tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.learn.monitors.ExportMonitor.exports_to_keep

tf.contrib.distributions.BernoulliWithSigmoidP.mean(name='mean') Mean.

class tf.contrib.rnn.TimeFreqLSTMCell Time-Frequency Long short-term memory unit (LSTM) recurrent network cell. This implementation is based on: Tara N. Sainath and Bo Li "Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks." submitted to INTERSPEECH, 2016. It uses peep-hole connections and optional cell clipping.

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.dtype

tf.errors.AlreadyExistsError.__init__(node_def, op, message) Creates an AlreadyExistsError.

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.name

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

tf.contrib.distributions.NormalWithSoftplusSigma.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_

tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double