tensorflow_backend¶
-
class
pyhf.tensor.tensorflow_backend.
tensorflow_backend
(**kwargs)[source]¶ Bases:
object
TensorFlow backend for pyhf
Methods
-
astensor
(tensor_in, dtype='float')[source]¶ Convert to a TensorFlow Tensor.
- Parameters
tensor_in (Number or Tensor) – Tensor object
- Returns
A symbolic handle to one of the outputs of a tf.Operation.
- Return type
tf.Tensor
-
clip
(tensor_in, min_value, max_value)[source]¶ Clips (limits) the tensor values to be within a specified min and max.
Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> a = pyhf.tensorlib.astensor([-2, -1, 0, 1, 2]) >>> t = pyhf.tensorlib.clip(a, -1, 1) >>> print(t) tf.Tensor([-1. -1. 0. 1. 1.], shape=(5,), dtype=float32)
- Parameters
tensor_in (tensor) – The input tensor object
min_value (scalar or tensor or None) – The minimum value to be cliped to
max_value (scalar or tensor or None) – The maximum value to be cliped to
- Returns
A clipped tensor
- Return type
TensorFlow Tensor
-
concatenate
(sequence, axis=0)[source]¶ Join a sequence of arrays along an existing axis.
- Parameters
sequence – sequence of tensors
axis – dimension along which to concatenate
- Returns
the concatenated tensor
- Return type
output
-
conditional
(predicate, true_callable, false_callable)[source]¶ Runs a callable conditional on the boolean value of the evaulation of a predicate
Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> tensorlib = pyhf.tensorlib >>> a = tensorlib.astensor([4]) >>> b = tensorlib.astensor([5]) >>> t = tensorlib.conditional((a < b)[0], lambda: a + b, lambda: a - b) >>> print(t) tf.Tensor([9.], shape=(1,), dtype=float32)
- Parameters
predicate (scalar) – The logical condition that determines which callable to evaluate
true_callable (callable) – The callable that is evaluated when the
predicate
evalutes totrue
false_callable (callable) – The callable that is evaluated when the
predicate
evalutes tofalse
- Returns
The output of the callable that was evaluated
- Return type
TensorFlow Tensor
-
einsum
(subscripts, *operands)[source]¶ A generalized contraction between tensors of arbitrary dimension.
This function returns a tensor whose elements are defined by equation, which is written in a shorthand form inspired by the Einstein summation convention.
- Parameters
subscripts – str, specifies the subscripts for summation
operands – list of array_like, these are the tensors for the operation
- Returns
the calculation based on the Einstein summation convention
- Return type
TensorFlow Tensor
-
normal
(x, mu, sigma)[source]¶ The probability density function of the Normal distribution evaluated at
x
given parameters of mean ofmu
and standard deviation ofsigma
.Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> t = pyhf.tensorlib.normal(0.5, 0., 1.) >>> print(t) tf.Tensor(0.35206532, shape=(), dtype=float32) >>> values = pyhf.tensorlib.astensor([0.5, 2.0]) >>> means = pyhf.tensorlib.astensor([0., 2.3]) >>> sigmas = pyhf.tensorlib.astensor([1., 0.8]) >>> t = pyhf.tensorlib.normal(values, means, sigmas) >>> print(t) tf.Tensor([0.35206532 0.46481887], shape=(2,), dtype=float32)
- Parameters
x (tensor or float) – The value at which to evaluate the Normal distribution p.d.f.
mu (tensor or float) – The mean of the Normal distribution
sigma (tensor or float) – The standard deviation of the Normal distribution
- Returns
Value of Normal(x|mu, sigma)
- Return type
TensorFlow Tensor
-
normal_cdf
(x, mu=0.0, sigma=1)[source]¶ Compute the value of cumulative distribution function for the Normal distribution at x.
Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> t = pyhf.tensorlib.normal_cdf(0.8) >>> print(t) tf.Tensor(0.7881446, shape=(), dtype=float32) >>> values = pyhf.tensorlib.astensor([0.8, 2.0]) >>> t = pyhf.tensorlib.normal_cdf(values) >>> print(t) tf.Tensor([0.7881446 0.97724986], shape=(2,), dtype=float32)
- Parameters
x (tensor or float) – The observed value of the random variable to evaluate the CDF for
mu (tensor or float) – The mean of the Normal distribution
sigma (tensor or float) – The standard deviation of the Normal distribution
- Returns
The CDF
- Return type
TensorFlow Tensor
-
normal_dist
(mu, sigma)[source]¶ Construct a Normal distribution with mean
mu
and standard deviationsigma
.Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> means = pyhf.tensorlib.astensor([5, 8]) >>> stds = pyhf.tensorlib.astensor([1, 0.5]) >>> values = pyhf.tensorlib.astensor([4, 9]) >>> normals = pyhf.tensorlib.normal_dist(means, stds) >>> t = normals.log_prob(values) >>> print(t) tf.Tensor([-1.4189385 -2.2257915], shape=(2,), dtype=float32)
- Parameters
mu (tensor or float) – The mean of the Normal distribution
sigma (tensor or float) – The standard deviation of the Normal distribution
- Returns
The Normal distribution class
- Return type
TensorFlow Probability Normal distribution
-
normal_logpdf
(x, mu, sigma)[source]¶ The log of the probability density function of the Normal distribution evaluated at
x
given parameters of mean ofmu
and standard deviation ofsigma
.Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> t = pyhf.tensorlib.normal_logpdf(0.5, 0., 1.) >>> print(t) tf.Tensor(-1.0439385, shape=(), dtype=float32) >>> values = pyhf.tensorlib.astensor([0.5, 2.0]) >>> means = pyhf.tensorlib.astensor([0., 2.3]) >>> sigmas = pyhf.tensorlib.astensor([1., 0.8]) >>> t = pyhf.tensorlib.normal_logpdf(values, means, sigmas) >>> print(t) tf.Tensor([-1.0439385 -0.7661075], shape=(2,), dtype=float32)
- Parameters
x (tensor or float) – The value at which to evaluate the Normal distribution p.d.f.
