tf.contrib.distributions.ExponentialWithSoftplusLam.std(name='std') Standard deviation.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.dtype

tf.errors.ResourceExhaustedError.__init__(node_def, op, message) Creates a ResourceExhaustedError.

tf.log(x, name=None) Computes natural logarithm of x element-wise. I.e., \(y = \log_e x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.squared_difference(x, y, name=None) Returns (x - y)(x - y) element-wise. NOTE: SquaredDifference supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. 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.contrib.learn.TensorFlowRNNClassifier.restore(cls, path, config=None) Restores model from give path. Args: path: Path to the checkpoints and other model information. config: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured. Returns: Estimator, object of the subclass of TensorFlowEstimator. Raises: ValueError: if path does not contain a model definition.

tf.contrib.distributions.BernoulliWithSigmoidP.logits

tf.nn.rnn_cell.MultiRNNCell.output_size

tf.contrib.learn.DNNRegressor.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

tf.contrib.distributions.MultivariateNormalDiag.std(name='std') Standard deviation.