tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.entropy(name='entropy')

tf.nn.rnn_cell.DropoutWrapper.state_size

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

const TensorShape& tensorflow::Tensor::shape() const Returns the shape of the tensor.

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.Dirichlet.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value_type

tensorflow::Session::Session()

TTypes<T>::Vec tensorflow::Tensor::vec() Return the tensor data as an Eigen::Tensor with the type and sizes of this Tensor. Use these methods when you know the data type and the number of dimensions of the Tensor and you want an Eigen::Tensor automatically sized to the Tensor sizes. The implementation check fails if either type or sizes mismatch. Example: Tensor my_mat(...built with Shape{rows: 3, cols: 5}...); auto mat = my_mat.matrix<T>(); // 2D Eigen::Tensor, 3 x 5. auto mat

class tf.FixedLengthRecordReader A Reader that outputs fixed-length records from a file. See ReaderBase for supported methods.