tf.contrib.distributions.Dirichlet.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.graph_editor.filter_ops(ops, positive_filter) Get the ops passing the given filter. Args: ops: an object convertible to a list of tf.Operation. positive_filter: a function deciding where to keep an operation or not. If True, all the operations are returned. Returns: A list of selected tf.Operation. Raises: TypeError: if ops cannot be converted to a list of tf.Operation.

class tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor Base Class for Tensor-like objects that emit stochastic values.

tf.contrib.graph_editor.SubGraphView.connected_inputs The connected input tensors of this subgraph view.

tf.contrib.learn.LinearClassifier.weights_

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.value_type

tf.image.random_hue(image, max_delta, seed=None) Adjust the hue of an RGB image by a random factor. Equivalent to adjust_hue() but uses a delta randomly picked in the interval [-max_delta, max_delta]. max_delta must be in the interval [0, 0.5]. Args: image: RGB image or images. Size of the last dimension must be 3. max_delta: float. Maximum value for the random delta. seed: An operation-specific seed. It will be used in conjunction with the graph-level seed to determine the real seeds that

tf.contrib.layers.convolution2d(*args, **kwargs) Adds a 2D convolution followed by an optional batch_norm layer. convolution2d creates a variable called weights, representing the convolutional kernel, that is convolved with the inputs to produce a Tensor of activations. If a normalizer_fn is provided (such as batch_norm), it is then applied. Otherwise, if normalizer_fn is None and a biases_initializer is provided then a biases variable would be created and added the activations. Finally, if ac

tf.contrib.distributions.MultivariateNormalCholesky.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.

tf.contrib.learn.monitors.ExportMonitor.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.