Add omit_names support to get_weights

 - `_id='*'` now fetches all layers (but input) (`get_weights`, `get_gradients`, `get_outputs`, `weights_norm`)
 - Add `omit_names` support to `get_weights`

see_rnn/inspect_gen.py

@@ -18,6 +18,8 @@ def get_outputs(model, _id, input_data, layer=None, learning_phase=0,
             list of str/int -> treat each str element as name, int as idx.
                       Ex: ['gru', 2] gets outputs of first layer with name
                       substring 'gru', then of layer w/ idx 2
+            '*': wildcard -> get outputs of all layers (except input) with
+                      'output' attribute. Overrides `layer`.
         input_data: np.ndarray & supported formats(1). Data w.r.t. which loss is
                to be computed for the gradient. Only for mode=='grads'.
         layer: keras.Layer/tf.keras.Layer. Layer whose outputs to return.
@@ -41,7 +43,15 @@ def _get_outs_tensors(model, names, idxs, layers):
             layers = [layers]
         return [l.output for l in layers]
 
-    names, idxs, layers, one_requested = _validate_args(_id, layer)
+    if _id != '*':
+        names, idxs, layers, one_requested = _validate_args(_id, layer)
+    else:
+        # exclude input layer & non-output layers
+        names = [l.name for l in model.layers[1:]
+                 if getattr(l, 'output', None) is not None]
+        idxs, layers = None, None
+        one_requested = len(_id) == 1
+
     layer_outs = _get_outs_tensors(model, names, idxs, layers)
 
     if TF_KERAS:
@@ -69,6 +79,10 @@ def get_gradients(model, _id, input_data, labels, layer=None, mode='outputs',
             list of str/int -> treat each str element as name, int as idx.
                       Ex: ['gru', 2] gets gradients of first layer with name
                       substring 'gru', then of layer w/ idx 2
+            '*': wildcard -> get outputs of all layers (except input) with:
+                      - 'output'  attribute (mode == 'outputs')
+                      - 'weights' attribute (mode == 'weights')
+                      Overrides `layer`.
         input_data: np.ndarray & supported formats(1). Data w.r.t. which loss is
                to be computed for the gradient.
         labels: np.ndarray & supported formats. Labels w.r.t. which loss is
@@ -100,8 +114,17 @@ def _validate_args_(_id, layer, mode):
             raise Exception("`mode` must be one of: 'outputs', 'weights'")
         return _validate_args(_id, layer)
 
-    names, idxs, layers, one_requested = _validate_args_(_id, layer, mode)
-    _id = [x for var in (names, idxs) if var for x in var] or None
+    if _id != '*':
+        names, idxs, layers, one_requested = _validate_args_(_id, layer, mode)
+        _id = [x for var in (names, idxs) if var for x in var] or None
+    else:
+        # exclude input layer & non-output/weightless layers (`mode`-dependent)
+        attr = 'output' if mode == 'outputs' else 'weights'
+        _id = [l.name for l in model.layers[1:]
+               if getattr(l, attr, None) is not None]
+        names = _id
+        idxs, layers = None, None
+        one_requested = len(_id) == 1
 
     if layers is None:
         layers = get_layer(model, _id)
@@ -230,6 +253,7 @@ def get_weights(model, _id, omit_names=None, as_dict=False):
                        weights of first layer with name substring 'gru', then all
                        weights of layer w/ idx 2, then weights w/ idxs 1 and 2 of
                        layer w/ idx 3.
+            '*': wildcard -> get weights of all layers with 'weights' attribute.
         omit_names: str/str list. List of names (can be substring) of weights
                                   to omit from fetching.
         as_dict: bool. True:  return weight fullname-value pairs in a dict
@@ -285,13 +309,17 @@ def _get_by_name(model, name):
                 del _weights[w_name]
         return _weights
 
-    weights = {}
-    names, idxs, *_ = _validate_args(_id)
-    _ids = [x for var in (names, idxs) if var for x in var] or None
-
+    if _id != '*':
+        names, idxs, *_ = _validate_args(_id)
+        _ids = [x for var in (names, idxs) if var for x in var] or None
+    else:
+        # exclude input layer & non-weight layers
+        _ids = [l.name for l in model.layers[1:]
+                if getattr(l, 'weights', None) not in (None, [])]
     if not isinstance(omit_names, list):
         omit_names = [omit_names] if omit_names else []
 
+    weights = {}
     for _id in _ids:
         weights.update(_get_weights_tensors(model, _id))
 
@@ -411,10 +439,9 @@ def _append(stats_all, l2_stats, w_idx, l_name):
             for stat_idx, stat in enumerate(l2_stats):
                 stats_all[l_name][w_idx][stat_idx].append(stat)
 
-        weights = get_weights(model, _id, omit_names, as_dict=True)
-        w_names, W = zip(*weights.items())
+        weights = get_weights(model, _id, omit_names)
 
-        for w_idx, (w, w_name) in enumerate(zip(W, w_names)):
+        for w_idx, w in enumerate(weights):
             l2 = _compute_norm(w, norm_fn, axis)
             l2_stats = [fn(l2) for fn in stat_fns]
             _append(stats_all, l2_stats, w_idx, l_name)

tests/test_all.py

@@ -336,6 +336,10 @@ def test_misc():  # test miscellaneous functionalities
     get_weights(model, ['gru', 1, (1, 1)])
     pass_on_error(get_weights, model, 'gru/goo')
 
+    get_weights(model, '*')
+    get_gradients(model, '*', x, y)
+    get_outputs(model, '*', x)
+
     from see_rnn.utils import _filter_duplicates_by_keys
     keys, data = _filter_duplicates_by_keys(list('abbc'), [1, 2, 3, 4])
     assert keys == ['a', 'b', 'c']