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

tf.contrib.graph_editor.swap_inputs(sgv0, sgv1) Swap all the inputs of sgv0 and sgv1 (see reroute_inputs).

tf.contrib.rnn.GridLSTMCell.state_tuple_type

tf.contrib.training.SequenceQueueingStateSaver.batch_size

tf.contrib.distributions.Uniform.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.

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.distribution

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

tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See Evaluable. Raises: ValueError: If at least one of x or y is provided, and at least one of input_fn or feed_fn is provided. Or if metrics is not None or dict.

tf.contrib.distributions.DirichletMultinomial.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

class tf.contrib.graph_editor.matcher Graph match class.