tf.contrib.learn.monitors.EveryN.end(session=None)

tf.contrib.learn.LinearRegressor.weights_

tf.contrib.distributions.MultivariateNormalDiag.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.nn.rnn_cell.LSTMStateTuple.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.

tf.digamma(x, name=None) Computes Psi, the derivative of Lgamma (the log of the absolute value of Gamma(x)), element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.fft3d(input, name=None) Compute the 3-dimensional discrete Fourier Transform over the inner-most 3 dimensions of input. Args: input: A Tensor of type complex64. A complex64 tensor. name: A name for the operation (optional). Returns: A Tensor of type complex64. A complex64 tensor of the same shape as input. The inner-most 3 dimensions of input are replaced with their 3D Fourier Transform.

Status tensorflow::EnvWrapper::GetSymbolFromLibrary(void *handle, const char *symbol_name, void **symbol) override

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.graph

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

tf.contrib.learn.Estimator.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.