tf.image.rgb_to_hsv(images, name=None) Converts one or more images from RGB to HSV. Outputs a tensor of the same shape as the images tensor, containing the HSV value of the pixels. The output is only well defined if the value in images are in [0,1]. output[..., 0] contains hue, output[..., 1] contains saturation, and output[..., 2] contains value. All HSV values are in [0,1]. A hue of 0 corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. Args: images: A Tensor. Must be one o

tf.contrib.distributions.Gamma.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.distributions.Chi2.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_shape(x) + self.bat

tf.contrib.copy_graph.copy_variable_to_graph(org_instance, to_graph, scope='') Given a Variable instance from one Graph, initializes and returns a copy of it from another Graph, under the specified scope (default ""). Args: org_instance: A Variable from some Graph. to_graph: The Graph to copy the Variable to. scope: A scope for the new Variable (default ""). Returns: The copied `Variable` from `to_graph`. Raises: TypeError: If org_instance is not a Variable.

tf.contrib.distributions.Chi2WithAbsDf.df

tf.contrib.distributions.Uniform.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.

tf.contrib.distributions.Multinomial.parameters Dictionary of parameters used by this Distribution.

tf.nn.rnn_cell.RNNCell.__call__(inputs, state, scope=None) Run this RNN cell on inputs, starting from the given state. Args: inputs: 2-D tensor with shape [batch_size x input_size]. state: if self.state_size is an integer, this should be a 2-D Tensor with shape [batch_size x self.state_size]. Otherwise, if self.state_size is a tuple of integers, this should be a tuple with shapes [batch_size x s] for s in self.state_size. scope: VariableScope for the created subgraph; defaults to class name

tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from Gamma: See the documentation for tf.random_gamma for more details. 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.

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.input_dict