tf.contrib.learn.monitors.GraphDump.run_on_all_workers

tf.contrib.distributions.BernoulliWithSigmoidP.mean(name='mean') Mean.

tf.nn.rnn_cell.OutputProjectionWrapper.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors wit

tf.contrib.distributions.Categorical.num_classes Scalar int32 tensor: the number of classes.

TTypes< T, NDIMS >::ConstTensor tensorflow::Tensor::flat_outer_dims() const

tf.contrib.distributions.NormalWithSoftplusSigma.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_

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

tf.contrib.learn.monitors.SummaryWriterCache.clear() Clear cached summary writers. Currently only used for unit tests.

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.