tf.contrib.learn.monitors.LoggingTrainable.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this

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

tf.SparseTensor.indices The indices of non-zero values in the represented dense tensor. Returns: A 2-D Tensor of int64 with shape [N, ndims], where N is the number of non-zero values in the tensor, and ndims is the rank.

string tensorflow::TensorShapeUtils::ShapeListString(const gtl::ArraySlice< TensorShape > &shapes)

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

class tf.QueueBase Base class for queue implementations. A queue is a TensorFlow data structure that stores tensors across multiple steps, and exposes operations that enqueue and dequeue tensors. Each queue element is a tuple of one or more tensors, where each tuple component has a static dtype, and may have a static shape. The queue implementations support versions of enqueue and dequeue that handle single elements, versions that support enqueuing and dequeuing a batch of elements at once. Se

tf.contrib.distributions.BetaWithSoftplusAB.a Shape parameter.

tf.assert_greater_equal(x, y, data=None, summarize=None, message=None, name=None) Assert the condition x >= y holds element-wise. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_greater_equal(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_greater_equal(x, y)], x) This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] >= y[

tf.contrib.learn.monitors.PrintTensor.end(session=None)

int64 tensorflow::TensorShape::dim_size(int d) const Returns the number of elements in dimension d. REQUIRES: 0 <= d < dims()