Python tensorflow.python.ops.gen_array_ops._squeeze() Examples
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code examples of tensorflow.python.ops.gen_array_ops._squeeze().
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Example #1
| Source File: array_ops.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def squeeze(input, squeeze_dims=None, name=None):
# pylint: disable=redefined-builtin
"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all dimensions of size 1 removed. If you don't want to remove all size 1
dimensions, you can remove specific size 1 dimensions by specifying
`squeeze_dims`.
For example:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
```
Or, to remove specific size 1 dimensions:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
```
Args:
input: A `Tensor`. The `input` to squeeze.
squeeze_dims: An optional list of `ints`. Defaults to `[]`.
If specified, only squeezes the dimensions listed. The dimension
index starts at 0. It is an error to squeeze a dimension that is not 1.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
Contains the same data as `input`, but has one or more dimensions of
size 1 removed.
"""
if np.isscalar(squeeze_dims):
squeeze_dims = [squeeze_dims]
return gen_array_ops._squeeze(input, squeeze_dims, name)
Example #2
| Source File: array_ops.py From lambda-packs with MIT License | 4 votes |
|
def squeeze(input, axis=None, name=None, squeeze_dims=None):
# pylint: disable=redefined-builtin
"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all dimensions of size 1 removed. If you don't want to remove all size 1
dimensions, you can remove specific size 1 dimensions by specifying
`axis`.
For example:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
```
Or, to remove specific size 1 dimensions:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
```
Args:
input: A `Tensor`. The `input` to squeeze.
axis: An optional list of `ints`. Defaults to `[]`.
If specified, only squeezes the dimensions listed. The dimension
index starts at 0. It is an error to squeeze a dimension that is not 1.
name: A name for the operation (optional).
squeeze_dims: Deprecated keyword argument that is now axis.
Returns:
A `Tensor`. Has the same type as `input`.
Contains the same data as `input`, but has one or more dimensions of
size 1 removed.
Raises:
ValueError: When both `squeeze_dims` and `axis` are specified.
"""
if squeeze_dims is not None:
if axis is not None:
raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
axis = squeeze_dims
if np.isscalar(axis):
axis = [axis]
return gen_array_ops._squeeze(input, axis, name)
Example #3
| Source File: array_ops.py From auto-alt-text-lambda-api with MIT License | 4 votes |
|
def squeeze(input, axis=None, name=None, squeeze_dims=None):
# pylint: disable=redefined-builtin
"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all dimensions of size 1 removed. If you don't want to remove all size 1
dimensions, you can remove specific size 1 dimensions by specifying
`axis`.
For example:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
```
Or, to remove specific size 1 dimensions:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
```
Args:
input: A `Tensor`. The `input` to squeeze.
axis: An optional list of `ints`. Defaults to `[]`.
If specified, only squeezes the dimensions listed. The dimension
index starts at 0. It is an error to squeeze a dimension that is not 1.
name: A name for the operation (optional).
squeeze_dims: Deprecated keyword argument that is now axis.
Returns:
A `Tensor`. Has the same type as `input`.
Contains the same data as `input`, but has one or more dimensions of
size 1 removed.
Raises:
ValueError: When both `squeeze_dims` and `axis` are specified.
"""
if squeeze_dims is not None:
if axis is not None:
raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
axis = squeeze_dims
if np.isscalar(axis):
axis = [axis]
return gen_array_ops._squeeze(input, axis, name)
Example #4
| Source File: array_ops.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
|
def squeeze(input, axis=None, name=None, squeeze_dims=None):
# pylint: disable=redefined-builtin
"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all dimensions of size 1 removed. If you don't want to remove all size 1
dimensions, you can remove specific size 1 dimensions by specifying
`axis`.
For example:
```python
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
tf.shape(tf.squeeze(t)) # [2, 3]
```
Or, to remove specific size 1 dimensions:
```python
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
tf.shape(tf.squeeze(t, [2, 4])) # [1, 2, 3, 1]
```
Args:
input: A `Tensor`. The `input` to squeeze.
axis: An optional list of `ints`. Defaults to `[]`.
If specified, only squeezes the dimensions listed. The dimension
index starts at 0. It is an error to squeeze a dimension that is not 1.
Must be in the range `[-rank(input), rank(input))`.
name: A name for the operation (optional).
squeeze_dims: Deprecated keyword argument that is now axis.
Returns:
A `Tensor`. Has the same type as `input`.
Contains the same data as `input`, but has one or more dimensions of
size 1 removed.
Raises:
ValueError: When both `squeeze_dims` and `axis` are specified.
"""
if squeeze_dims is not None:
if axis is not None:
raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
axis = squeeze_dims
if np.isscalar(axis):
axis = [axis]
return gen_array_ops._squeeze(input, axis, name)
Example #5
| Source File: array_ops.py From keras-lambda with MIT License | 4 votes |
|
def squeeze(input, axis=None, name=None, squeeze_dims=None):
# pylint: disable=redefined-builtin
"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all dimensions of size 1 removed. If you don't want to remove all size 1
dimensions, you can remove specific size 1 dimensions by specifying
`axis`.
For example:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
```
Or, to remove specific size 1 dimensions:
```prettyprint
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
```
Args:
input: A `Tensor`. The `input` to squeeze.
axis: An optional list of `ints`. Defaults to `[]`.
If specified, only squeezes the dimensions listed. The dimension
index starts at 0. It is an error to squeeze a dimension that is not 1.
name: A name for the operation (optional).
squeeze_dims: Deprecated keyword argument that is now axis.
Returns:
A `Tensor`. Has the same type as `input`.
Contains the same data as `input`, but has one or more dimensions of
size 1 removed.
Raises:
ValueError: When both `squeeze_dims` and `axis` are specified.
"""
if squeeze_dims is not None:
if axis is not None:
raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
axis = squeeze_dims
if np.isscalar(axis):
axis = [axis]
return gen_array_ops._squeeze(input, axis, name)
