tf.contrib.learn.TensorFlowEstimator.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.learn.TensorFlowEstimator.save(path) Saves checkpoints and graph to given path. Args: path: Folder to save model to.

tf.contrib.learn.TensorFlowEstimator.restore(cls, path, config=None) Restores model from give path. Args: path: Path to the checkpoints and other model information. config: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured. Returns: Estimator, object of the subclass of TensorFlowEstimator. Raises: ValueError: if path does not contain a model definition.

tf.contrib.learn.TensorFlowEstimator.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.contrib.learn.TensorFlowEstimator.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 batc

tf.contrib.learn.TensorFlowEstimator.partial_fit(x, y) Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training. This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want to split up training into subparts. Args: x: matrix or tens

tf.contrib.learn.TensorFlowEstimator.model_dir

tf.contrib.learn.TensorFlowEstimator.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Numpy array - value of the tensor.

tf.contrib.learn.TensorFlowEstimator.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.learn.TensorFlowEstimator.get_tensor(name) Returns tensor by name. Args: name: string, name of the tensor. Returns: Tensor.