def zeros_like(tensor, dtype=None, name=None):
"""Creates a tensor with all elements set to zero.
Given a single tensor (`tensor`), this operation returns a tensor of the
same type and shape as `tensor` with all elements set to zero. Optionally,
you can use `dtype` to specify a new type for the returned tensor.
For example:
```python
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
```
Args:
tensor: A `Tensor`.
dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
`int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
name: A name for the operation (optional).
Returns:
A `Tensor` with all elements set to zero.
"""
with ops.op_scope([tensor], name, "zeros_like") as name:
tensor = ops.convert_to_tensor(tensor, name="tensor")
ret = gen_array_ops._zeros_like(tensor)
if (dtype is not None) and (tensor.dtype != dtype):
ret = gen_math_ops.cast(ret, dtype)
return ret
def constants_like(tensor, value, dtype=None, name=None, optimize=True):
"""Creates a tensor with all elements set to zero.
Given a single tensor (`tensor`), this operation returns a tensor of the
same type and shape as `tensor` with all elements set to zero. Optionally,
you can use `dtype` to specify a new type for the returned tensor.
For example:
```python
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
```
Args:
tensor: A `Tensor`.
dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
`int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`.
name: A name for the operation (optional).
optimize: if true, attempt to statically determine the shape of 'tensor'
and encode it as a constant.
Returns:
A `Tensor` with all elements set to zero.
"""
with ops.name_scope(name, "constants_like", [tensor]) as name:
tensor = ops.convert_to_tensor(tensor, name="tensor")
if dtype is not None and tensor.dtype != dtype:
ret = constants(value, shape_internal(tensor, optimize=optimize), dtype, name=name)
ret.set_shape(tensor.get_shape())
return ret
else:
#TODO better handle
return gen_array_ops._zeros_like(tensor, name=name) + value
def zeros_like(tensor, dtype=None, name=None):
"""Creates a tensor with all elements set to zero.
Given a single tensor (`tensor`), this operation returns a tensor of the
same type and shape as `tensor` with all elements set to zero. Optionally,
you can use `dtype` to specify a new type for the returned tensor.
For example:
```python
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
```
Args:
tensor: A `Tensor`.
dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
`int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
name: A name for the operation (optional).
Returns:
A `Tensor` with all elements set to zero.
"""
with ops.op_scope([tensor], name, "zeros_like") as name:
tensor = ops.convert_to_tensor(tensor, name="tensor")
if dtype is not None and tensor.dtype != dtype:
ret = zeros(shape(tensor), dtype, name=name)
ret.set_shape(tensor.get_shape())
return ret
else:
return gen_array_ops._zeros_like(tensor, name=name)
def testGenZerosLike(self):
for dtype in [tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64]:
numpy_dtype = dtype.as_numpy_dtype
with self.test_session():
# Creates a tensor of non-zero values with shape 2 x 3.
d = tf.constant(np.ones((2, 3), dtype=numpy_dtype), dtype=dtype)
# Constructs a tensor of zeros of the same dimensions and type as "d".
z_var = gen_array_ops._zeros_like(d)
# Test that the type is correct
self.assertEqual(z_var.dtype, dtype)
z_value = z_var.eval()
# Test that the value is correct
self.assertTrue(np.array_equal(z_value, np.array([[0] * 3] * 2)))
self.assertEqual([2, 3], z_var.get_shape())
def testGenZerosLike(self):
for dtype in [tf.float32, tf.float64, tf.int32,
tf.uint8, tf.int16, tf.int8,
tf.complex64, tf.int64]:
numpy_dtype = dtype.as_numpy_dtype
with self.test_session():
# Creates a tensor of non-zero values with shape 2 x 3.
d = tf.constant(np.ones((2, 3), dtype=numpy_dtype), dtype=dtype)
# Constructs a tensor of zeros of the same dimensions and type as "d".
z_var = gen_array_ops._zeros_like(d)
# Test that the type is correct
self.assertEqual(z_var.dtype, dtype)
z_value = z_var.eval()
# Test that the value is correct
self.assertTrue(np.array_equal(z_value, np.array([[0] * 3] * 2)))
self.assertEqual([2, 3], z_var.get_shape())
