tf.WholeFileReader.reader_ref Op that implements the reader.

tf.contrib.distributions.WishartCholesky.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)

tf.sub(x, y, name=None) Returns x - y element-wise. NOTE: Sub supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

Options to configure a Thread . Note that the options are all hints, and the underlying implementation may choose to ignore it. Member Details size_t tensorflow::ThreadOptions::stack_size Thread stack size to use (in bytes). size_t tensorflow::ThreadOptions::guard_size Guard area size to use near thread stacks to use (in bytes)

tf.contrib.distributions.Distribution.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) +

tf.contrib.metrics.streaming_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the mean squared error between the labels and predictions. The streaming_mean_squared_error function creates two local variables, total and count that are used to compute the mean squared error. This average is weighted by weights, and it is ultimately returned as mean_squared_error: an idempotent operation that simply divides total by count

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

class tf.QueueBase Base class for queue implementations. A queue is a TensorFlow data structure that stores tensors across multiple steps, and exposes operations that enqueue and dequeue tensors. Each queue element is a tuple of one or more tensors, where each tuple component has a static dtype, and may have a static shape. The queue implementations support versions of enqueue and dequeue that handle single elements, versions that support enqueuing and dequeuing a batch of elements at once. Se

tf.sparse_reset_shape(sp_input, new_shape=None) Resets the shape of a SparseTensor with indices and values unchanged. If new_shape is None, returns a copy of sp_input with its shape reset to the tight bounding box of sp_input. If new_shape is provided, then it must be larger or equal in all dimensions compared to the shape of sp_input. When this condition is met, the returned SparseTensor will have its shape reset to new_shape and its indices and values unchanged from that of sp_input. For exa

tf.sparse_concat(concat_dim, sp_inputs, name=None, expand_nonconcat_dim=False) Concatenates a list of SparseTensor along the specified dimension. Concatenation is with respect to the dense versions of each sparse input. It is assumed that each inputs is a SparseTensor whose elements are ordered along increasing dimension number. If expand_nonconcat_dim is False, all inputs' shapes must match, except for the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are allowd to va