tf.contrib.distributions.ExponentialWithSoftplusLam.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.

Status tensorflow::Env::NewReadOnlyMemoryRegionFromFile(const string &fname, std::unique_ptr< ReadOnlyMemoryRegion > *result) Creates a readonly region of memory with the file context. On success, it returns a pointer to read-only memory region from the content of file fname. The ownership of the region is passed to the caller. On failure stores nullptr in *result and returns non-OK. The returned memory region can be accessed from many threads in parallel. The ownership of the return

tf.contrib.distributions.Dirichlet.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Normal.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='Normal') Construct Normal distributions with mean and stddev mu and sigma. The parameters mu and sigma must be shaped in a way that supports broadcasting (e.g. mu + sigma is a valid operation). Args: mu: Floating point tensor, the means of the distribution(s). sigma: Floating point tensor, the stddevs of the distribution(s). sigma must contain only positive values. validate_args: Boolean, de

tf.contrib.learn.TensorFlowRNNClassifier.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be remove

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.variance(name='variance') Variance.

tf.contrib.learn.monitors.PrintTensor.every_n_step_end(step, outputs)

tf.contrib.distributions.Multinomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Multinomial') Initialize a batch of Multinomial distributions. Args: n: Non-negative floating point tensor with shape broadcastable to [N1,..., Nm] with m >= 0. Defines this as a batch of N1 x ... x Nm different Multinomial distributions. Its components should be equal to integer values. logits: Floating point tensor representing the log-odds of a positive event with shap

tf.nn.rnn_cell.BasicRNNCell.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 with the shape

class tf.contrib.distributions.WishartFull The matrix Wishart distribution on positive definite matrices. This distribution is defined by a scalar degrees of freedom df and a symmetric, positive definite scale matrix. Evaluation of the pdf, determinant, and sampling are all O(k^3) operations where (k, k) is the event space shape.