class tf.contrib.learn.monitors.PrintTensor Prints given tensors every N steps. This is an EveryN monitor and has consistent semantic for every_n and first_n. The tensors will be printed to the log, with INFO severity.

class tf.contrib.learn.DNNRegressor A regressor 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 = e

tf.contrib.learn.run_feeds(*args, **kwargs) See run_feeds_iter(). Returns a list instead of an iterator.

tf.contrib.rnn.AttentionCellWrapper.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with t

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.clone(name=None, **dist_args)

tf.contrib.util.make_tensor_proto(values, dtype=None, shape=None) Create a TensorProto. Args: values: Values to put in the TensorProto. dtype: Optional tensor_pb2 DataType value. shape: List of integers representing the dimensions of tensor. Returns: A TensorProto. Depending on the type, it may contain data in the "tensor_content" attribute, which is not directly useful to Python programs. To access the values you should convert the proto back to a numpy ndarray with tensor_util.MakeNdarr

tf.contrib.crf.crf_binary_score(tag_indices, sequence_lengths, transition_params) Computes the binary scores of tag sequences. Args: tag_indices: A [batch_size, max_seq_len] matrix of tag indices. sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] matrix of binary potentials. Returns: binary_scores: A [batch_size] vector of binary scores.

tf.contrib.crf.crf_log_likelihood(inputs, tag_indices, sequence_lengths, transition_params=None) Computes the log-likehood of tag sequences in a CRF. Args: inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer. tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we compute the log-likehood. sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] transition matrix, if

tf.contrib.learn.monitors.EveryN.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this step, or F

tf.contrib.distributions.BetaWithSoftplusAB.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.