tf.minimum(x, y, name=None) Returns the min of x and y (i.e. x < y ? x : y) element-wise. NOTE: Minimum supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.nn.rnn_cell.RNNCell.output_size Integer or TensorShape: size of outputs produced by this cell.

tf.WholeFileReader.__init__(name=None) Create a WholeFileReader. Args: name: A name for the operation (optional).

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.input_dict

tf.contrib.learn.DNNRegressor.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Numpy array - value of the tensor.

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

PartialTensorShape tensorflow::PartialTensorShape::Concatenate(int64 size) const Add a dimension to the end ("inner-most"), returns a new PartialTensorShape . REQUIRES: size >= -1, where -1 means unknown

tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.input_dict

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