tf.contrib.distributions.MultivariateNormalFull.sigma Dense (batch) covariance matrix, if available.

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.QueueBase.enqueue_many(vals, name=None) Enqueues zero or more elements to this queue. This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tensors in vals must have the same size in the 0th dimension. If the queue is full when this operation executes, it will block until all of the elements have been enqueued. At runtime, this operation may raise an error if the queue is closed before or during its execution. If the queue is closed

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.