tf.contrib.distributions.StudentT.parameters Dictionary of parameters used by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.graph

tf.TextLineReader.read_up_to(queue, num_records, name=None) Returns up to num_records (key, value pairs) produced by a reader. Will dequeue a work unit from queue if necessary (e.g., when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch. Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. num_records: Number of records to read.

tf.contrib.learn.TensorFlowRNNClassifier.partial_fit(x, y) Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training. This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want to split up training into subparts. Args: x: matrix or

tf.IdentityReader.reset(name=None) Restore a reader to its initial clean state. Args: name: A name for the operation (optional). Returns: The created Operation.

tf.contrib.distributions.Multinomial.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.

bool tensorflow::PartialTensorShapeUtils::AreCompatible(const gtl::ArraySlice< PartialTensorShape > &shapes0, const gtl::ArraySlice< PartialTensorShape > &shapes1)

virtual Status tensorflow::RandomAccessFile::Read(uint64 offset, size_t n, StringPiece *result, char *scratch) const =0 Reads up to n bytes from the file starting at offset. scratch[0..n-1] may be written by this routine. Sets *result to the data that was read (including if fewer than n bytes were successfully read). May set *result to point at data in scratch[0..n-1], so scratch[0..n-1] must be live when *result is used. On OK returned status: n bytes have been stored in *result. On non-OK re

tf.contrib.metrics.streaming_accuracy(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Calculates how often predictions matches labels. The streaming_accuracy function creates two local variables, total and count that are used to compute the frequency with which predictions matches labels. This frequency is ultimately returned as accuracy: an idempotent operation that simply divides total by count. For estimation of the metric over a stream of d

tf.contrib.learn.Estimator.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altogether.