tf.random_gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None) Draws shape samples from each of the given Gamma distribution(s). alpha is the shape parameter describing the distribution(s), and beta is the inverse scale parameter(s). Example: samples = tf.random_gamma([10], [0.5, 1.5]) # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents # the samples drawn from each distribution samples = tf.random_gamma([7, 5], [0.5, 1.5]) # samples has shape [7, 5, 2]

tf.SparseTensor.__mul__(sp_x, y) Component-wise multiplies a SparseTensor by a dense Tensor. The output locations corresponding to the implicitly zero elements in the sparse tensor will be zero (i.e., will not take up storage space), regardless of the contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). Limitation: this Op only broadcasts the dense side to the sparse side, but not the other direction. Args: sp_indices: A Tensor of type int64. 2-D. N x R matrix with the in

tf.contrib.distributions.Laplace.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.framework.variable(*args, **kwargs) Gets an existing variable with these parameters or creates a new one. Args: name: the name of the new or existing variable. shape: shape of the new or existing variable. dtype: type of the new or existing variable (defaults to DT_FLOAT). initializer: initializer for the variable if one is created. regularizer: a (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the collection GraphK

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.name

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.

tf.contrib.learn.LinearRegressor.predict(*args, **kwargs) Returns predictions for given features. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below. Args: x: Matrix of shape [n_samples, n_features...]. Can be iterator that retur

tf.TFRecordReader.num_work_units_completed(name=None) Returns the number of work units this reader has finished processing. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

tf.image.random_hue(image, max_delta, seed=None) Adjust the hue of an RGB image by a random factor. Equivalent to adjust_hue() but uses a delta randomly picked in the interval [-max_delta, max_delta]. max_delta must be in the interval [0, 0.5]. Args: image: RGB image or images. Size of the last dimension must be 3. max_delta: float. Maximum value for the random delta. seed: An operation-specific seed. It will be used in conjunction with the graph-level seed to determine the real seeds that

tf.contrib.graph_editor.make_placeholder_from_dtype_and_shape(dtype, shape=None, scope=None) Create a tf.placeholder for the Graph Editor. Note that the correct graph scope must be set by the calling function. The placeholder is named using the function placeholder_name (with no tensor argument). Args: dtype: the tensor type. shape: the tensor shape (optional). scope: absolute scope within which to create the placeholder. None means that the scope of t is preserved. "" means the root scope.