tf.contrib.training.NextQueuedSequenceBatch.sequence_count An int32 vector, length batch_size: the sequence count of each entry. When an input is split up, the number of splits is equal to: padded_length / num_unroll. This is the sequence_count. Returns: An int32 vector Tensor.

tf.contrib.learn.monitors.SummarySaver.__init__(summary_op, save_steps=100, output_dir=None, summary_writer=None, scaffold=None) Initializes a SummarySaver monitor. Args: summary_op: Tensor of type string. A serialized Summary protocol buffer, as output by TF summary methods like scalar_summary or merge_all_summaries. save_steps: int, save summaries every N steps. See EveryN. output_dir: string, the directory to save the summaries to. Only used if no summary_writer is supplied. summary_wri

class tf.contrib.learn.monitors.ValidationMonitor Runs evaluation of a given estimator, at most every N steps. Note that the evaluation is done based on the saved checkpoint, which will usually be older than the current step. Can do early stopping on validation metrics if early_stopping_rounds is provided.

tf.TextLineReader.supports_serialize Whether the Reader implementation can serialize its state.

tf.matrix_diag(diagonal, name=None) Returns a batched diagonal tensor with a given batched diagonal values. Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros. The diagonal is computed as follows: Assume diagonal has k dimensions [I, J, K, ..., N], then the output is a tensor of rank k+1 with dimensions [I, J, K, ..., N, N]` where: output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]. For example: # 'diagonal' is [[1, 2, 3, 4],

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.input_dict

tf.VarLenFeature.__getstate__() Exclude the OrderedDict from pickling

tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.encode_base64(input, pad=None, name=None) Encode strings into web-safe base64 format. Refer to the following article for more information on base64 format: en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the end so that the encoded has length multiple of 4. See Padding section of the link above. Web-safe means that the encoder uses - and _ instead of + and /. Args: input: A Tensor of type string. Strings to be encoded. pad: An optional bool. Defaults to False. Bo

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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.