class tf.nn.rnn_cell.RNNCell Abstract object representing an RNN cell. The definition of cell in this package differs from the definition used in the literature. In the literature, cell refers to an object with a single scalar output. The definition in this package refers to a horizontal array of such units. An RNN cell, in the most abstract setting, is anything that has a state and performs some operation that takes a matrix of inputs. This operation results in an output matrix with self.outp

class tf.contrib.distributions.Exponential The Exponential distribution with rate parameter lam. The PDF of this distribution is: prob(x) = (lam * e^(-lam * x)), x > 0 Note that the Exponential distribution is a special case of the Gamma distribution, with Exponential(lam) = Gamma(1, lam).

tf.contrib.distributions.WishartCholesky.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

TTypes<T>::Flat tensorflow::Tensor::flat() Return the tensor data as an Eigen::Tensor of the data type and a specified shape. These methods allow you to access the data with the dimensions and sizes of your choice. You do not need to know the number of dimensions of the Tensor to call them. However, they CHECK that the type matches and the dimensions requested creates an Eigen::Tensor with the same number of elements as the tensor. Example: Tensor my_ten(...built with Shape{planes: 4, ro

tensorflow::Tensor::Tensor() Creates a 1-dimensional, 0-element float tensor. The returned Tensor is not a scalar (shape {}), but is instead an empty one-dimensional Tensor (shape {0}, NumElements() == 0). Since it has no elements, it does not need to be assigned a value and is initialized by default ( IsInitialized() is true). If this is undesirable, consider creating a one-element scalar which does require initialization:

tf.contrib.learn.monitors.StepCounter.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tensorflow::WritableFile::WritableFile()

tf.complex(real, imag, name=None) Converts two real numbers to a complex number. Given a tensor real representing the real part of a complex number, and a tensor imag representing the imaginary part of a complex number, this operation returns complex numbers elementwise of the form (a + bj), where a represents the real part and b represents the imag part. The input tensors real and imag must have the same shape. For example: # tensor 'real' is [2.25, 3.25] # tensor `imag` is [4.75, 5.75] tf.co

virtual tensorflow::Session::~Session()

tf.sparse_placeholder(dtype, shape=None, name=None) Inserts a placeholder for a sparse tensor that will be always fed. Important: This sparse tensor will produce an error if evaluated. Its value must be fed using the feed_dict optional argument to Session.run(), Tensor.eval(), or Operation.run(). For example: x = tf.sparse_placeholder(tf.float32) y = tf.sparse_reduce_sum(x) with tf.Session() as sess: print(sess.run(y)) # ERROR: will fail because x was not fed. indices = np.array([[3, 2,