diff options
Diffstat (limited to 'tensorflow/python/estimator/estimator.py')
| -rw-r--r-- | tensorflow/python/estimator/estimator.py | 34 |
1 files changed, 17 insertions, 17 deletions
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 9fcbd4ff77..6a4132bca2 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -139,8 +139,8 @@ class Estimator(object): to configure Estimators from hyper parameter tuning. * `config`: Optional configuration object. Will receive what is passed to Estimator in `config` parameter, or the default `config`. - Allows updating things in your model_fn based on configuration - such as `num_ps_replicas`, or `model_dir`. + Allows updating things in your `model_fn` based on + configuration such as `num_ps_replicas`, or `model_dir`. * Returns: `EstimatorSpec` @@ -301,11 +301,11 @@ class Estimator(object): * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a tuple (features, labels) with same constraints as below. - * A tuple (features, labels): Where features is a `Tensor` or a - dictionary of string feature name to `Tensor` and labels is a + * A tuple (features, labels): Where `features` is a `Tensor` or a + dictionary of string feature name to `Tensor` and `labels` is a `Tensor` or a dictionary of string label name to `Tensor`. Both - features and labels are consumed by `model_fn`. They should satisfy - the expectation of `model_fn` from inputs. + `features` and `labels` are consumed by `model_fn`. They should + satisfy the expectation of `model_fn` from inputs. hooks: List of `SessionRunHook` subclass instances. Used for callbacks inside the training loop. @@ -381,11 +381,11 @@ class Estimator(object): * A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a tuple (features, labels) with same constraints as below. - * A tuple (features, labels): Where features is a `Tensor` or a - dictionary of string feature name to `Tensor` and labels is a + * A tuple (features, labels): Where `features` is a `Tensor` or a + dictionary of string feature name to `Tensor` and `labels` is a `Tensor` or a dictionary of string label name to `Tensor`. Both - features and labels are consumed by `model_fn`. They should satisfy - the expectation of `model_fn` from inputs. + `features` and `labels` are consumed by `model_fn`. They should + satisfy the expectation of `model_fn` from inputs. steps: Number of steps for which to evaluate model. If `None`, evaluates until `input_fn` raises an end-of-input exception. @@ -457,17 +457,17 @@ class Estimator(object): checkpoint_path: Path of a specific checkpoint to predict. If `None`, the latest checkpoint in `model_dir` is used. yield_single_examples: If False, yield the whole batch as returned by the - model_fn instead of decomposing the batch into individual elements. This - is useful if model_fn return some tensor with first dimension not - equal to the batch size + `model_fn` instead of decomposing the batch into individual elements. + This is useful if `model_fn` returns some tensors whose first dimension + is not equal to the batch size. Yields: Evaluated values of `predictions` tensors. Raises: - ValueError: Could not find a trained model in model_dir. - ValueError: if batch length of predictions are not same and - yield_single_examples is True. + ValueError: Could not find a trained model in `model_dir`. + ValueError: If batch length of predictions is not the same and + `yield_single_examples` is True. ValueError: If there is a conflict between `predict_keys` and `predictions`. For example if `predict_keys` is not `None` but `EstimatorSpec.predictions` is not a `dict`. @@ -849,7 +849,7 @@ class Estimator(object): 'loss': estimator_spec.loss, 'step': global_step_tensor }, - every_n_iter=100) + every_n_iter=self._config.log_step_count_steps) ]) worker_hooks.extend(estimator_spec.training_hooks) |