我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用tensorflow.python.framework.ops.add_to_collections()。
def collect_named_outputs(collections, alias, outputs): """Add `Tensor` outputs tagged with alias to collections. It is useful to collect end-points or tags for summaries. Example of usage: logits = collect_named_outputs('end_points', 'inception_v3/logits', logits) assert logits.alias == 'inception_v3/logits' Args: collections: A collection or list of collections. If None skip collection. alias: String, alias to name the outputs, ex. 'inception_v3/conv1' outputs: Tensor, an output tensor to collect Returns: The outputs Tensor to allow inline call. """ # Remove ending '/' if present. if alias[-1] == '/': alias = alias[:-1] outputs.alias = alias if collections: ops.add_to_collections(collections, outputs) return outputs
def collect_named_outputs(collections, alias, outputs): """Add `Tensor` outputs tagged with alias to collections. It is useful to collect end-points or tags for summaries. Example of usage: logits = collect_named_outputs('end_points', 'inception_v3/logits', logits) assert 'inception_v3/logits' in logits.aliases Args: collections: A collection or list of collections. If None skip collection. alias: String to append to the list of aliases of outputs, for example, 'inception_v3/conv1'. outputs: Tensor, an output tensor to collect Returns: The outputs Tensor to allow inline call. """ if collections: append_tensor_alias(outputs, alias) ops.add_to_collections(collections, outputs) return outputs
def _apply_activation(y, activation_fn, output_collections): if activation_fn is not None: y = activation_fn(y) ops.add_to_collections(list(output_collections or []) + [ops.GraphKeys.ACTIVATIONS], y) return y
def _count_condition(values, weights=None, metrics_collections=None, updates_collections=None): """Sums the weights of cases where the given values are True. If `weights` is `None`, weights default to 1. Use weights of 0 to mask values. Args: values: A `bool` `Tensor` of arbitrary size. weights: An optional `Tensor` whose shape is broadcastable to `values`. metrics_collections: An optional list of collections that the metric value variable should be added to. updates_collections: An optional list of collections that the metric update ops should be added to. Returns: value_tensor: A tensor representing the current value of the metric. update_op: An operation that accumulates the error from a batch of data. Raises: ValueError: If `weights` is not `None` and its shape doesn't match `values`, or if either `metrics_collections` or `updates_collections` are not a list or tuple. """ check_ops.assert_type(values, dtypes.bool) count = _create_local('count', shape=[]) values = math_ops.to_float(values) if weights is not None: weights = math_ops.to_float(weights) values = math_ops.mul(values, weights) value_tensor = array_ops.identity(count) update_op = state_ops.assign_add(count, math_ops.reduce_sum(values)) if metrics_collections: ops.add_to_collections(metrics_collections, value_tensor) if updates_collections: ops.add_to_collections(updates_collections, update_op) return value_tensor, update_op
def _count_condition(values, weights=None, metrics_collections=None, updates_collections=None): """Sums the weights of cases where the given values are True. If `weights` is `None`, weights default to 1. Use weights of 0 to mask values. Args: values: A `bool` `Tensor` of arbitrary size. weights: Optional `Tensor` whose rank is either 0, or the same rank as `values`, and must be broadcastable to `values` (i.e., all dimensions must be either `1`, or the same as the corresponding `values` dimension). metrics_collections: An optional list of collections that the metric value variable should be added to. updates_collections: An optional list of collections that the metric update ops should be added to. Returns: value_tensor: A `Tensor` representing the current value of the metric. update_op: An operation that accumulates the error from a batch of data. Raises: ValueError: If `weights` is not `None` and its shape doesn't match `values`, or if either `metrics_collections` or `updates_collections` are not a list or tuple. """ check_ops.assert_type(values, dtypes.bool) count = _create_local('count', shape=[]) values = math_ops.to_float(values) if weights is not None: weights = math_ops.to_float(weights) with ops.control_dependencies((_assert_weights_rank(weights, values),)): values = math_ops.multiply(values, weights) value_tensor = array_ops.identity(count) update_op = state_ops.assign_add(count, math_ops.reduce_sum(values)) if metrics_collections: ops.add_to_collections(metrics_collections, value_tensor) if updates_collections: ops.add_to_collections(updates_collections, update_op) return value_tensor, update_op
