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()

class tf.nn.rnn_cell.BasicRNNCell The most basic RNN cell.

tf.contrib.framework.add_arg_scope(func) Decorates a function with args so it can be used within an arg_scope. Args: func: function to decorate. Returns: A tuple with the decorated function func_with_args().

tf.contrib.learn.monitors.StepCounter.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.state_size

tf.SparseTensor.dtype The DType of elements in this tensor.

tf.contrib.bayesflow.entropy.entropy_shannon(p, z=None, n=None, seed=None, form=None, name='entropy_shannon') Monte Carlo or deterministic computation of Shannon's entropy. Depending on the kwarg form, this Op returns either the analytic entropy of the distribution p, or the sampled entropy: -n^{-1} sum_{i=1}^n p.log_prob(z_i), where z_i ~ p, \approx - E_p[ Log[p(Z)] ] = Entropy[p] User supplies either Tensor of samples z, or number of samples to draw n Args: p: tf.contrib.distribut

tf.contrib.graph_editor.ControlOutputs.get(op) return the control outputs of op.

tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.