trying some stuff
@@ -89,6 +89,7 @@ def debug_model(nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] =
# STEP 1: Initializing the model and printing again
X = _get_docs()
goldY = _get_output(model.ops)
+ # _set_output_dim(nO=goldY.shape[-1], model=model)
# The output vector might differ from the official type of the output layer
with data_validation(False):
model.initialize(X=X, Y=goldY)
@@ -108,6 +109,7 @@ def debug_model(nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] =
if tok2vec:
tok2vec.predict(X)
Y, get_dX = model.begin_update(X)
+ print("get_dX", get_dX)
dY = get_gradient(goldY, Y)
get_dX(dY)
model.finish_update(optimizer)
@@ -152,6 +154,10 @@ def _get_output(ops):
return ops.xp.asarray(output)
+def _get_output_old(xp):
+ return xp.asarray([i + 10 for i, _ in enumerate(_get_docs())], dtype="float32")
+
+
def _print_model(model, print_settings):
layers = print_settings.get("layers", "")
parameters = print_settings.get("parameters", False)
@@ -200,3 +206,12 @@ def _print_matrix(value):
sample_matrix = sample_matrix[0:5]
result = result + str(sample_matrix)
return result
+
+
+def _set_output_dim(model, nO):
+ # the dim inference doesn't always work 100%, we need this hack like we have it in pipe.pyx
+ if model.has_dim("nO") is None:
+ model.set_dim("nO", nO)
+ if model.has_ref("output_layer"):
+ if model.get_ref("output_layer").has_dim("nO") is None:
+ model.get_ref("output_layer").set_dim("nO", nO)
\ No newline at end of file