mu (tensor or float) – The mean of the Normal distribution
sigma (tensor or float) – The standard deviation of the Normal distribution
- Returns
Value of log(Normal(x|mu, sigma))
- Return type
TensorFlow Tensor
-
poisson
(n, lam)[source]¶ The continous approximation, using \(n! = \Gamma\left(n+1\right)\), to the probability mass function of the Poisson distribution evaluated at
n
given the parameterlam
.Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> t = pyhf.tensorlib.poisson(5., 6.) >>> print(t) tf.Tensor(0.16062315, shape=(), dtype=float32) >>> values = pyhf.tensorlib.astensor([5., 9.]) >>> rates = pyhf.tensorlib.astensor([6., 8.]) >>> t = pyhf.tensorlib.poisson(values, rates) >>> print(t) tf.Tensor([0.16062315 0.12407687], shape=(2,), dtype=float32)
- Parameters
n (tensor or float) – The value at which to evaluate the approximation to the Poisson distribution p.m.f. (the observed number of events)
lam (tensor or float) – The mean of the Poisson distribution p.m.f. (the expected number of events)
- Returns
Value of the continous approximation to Poisson(n|lam)
- Return type
TensorFlow Tensor
-
poisson_dist
(rate)[source]¶ Construct a Poisson distribution with rate parameter
rate
.Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> rates = pyhf.tensorlib.astensor([5, 8]) >>> values = pyhf.tensorlib.astensor([4, 9]) >>> poissons = pyhf.tensorlib.poisson_dist(rates) >>> t = poissons.log_prob(values) >>> print(t) tf.Tensor([-1.7403021 -2.086854 ], shape=(2,), dtype=float32)
- Parameters
rate (tensor or float) – The mean of the Poisson distribution (the expected number of events)
- Returns
The Poisson distribution class
- Return type
TensorFlow Probability Poisson distribution
-
poisson_logpdf
(n, lam)[source]¶ The log of the continous approximation, using \(n! = \Gamma\left(n+1\right)\), to the probability mass function of the Poisson distribution evaluated at
n
given the parameterlam
.Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> t = pyhf.tensorlib.poisson_logpdf(5., 6.) >>> print(t) tf.Tensor(-1.8286943, shape=(), dtype=float32) >>> values = pyhf.tensorlib.astensor([5., 9.]) >>> rates = pyhf.tensorlib.astensor([6., 8.]) >>> t = pyhf.tensorlib.poisson_logpdf(values, rates) >>> print(t) tf.Tensor([-1.8286943 -2.086854 ], shape=(2,), dtype=float32)
- Parameters
n (tensor or float) – The value at which to evaluate the approximation to the Poisson distribution p.m.f. (the observed number of events)
lam (tensor or float) – The mean of the Poisson distribution p.m.f. (the expected number of events)
- Returns
Value of the continous approximation to log(Poisson(n|lam))
- Return type
TensorFlow Tensor
-
simple_broadcast
(*args)[source]¶ Broadcast a sequence of 1 dimensional arrays.
Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> b = pyhf.tensorlib.simple_broadcast( ... pyhf.tensorlib.astensor([1]), ... pyhf.tensorlib.astensor([2, 3, 4]), ... pyhf.tensorlib.astensor([5, 6, 7])) >>> print([str(t) for t in b]) ['tf.Tensor([1. 1. 1.], shape=(3,), dtype=float32)', 'tf.Tensor([2. 3. 4.], shape=(3,), dtype=float32)', 'tf.Tensor([5. 6. 7.], shape=(3,), dtype=float32)']
- Parameters
args (Array of Tensors) – Sequence of arrays
- Returns
The sequence broadcast together.
- Return type
list of Tensors
-
tile
(tensor_in, repeats)[source]¶ Repeat tensor data along a specific dimension
Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> a = pyhf.tensorlib.astensor([[1.0], [2.0]]) >>> t = pyhf.tensorlib.tile(a, (1, 2)) >>> print(t) tf.Tensor( [[1. 1.] [2. 2.]], shape=(2, 2), dtype=float32)
- Parameters
tensor_in (Tensor) – The tensor to be repeated
repeats (Tensor) – The tuple of multipliers for each dimension
- Returns
The tensor with repeated axes
- Return type
TensorFlow Tensor
-
where
(mask, tensor_in_1, tensor_in_2)[source]¶ Apply a boolean selection mask to the elements of the input tensors.
Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> t = pyhf.tensorlib.where( ... pyhf.tensorlib.astensor([1, 0, 1], dtype='bool'), ... pyhf.tensorlib.astensor([1, 1, 1]), ... pyhf.tensorlib.astensor([2, 2, 2]), ... ) >>> print(t) tf.Tensor([1. 2. 1.], shape=(3,), dtype=float32)
- Parameters
mask (bool) – Boolean mask (boolean or tensor object of booleans)
tensor_in_1 (Tensor) – Tensor object
tensor_in_2 (Tensor) – Tensor object
- Returns
The result of the mask being applied to the tensors.
- Return type
TensorFlow Tensor
-