tf.contrib.graph_editor.transform_op_if_inside_handler(info, op, keep_if_possible=True) Transform an optional op only if it is inside the subgraph. This handler is typically use to handle original op: it is fine to keep them if they are inside the subgraph, otherwise they are just ignored. Args: info: Transform._Info instance. op: the optional op to transform (or ignore). keep_if_possible: re-attach to the original op if possible, that is, if the source graph and the destination graph are t

tf.decode_raw(bytes, out_type, little_endian=None, name=None) Reinterpret the bytes of a string as a vector of numbers. Args: bytes: A Tensor of type string. All the elements must have the same length. out_type: A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.int64. little_endian: An optional bool. Defaults to True. Whether the input bytes are in little-endian order. Ignored for out_type values that are stored in a single byte like uint8. name: A name for

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

tf.contrib.distributions.Dirichlet.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_shape(x) + sel

tf.delete_session_tensor(handle, name=None) Delete the tensor for the given tensor handle. This is EXPERIMENTAL and subject to change. Delete the tensor of a given tensor handle. The tensor is produced in a previous run() and stored in the state of the session. Args: handle: The string representation of a persistent tensor handle. name: Optional name prefix for the return tensor. Returns: A pair of graph elements. The first is a placeholder for feeding a tensor handle and the second is a d

tf.image.grayscale_to_rgb(images, name=None) Converts one or more images from Grayscale to RGB. Outputs a tensor of the same DType and rank as images. The size of the last dimension of the output is 3, containing the RGB value of the pixels. Args: images: The Grayscale tensor to convert. Last dimension must be size 1. name: A name for the operation (optional). Returns: The converted grayscale image(s).

tf.atan(x, name=None) Computes atan of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.InverseGamma.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.Normal.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.