Draw samples from a Binomial distribution.

The values are drawn from a Binomial distribution with specified trial count and probability of success.

Usage

random_binomial(shape, counts, probabilities, dtype = NULL, seed = NULL)

Arguments

shape

The shape of the random values to generate.

counts

A number or array of numbers representing the number of trials. It must be broadcastable with probabilities.

probabilities

A float or array of floats representing the probability of success of an individual event. It must be broadcastable with counts.

dtype

Optional dtype of the tensor. Only floating point types are supported. If not specified, config_floatx() is used, which defaults to "float32" unless you configured it otherwise (via config_set_floatx(float_dtype)).

seed

A Python integer or instance of random_seed_generator(). Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or None (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of random_seed_generator().

Value

A tensor of random values.

See also

• https://www.tensorflow.org/api_docs/python/tf/keras/random/binomial

Other random:
random_beta()
random_categorical()
random_dropout()
random_gamma()
random_integer()
random_normal()
random_seed_generator()
random_shuffle()
random_truncated_normal()
random_uniform()