class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor MultivariateNormalFullTensor is a StochasticTensor backed by the distribution MultivariateNormalFull.

tf.image.encode_png(image, compression=None, name=None) PNG-encode an image. image is a 3-D uint8 or uint16 Tensor of shape [height, width, channels] where channels is: 1: for grayscale. 2: for grayscale + alpha. 3: for RGB. 4: for RGBA. The ZLIB compression level, compression, can be -1 for the PNG-encoder default or a value from 0 to 9. 9 is the highest compression level, generating the smallest output, but is slower. Args: image: A Tensor. Must be one of the following types: uint8, uint1

tf.exp(x, name=None) Computes exponential of x element-wise. \(y = e^x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.FixedLenSequenceFeature.shape Alias for field number 0

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.value_type

tf.nn.rnn_cell.EmbeddingWrapper.__init__(cell, embedding_classes, embedding_size, initializer=None) Create a cell with an added input embedding. Args: cell: an RNNCell, an embedding will be put before its inputs. embedding_classes: integer, how many symbols will be embedded. embedding_size: integer, the size of the vectors we embed into. initializer: an initializer to use when creating the embedding; if None, the initializer from variable scope or a default one is used. Raises: TypeErro

tf.contrib.learn.monitors.EveryN.__init__(every_n_steps=100, first_n_steps=1) Initializes an EveryN monitor. Args: every_n_steps: int, the number of steps to allow between callbacks. first_n_steps: int, specifying the number of initial steps during which the callbacks will always be executed, regardless of the value of every_n_steps. Note that this value is relative to the global step

bool tensorflow::Status::ok() const Returns true iff the status indicates success.

tf.contrib.learn.monitors.LoggingTrainable.every_n_step_end(step, outputs)