tf.contrib.distributions.LaplaceWithSoftplusScale.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.graph_editor.SubGraphView.__enter__() Allow Python context to minize the life time of a subgraph view. A subgraph view is meant to be a lightweight and transient object. A short lifetime will alleviate the "out-of-sync" issue mentioned earlier. For that reason, a SubGraphView instance can be used within a Python context. For example: from tensorflow.contrib import graph_editor as ge with ge.make_sgv(...) as sgv: print(sgv) Returns: Itself.

tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

int tensorflow::Tensor::dims() const Convenience accessor for the tensor shape. For all shape accessors, see comments for relevant methods of TensorShape in tensor_shape.h.

tf.contrib.learn.monitors.BaseMonitor.step_begin(step) Callback before training step begins. You may use this callback to request evaluation of additional tensors in the graph. Args: step: int, the current value of the global step. Returns: List of Tensor objects or string tensor names to be run. Raises: ValueError: if we've already begun a step, or step < 0, or step > max_steps.

tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Poisson.entropy(name='entropy') Shanon entropy in nats.

tf.FixedLenFeature.shape Alias for field number 0

tf.contrib.distributions.BernoulliWithSigmoidP.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.