tf.contrib.distributions.Bernoulli.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is

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.NormalWithSoftplusSigma.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.TensorArray.unpack(value, name=None) Pack the values of a Tensor in the TensorArray. Args: value: (N+1)-D. Tensor of type dtype. The Tensor to unpack. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the unpack occurs. Use this object all for subsequent operations. Raises: ValueError: if the shape inference fails.

tf.image.decode_png(contents, channels=None, dtype=None, name=None) Decode a PNG-encoded image to a uint8 or uint16 tensor. The attr channels indicates the desired number of color channels for the decoded image. Accepted values are: 0: Use the number of channels in the PNG-encoded image. 1: output a grayscale image. 3: output an RGB image. 4: output an RGBA image. If needed, the PNG-encoded image is transformed to match the requested number of color channels. Args: contents: A Tensor of typ

Configuration information for a Session . Member Details Env* tensorflow::SessionOptions::env The environment to use. string tensorflow::SessionOptions::target The TensorFlow runtime to connect to. If 'target' is empty or unspecified, the local TensorFlow runtime implementation will be used. Otherwise, the TensorFlow engine defined by 'target' will be used to perform all computations. "target" can be either a single entry or a comma separated list of entries. Each entry is a resolvable address

tf.contrib.learn.monitors.ExportMonitor.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.