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

tf.python_io.TFRecordWriter.close() Close the file.

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.contrib.distributions.Gamma.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

class tf.contrib.learn.TensorFlowRNNClassifier TensorFlow RNN Classifier model.

bool tensorflow::Tensor::IsAligned() const Returns true iff this tensor is aligned.

tf.contrib.distributions.Beta.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.Gamma.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.Gamma.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.