tf.contrib.distributions.TransformedDistribution.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.TransformedDistribution.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.Normal.sigma Distribution parameter for standard deviation.

class tf.contrib.learn.LinearRegressor Linear regressor model. Train a linear regression model to predict target variable value given observation of feature values. Example: education = sparse_column_with_hash_bucket(column_name="education", hash_bucket_size=1000) occupation = sparse_column_with_hash_bucket(column_name="occupation", hash_bucket_size=1000) education_x_occupation = crossed_column(columns=[edu

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.mean(name='mean')

tf.contrib.distributions.Gamma.entropy(name='entropy') Shanon entropy in nats. Additional documentation from Gamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

uint64 tensorflow::EnvWrapper::NowMicros() override Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.

void tensorflow::EnvWrapper::SleepForMicroseconds(int64 micros) override Sleeps/delays the thread for the prescribed number of micro-seconds.

tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1) Run output_dict tensors n times, with the same feed_dict each run. Args: output_dict: A dict mapping string names to tensors to run. Must all be from the same graph. feed_dict: dict of input values to feed each run. restore_checkpoint_path: A string containing the path to a checkpoint to restore. n: Number of times to repeat. Returns: A list of n dict objects, each containing values read from output_di