tf.contrib.distributions.Beta.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.contrib.distributions.DirichletMultinomial.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.image.convert_image_dtype(image, dtype, saturate=False, name=None) Convert image to dtype, scaling its values if needed. Images that are represented using floating point values are expected to have values in the range [0,1). Image data stored in integer data types are expected to have values in the range [0,MAX], where MAX is the largest positive representable number for the data type. This op converts between data types, scaling the values appropriately before casting. Note that converting

class tf.TensorArray Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays. This class is meant to be used with dynamic iteration primitives such as while_loop and map_fn. It supports gradient back-propagation via special "flow" control flow dependencies.

tf.contrib.learn.LinearRegressor.dnn_bias_ Returns bias of deep neural network part.

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value(name='value')

tf.contrib.distributions.StudentT.dtype The DType of Tensors handled by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.name

tf.TensorArray.read(index, name=None) Read the value at location index in the TensorArray. Args: index: 0-D. int32 tensor with the index to read from. name: A name for the operation (optional). Returns: The tensor at index index.

tf.contrib.distributions.Multinomial.n Number of trials.