tf.contrib.learn.LinearClassifier.model_dir

class tf.contrib.distributions.Mixture Mixture distribution. The Mixture object implements batched mixture distributions. The mixture model is defined by a Categorical distribution (the mixture) and a python list of Distribution objects. Methods supported include log_prob, prob, mean, sample, and entropy_lower_bound.

tf.contrib.graph_editor.get_generating_ops(ts) Return all the generating ops of the tensors in ts. Args: ts: a list of tf.Tensor Returns: A list of all the generating tf.Operation of the tensors in ts. Raises: TypeError: if ts cannot be converted to a list of tf.Tensor.

tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes log(sum(exp(elements across dimensions of a tensor))). Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is

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.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.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.