tf.contrib.learn.monitors.StopAtStep.__init__(num_steps=None, last_step=None) Create a StopAtStep monitor. This monitor requests stop after either a number of steps have been executed or a last step has been reached. Only of the two options can be specified. if num_steps is specified, it indicates the number of steps to execute after begin() is called. If instead last_step is specified, it indicates the last step we want to execute, as passed to the step_begin() call. Args: num_steps: Number

tf.foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None) foldl on the list of tensors unpacked from elems on dimension 0. This foldl operator repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems on dimension 0. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invocation of fn. If initialize

tf.contrib.metrics.aggregate_metric_map(names_to_tuples) Aggregates the metric names to tuple dictionary. This function is useful for pairing metric names with their associated value and update ops when the list of metrics is long. For example: metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map({ 'Mean Absolute Error': new_slim.metrics.streaming_mean_absolute_error( predictions, labels, weights), 'Mean Relative Error': new_slim.metrics.streaming_mean_relative_error( pred

tf.contrib.layers.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner=None, default_id=None, name=None, partition_strategy='div') Lookup embedding results, accounting for invalid IDs and empty features. The partitioned embedding in embedding_weights must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of P. Invalid IDs (< 0) are pruned from input IDs and we

tf.contrib.training.NextQueuedSequenceBatch.total_length The lengths of the original (non-truncated) unrolled examples. Returns: An integer vector of length batch_size, the total lengths.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.

tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Produces the integers from 0 to limit-1 in a queue. Args: limit: An int32 scalar tensor. num_epochs: An integer (optional). If specified, range_input_producer produces each integer num_epochs times before generating an OutOfRange error. If not specified, range_input_producer can cycle through the integers an unlimited number of times. shuffle: Boolean. If true, the intege

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

TTypes< T >::ConstScalar tensorflow::Tensor::scalar() const

tf.contrib.metrics.streaming_sensitivity_at_specificity(predictions, labels, specificity, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, name=None) Computes the the specificity at a given sensitivity. The streaming_sensitivity_at_specificity function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the sensitivity at the given specificity value. The threshold for the given specifici