tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor MultivariateNormalDiagWithSoftplusStDevTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagWithSoftplusStDev.

tf.contrib.framework.reduce_sum_n()

tf.contrib.framework.reduce_sum_n(tensors, name=None) Reduce tensors to a scalar sum. This reduces each tensor in tensors to a scalar via tf.reduce_sum, then adds them via tf.add_n. Args: tensors: List of tensors, all of the same numeric type. name: Tensor name, and scope for all other ops. Returns: Total loss tensor, or None if no losses have been configured. Raises: ValueError: if losses is missing or empty.

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value_type

tf.contrib.distributions.BetaWithSoftplusAB.std()

tf.contrib.distributions.BetaWithSoftplusAB.std(name='std') Standard deviation.

tf.contrib.learn.DNNClassifier

class tf.contrib.learn.DNNClassifier A classifier for TensorFlow DNN models. 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_emb = embedding_column(sparse_id_column=education, dimension=16, combiner="sum") occupation_emb =

tensorflow::TensorShape::InsertDim()

void tensorflow::TensorShape::InsertDim(int d, int64 size) Insert a dimension somewhere in the TensorShape. REQUIRES: 0 <= d <= dims() REQUIRES: size >= 0

tf.contrib.rnn.TimeFreqLSTMCell.output_size

tf.contrib.rnn.TimeFreqLSTMCell.output_size

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.loss()

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.Binomial.p

tf.contrib.distributions.Binomial.p Probability of success.