tf.contrib.distributions.Uniform.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.learn.BaseEstimator.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None) 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

tf.contrib.learn.LinearClassifier.weights_

tf.contrib.distributions.LaplaceWithSoftplusScale.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.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 tens

tf.cross(a, b, name=None) Compute the pairwise cross product. a and b must be the same shape; they can either be simple 3-element vectors, or any shape where the innermost dimension is 3. In the latter case, each pair of corresponding 3-element vectors is cross-multiplied independently. Args: a: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. A tensor containing 3-element vectors. b: A Tensor. Must have the same type as a. Anoth

tf.IdentityReader.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.graph_editor.keep_t_if_possible_handler(info, t) Transform a tensor into itself (identity) if possible. This handler transform a tensor into itself if the source and destination graph are the same. Otherwise it will create a placeholder. This handler is typically used to transform a hidden input tensors. Args: info: Transform._Info instance. t: tensor whose input must be transformed into a place holder. Returns: The tensor generated by the newly created place holder.

tf.contrib.distributions.Normal.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.contrib.distributions.BernoulliWithSigmoidP.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.