tf.contrib.learn.Estimator.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.Estimator.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.clone(name=None, **dist_args)

tf.contrib.distributions.Multinomial.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None) Outputs random values from a uniform distribution. The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded. For floats, the default range is [0, 1). For ints, at least maxval must be specified explicitly. In the integer case, the random integers are slightly biased unless maxval - minval is an

tf.contrib.learn.LinearRegressor.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.sparse_reshape(sp_input, shape, name=None) Reshapes a SparseTensor to represent values in a new dense shape. This operation has the same semantics as reshape on the represented dense tensor. The indices of non-empty values in sp_input are recomputed based on the new dense shape, and a new SparseTensor is returned containing the new indices and new shape. The order of non-empty values in sp_input is unchanged. If one component of shape is the special value -1, the size of that dimension is c

tf.contrib.learn.TensorFlowRNNClassifier.__repr__()

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

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.output_size