tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a normal distribution. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. mean: A 0-D Tensor or Python value of type dtype. The mean of the normal distribution. stddev: A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution. dtype: The type of the output. seed: A Python integer. Used to create a random seed

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.MultivariateNormalDiag.name Name prepended to all ops created by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.MeanValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

tf.image.random_saturation(image, lower, upper, seed=None) Adjust the saturation of an RGB image by a random factor. Equivalent to adjust_saturation() but uses a saturation_factor randomly picked in the interval [lower, upper]. Args: image: RGB image or images. Size of the last dimension must be 3. lower: float. Lower bound for the random saturation factor. upper: float. Upper bound for the random saturation factor. seed: An operation-specific seed. It will be used in conjunction with the

tf.contrib.graph_editor.SubGraphView.is_passthrough(t) Check whether a tensor is passthrough.

tf.contrib.util.ops_used_by_graph_def(graph_def) Collect the list of ops used by a graph. Does not validate that the ops are all registered. Args: graph_def: A GraphDef proto, as from graph.as_graph_def(). Returns: A list of strings, each naming an op used by the graph.

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.entropy(name='entropy')

tf.contrib.distributions.Chi2WithAbsDf.__init__(df, validate_args=False, allow_nan_stats=True, name='Chi2WithAbsDf')

tf.contrib.learn.monitors.NanLoss.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.