code stringlengths 3 6.57k |
|---|
bias (optional) |
if (running_mean is None or running_var is None) |
assert (stats_id < self.num_stats) |
self.get_stats(stats_id) |
NotImplementedError() |
assert (not self._track_running_stats) |
checkpoint_stats(self, device=None) |
device (optional) |
self._stats_names(self._num_stats - 1) |
getattr(self, mname_old) |
NotImplementedError() |
self._stats_names(self._num_stats) |
get_stats(self, stats_id=None) |
statistics (means and variances) |
stats_id (optional) |
assert (stats_id < self.num_stats) |
self._stats_names(stats_id) |
getattr(self, mname) |
getattr(self, vname) |
_stats_names(self, stats_id) |
TODO(b/29580537) |
_changing(feature_columns) |
tf.contrib.learn.infer_real_valued_columns_from_input(x) |
tf.contrib.learn.infer_real_valued_columns_from_input_fn(input_fn) |
LinearClassifier(dnn_linear_combined.DNNLinearCombinedClassifier) |
estimator.fit(input_fn=input_fn_train) |
estimator.evaluate(input_fn=input_fn_eval) |
estimator.predict(x=x) |
_changing(feature_columns) |
super(LinearClassifier, self) |
TODO(b/29580537) |
_validate_linear_feature_columns(self, features) |
layers.infer_real_valued_columns(features) |
layers.infer_real_valued_columns(features) |
_get_train_ops(self, features, targets) |
self._validate_linear_feature_columns(features) |
isinstance(self._linear_optimizer, sdca_optimizer.SDCAOptimizer) |
super(LinearClassifier, self) |
_get_train_ops(features, targets) |
contrib_variables.get_global_step() |
ops.control_dependencies([self._centered_bias() |
self._target_column.loss(logits, targets, features) |
logging_ops.scalar_summary("loss", loss) |
_get_eval_ops(self, features, targets, metrics=None) |
self._validate_linear_feature_columns(features) |
super(LinearClassifier, self) |
_get_predict_ops(self, features) |
self._validate_linear_feature_columns(features) |
super(LinearClassifier, self) |
_get_predict_ops(features) |
weights_(self) |
bias_(self) |
LinearRegressor(dnn_linear_combined.DNNLinearCombinedRegressor) |
estimator.fit(input_fn=input_fn_train) |
estimator.evaluate(input_fn=input_fn_eval) |
estimator.predict(x=x) |
isinstance(column, `SparseColumn`) |
isinstance(column, `RealValuedColumn`) |
_changing(feature_columns) |
super(LinearRegressor, self) |
TODO(b/29580537) |
_validate_linear_feature_columns(self, features) |
layers.infer_real_valued_columns(features) |
layers.infer_real_valued_columns(features) |
_get_train_ops(self, features, targets) |
isinstance(self._linear_optimizer, sdca_optimizer.SDCAOptimizer) |
ValueError("SDCAOptimizer does not currently support regression.") |
self._validate_linear_feature_columns(features) |
super(LinearRegressor, self) |
_get_train_ops(features, targets) |
_get_eval_ops(self, features, targets, metrics=None) |
self._validate_linear_feature_columns(features) |
super(LinearRegressor, self) |
_get_predict_ops(self, features) |
self._validate_linear_feature_columns(features) |
super(LinearRegressor, self) |
_get_predict_ops(features) |
weights_(self) |
bias_(self) |
_supported_input_size_type(input_size: Any) |
isinstance(input_size, torch.Size) |
isinstance(input_size, tuple) |
isinstance(input_size, list) |
sizes (min, opt, max) |
str(type(input_size) |
_parse_input_ranges(input_sizes: List) |
any(not isinstance(i, dict) |
_supported_input_size_type(i) |
KeyError("An input size must either be a static size or a range of three sizes (min, opt, max) |
isinstance(i, dict) |
all(k in i for k in ["min", "opt", "min"]) |
trtorch._C.InputRange() |
parsed_input_sizes.append(in_range) |
trtorch._C.InputRange() |
parsed_input_sizes.append(in_range) |
sizes (min, opt, max) |
isinstance(i, list) |
trtorch._C.InputRange() |
parsed_input_sizes.append(in_range) |
isinstance(i, tuple) |
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