tf.contrib.layers.max_pool2d(*args, **kwargs) Adds a 2D Max Pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A Tensor of size [batch_size, height, width, channels]. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that pr

tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample') 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.

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.name

tf.contrib.graph_editor.SubGraphView.find_op_by_name(op_name) Return the op named op_name. Args: op_name: the name to search for Returns: The op named op_name. Raises: ValueError: if the op_name could not be found. AssertionError: if the name was found multiple time.

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

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

tf.contrib.distributions.Multinomial.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.VarLenFeature.__repr__() Return a nicely formatted representation string

tf.contrib.distributions.Chi2.entropy(name='entropy') Shanon entropy in nats. Additional documentation from Gamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

tf.contrib.distributions.WishartFull.parameters Dictionary of parameters used by this Distribution.