tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or doubl

tf.abs(x, name=None) Computes the absolute value of a tensor. Given a tensor of real numbers x, this operation returns a tensor containing the absolute value of each element in x. For example, if x is an input element and y is an output element, this operation computes \(y = |x|\). See tf.complex_abs() to compute the absolute value of a complex number. Args: x: A Tensor or SparseTensor of type float32, float64, int32, or int64. name: A name for the operation (optional). Returns: A Tensor o

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: val

class tf.FixedLenFeature Configuration for parsing a fixed-length input feature. To treat sparse input as dense, provide a default_value; otherwise, the parse functions will fail on any examples missing this feature. Fields: shape: Shape of input data. dtype: Data type of input. default_value: Value to be used if an example is missing this feature. It must be compatible with dtype.

tf.image.hsv_to_rgb(images, name=None) Convert one or more images from HSV to RGB. Outputs a tensor of the same shape as the images tensor, containing the RGB value of the pixels. The output is only well defined if the value in images are in [0,1]. See rgb_to_hsv for a description of the HSV encoding. Args: images: A Tensor. Must be one of the following types: float32, float64. 1-D or higher rank. HSV data to convert. Last dimension must be size 3. name: A name for the operation (optional).

tf.contrib.learn.LinearClassifier.get_variable_names()

tf.contrib.distributions.StudentT.validate_args Python boolean indicated possibly expensive checks are enabled.

size_t tensorflow::ThreadOptions::guard_size Guard area size to use near thread stacks to use (in bytes)

TTypes< T, NDIMS >::Tensor tensorflow::Tensor::bit_casted_tensor() Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T. Using a bitcast is useful for move and copy operations. NOTE: this is the same as tensor() except a bitcast is allowed.

tf.matrix_band_part(input, num_lower, num_upper, name=None) Copy a tensor setting everything outside a central band in each innermost matrix to zero. The band part is computed as follows: Assume input has k dimensions [I, J, K, ..., M, N], then the output is a tensor with the same shape where band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]. The indicator function 'in_band(m, n)is one if(num_lower < 0 || (m-n) <= num_lower)) && (num_upper < 0 || (n-m) <=