tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma') Construct Gamma

tf.contrib.learn.LinearRegressor.set_params(**params) Set the parameters of this estimator. The

tf.contrib.distributions.Distribution.log_prob(value, name='log_prob') Log probability density/mass function (depending on

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.dtype

tf.contrib.distributions.ExponentialWithSoftplusLam.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape)

tf.contrib.distributions.Binomial.is_reparameterized

tf.contrib.distributions.WishartFull.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given

tf.contrib.distributions.TransformedDistribution.dtype The DType of Tensors handled by this Distribution

tf.contrib.learn.monitors.StopAtStep.begin(max_steps=None) Called at the beginning of training. When

tf.contrib.distributions.Categorical.get_batch_shape() Shape of a single sample from a single event index as a TensorShape