tf.contrib.distributions.QuantizedDistribution.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

PartialTensorShape tensorflow::PartialTensorShape::Concatenate(int64 size) const Add a dimension to the end ("inner-most"), returns a new PartialTensorShape . REQUIRES: size >= -1, where -1 means unknown

tf.contrib.distributions.MultivariateNormalFull.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.Beta.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.nn.rnn_cell.RNNCell.output_size Integer or TensorShape: size of outputs produced by this cell.

tf.contrib.distributions.Binomial.variance(name='variance') Variance.

tf.contrib.distributions.Normal.mean(name='mean') Mean.

tf.image.transpose_image(image) Transpose an image by swapping the first and second dimension. See also transpose(). Args: image: 3-D tensor of shape [height, width, channels] Returns: A 3-D tensor of shape [width, height, channels] Raises: ValueError: if the shape of image not supported.

tf.contrib.distributions.BernoulliWithSigmoidP.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None) Create batches by randomly shuffling tensors. The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.train.shuffle_batch(). This version enqueues a different list of tensors in different threa