(bottom, **self.call_args)
