TTypes< T, NDIMS >::Tensor tensorflow::Tensor::bit_casted_tensor() Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T. Using a bitcast is useful for move and copy operations. NOTE: this is the same as tensor() except a bitcast is allowed.

tf.contrib.learn.TensorFlowRNNClassifier.predict_proba(x, batch_size=None) Predict class probability of the input samples x. Args: x: array-like matrix, [n_samples, n_features...] or iterator. batch_size: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used. Returns: y: array of shape [n_samples, n_classes]. The predicted probabilities for each class.

tf.FixedLengthRecordReader.supports_serialize Whether the Reader implementation can serialize its state.

tf.contrib.distributions.TransformedDistribution.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: flo

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

tf.contrib.distributions.Distribution.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

class tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev MultivariateNormalDiag with diag_stddev = softplus(diag_stddev).

class tf.InteractiveSession A TensorFlow Session for use in interactive contexts, such as a shell. The only difference with a regular Session is that an InteractiveSession installs itself as the default session on construction. The methods Tensor.eval() and Operation.run() will use that session to run ops. This is convenient in interactive shells and IPython notebooks, as it avoids having to pass an explicit Session object to run ops. For example: sess = tf.InteractiveSession() a = tf.constant

tf.contrib.distributions.LaplaceWithSoftplusScale.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's

tf.sparse_fill_empty_rows(sp_input, default_value, name=None) Fills empty rows in the input 2-D SparseTensor with a default value. This op adds entries with the specified default_value at index [row, 0] for any row in the input that does not already have a value. For example, suppose sp_input has shape [5, 6] and non-empty values: [0, 1]: a [0, 3]: b [2, 0]: c [3, 1]: d Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values: [0, 1]: a [0, 3]: b [1, 0]: default_value [2, 0]: