tf.contrib.distributions.BernoulliWithSigmoidP.logits

tf.nn.rnn_cell.MultiRNNCell.output_size

tf.contrib.learn.DNNRegressor.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.

tf.contrib.distributions.MultivariateNormalDiag.std(name='std') Standard deviation.

tf.contrib.learn.TensorFlowRNNRegressor.predict(x, axis=1, batch_size=None) Predict class or regression for x. For a classification model, the predicted class for each sample in x is returned. For a regression model, the predicted value based on x is returned. Args: x: array-like matrix, [n_samples, n_features...] or iterator. axis: Which axis to argmax for classification. By default axis 1 (next after batch) is used. Use 2 for sequence predictions. batch_size: If test set is too big, use b

tf.errors.OutOfRangeError.__init__(node_def, op, message) Creates an OutOfRangeError.

tf.contrib.learn.LinearClassifier.get_variable_value(name)

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

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

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_sigma_det(name='log_sigma_det') Log of determinant of covariance matrix.