tf.contrib.distributions.BernoulliWithSigmoidP.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.MultivariateNormalCholesky.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.bayesflow.stochastic_tensor.MeanValue.__init__(stop_gradient=False)

tf.contrib.rnn.LayerNormBasicLSTMCell.output_size

tf.contrib.learn.TensorFlowRNNRegressor.model_dir

tf.contrib.rnn.AttentionCellWrapper.__call__(inputs, state, scope=None) Long short-term memory cell with attention (LSTMA).

tf.contrib.distributions.Mixture.cat

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__(mu, diag_large, v, diag_small=None, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagPlusVDVT') Multivariate Normal distributions on R^k. For every batch member, this distribution represents k random variables (X_1,...,X_k), with mean E[X_i] = mu[i], and covariance matrix C_{ij} := E[(X_i - mu[i])(X_j - mu[j])] The user initializes this class by providing the mean mu, and a lightweight definition of C: C = S

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

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.dtype