def testDTypeCombinationsV2(self):
"""Tests random_poisson_v2() for all supported dtype combinations."""
# All supported dtypes by random_poisson_v2().
supported_dtypes = [
dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
dtypes.int64
]
with self.test_session():
for lam_dt in supported_dtypes:
for out_dt in supported_dtypes:
# TODO(dhananjayn): Change this to use random_poisson() after
# switching it to RandomPoissonV2.
gen_random_ops.random_poisson_v2(
[10], constant_op.constant([1], dtype=lam_dt),
dtype=out_dt).eval()
def testDTypeCombinationsV2(self):
"""Tests random_poisson_v2() for all supported dtype combinations."""
# All supported dtypes by random_poisson_v2().
supported_dtypes = [
dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
dtypes.int64
]
with self.test_session():
for lam_dt in supported_dtypes:
for out_dt in supported_dtypes:
# TODO(dhananjayn): Change this to use random_poisson() after
# switching it to RandomPoissonV2.
gen_random_ops.random_poisson_v2([10],
constant_op.constant(
[1], dtype=lam_dt),
dtype=out_dt).eval()
def random_poisson_v2(shape, lam, dtype=dtypes.float32, seed=None, name=None):
"""Draws `shape` samples from each of the given Poisson distribution(s).
`lam` is the rate parameter describing the distribution(s).
Example:
```python
samples = tf.random.poisson([10], [0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random.poisson([7, 5], [12.2, 3.3])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output samples
to be drawn per "rate"-parameterized distribution.
lam: A Tensor or Python value or N-D array of type `dtype`.
`lam` provides the rate parameter(s) describing the poisson
distribution(s) to sample.
dtype: The type of the output: `float16`, `float32`, `float64`, `int32` or
`int64`.
seed: A Python integer. Used to create a random seed for the distributions.
See
`tf.random.set_seed`
for behavior.
name: Optional name for the operation.
Returns:
samples: a `Tensor` of shape `tf.concat([shape, tf.shape(lam)], axis=0)`
with values of type `dtype`.
"""
with ops.name_scope(name, "random_poisson", [lam, shape]):
shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
seed1, seed2 = random_seed.get_seed(seed)
result = gen_random_ops.random_poisson_v2(shape,
lam,
dtype=dtype,
seed=seed1,
seed2=seed2)
_maybe_set_static_shape_helper(result, shape, lam)
return result
def random_poisson_v2(shape, lam, dtype=dtypes.float32, seed=None, name=None):
"""Draws `shape` samples from each of the given Poisson distribution(s).
`lam` is the rate parameter describing the distribution(s).
Example:
```python
samples = tf.random_poisson([10], [0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random_poisson([7, 5], [12.2, 3.3])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output samples
to be drawn per "rate"-parameterized distribution.
lam: A Tensor or Python value or N-D array of type `dtype`.
`lam` provides the rate parameter(s) describing the poisson
distribution(s) to sample.
dtype: The type of the output: `float16`, `float32`, `float64`, `int32` or
`int64`.
seed: A Python integer. Used to create a random seed for the distributions.
See
`tf.set_random_seed`
for behavior.
name: Optional name for the operation.
Returns:
samples: a `Tensor` of shape `tf.concat([shape, tf.shape(lam)], axis=0)`
with values of type `dtype`.
"""
with ops.name_scope(name, "random_poisson", [lam, shape]):
shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.random_poisson_v2(
shape, lam, dtype=dtype, seed=seed1, seed2=seed2)
