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

tf.asin(x, name=None) Computes asin of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.Exponential.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.StudentTWithAbsDfSoftplusSigma.mu Locations of these Student's t distribution(s).

tf.contrib.graph_editor.OpMatcher.__init__(positive_filter) Graph match constructor.

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.n

virtual uint64 tensorflow::Env::NowSeconds() Returns the number of seconds since some fixed point in time. Only useful for computing deltas of time.

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

bool tensorflow::Env::FileExists(const string &fname) Returns true iff the named file exists.

class tf.contrib.rnn.GRUBlockCell Block GRU cell implementation. The implementation is based on: http://arxiv.org/abs/1406.1078 Computes the LSTM cell forward propagation for 1 time step. This kernel op implements the following mathematical equations: Baises are initialized with : b_ru - constant_initializer(1.0) b_c - constant_initializer(0.0) ``` x_h_prev = [x, h_prev] [r_bar u_bar] = x_h_prev * w_ru + b_ru r = sigmoid(r_bar) u = sigmoid(u_bar) h_prevr = h_prev \circ r x_h_prevr = [x h_prevr