tf.contrib.bayesflow.stochastic_tensor.MeanValue.stop_gradient

tf.contrib.rnn.LayerNormBasicLSTMCell.__call__(inputs, state, scope=None) LSTM cell with layer normalization and recurrent dropout.

tf.contrib.distributions.Chi2.dtype The DType of Tensors handled by this Distribution.

uint8 tensorflow::TensorShape::buf[16][16]

tensorflow::RandomAccessFile::RandomAccessFile()

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.alpha Shape parameter.

TTypes< T, NDIMS >::Tensor tensorflow::Tensor::flat_inner_dims() Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing all Tensor dimensions but the last NDIMS-1 into the first dimension of the result. If NDIMS > dims() then leading dimensions of size 1 will be added to make the output rank NDIMS.

tf.contrib.distributions.TransformedDistribution.validate_args Python boolean indicated possibly expensive checks are enabled.

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

tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None, feature_engineering_fn=None) Constructs an Estimator instance. Args: model_fn: Model function, takes features and targets tensors or dicts of tensors and returns predictions and loss tensors. Supports next three signatures for the function: (features, targets) -> (predictions, loss, train_op) (features, targets, mode) -> (predictions, loss, train_op) (features, targets, mode, params) -> (pred