class tf.contrib.distributions.Uniform Uniform distribution with a and b parameters. The PDF of this distribution is constant between [a, b], and 0 elsewhere.

tf.nn.rnn_cell.GRUCell.state_size

tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_shape(x) + s

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.mean(name='mean')

tf.contrib.learn.TensorFlowRNNClassifier.model_dir

tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See Trainable. Raises: ValueError: If x or y are not None while input_fn is not None. ValueError: If both steps and max_steps are not None.

tf.contrib.learn.monitors.PrintTensor.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.FixedLenSequenceFeature.dtype Alias for field number 1