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Merge remote-tracking branch 'upstream/develop' into fix/debug_model 

# Conflicts:
#	spacy/cli/debug_model.py
This commit is contained in:
svlandeg 2020-09-21 16:58:40 +02:00
parent eb9b447960
commit 447b3e5787
1 changed files with 27 additions and 21 deletions
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48
spacy/cli/debug_model.py
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@ -1,4 +1,4 @@
from typing import Dict, Any, Optional from typing import Dict, Any, Optional, Iterable
from pathlib import Path from pathlib import Path
from wasabi import msg from wasabi import msg
from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam
@ -93,11 +93,10 @@ def debug_model(nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] =
# STEP 1: Initializing the model and printing again # STEP 1: Initializing the model and printing again
X = _get_docs() X = _get_docs()
goldY = _get_output(model.ops) _set_output_dim(nO=7, model=model)
# _set_output_dim(nO=goldY.shape[-1], model=model)
# The output vector might differ from the official type of the output layer # The output vector might differ from the official type of the output layer
with data_validation(False): with data_validation(False):
model.initialize(X=X, Y=goldY) model.initialize(X=X)
if print_settings.get("print_after_init"): if print_settings.get("print_after_init"):
msg.divider(f"STEP 1 - after initialization") msg.divider(f"STEP 1 - after initialization")
_print_model(model, print_settings) _print_model(model, print_settings)
@ -110,12 +109,15 @@ def debug_model(nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] =
if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener": if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener":
tok2vec = nlp.get_pipe("tok2vec") tok2vec = nlp.get_pipe("tok2vec")
tok2vec.model.initialize(X=X) tok2vec.model.initialize(X=X)
goldY = None
for e in range(3): for e in range(3):
if tok2vec: if tok2vec:
tok2vec.predict(X) tok2vec.predict(X)
Y, get_dX = model.begin_update(X) Y, get_dX = model.begin_update(X)
print("get_dX", get_dX) # simulate a goldY value
dY = get_gradient(goldY, Y) if not goldY:
goldY = _simulate_gold(Y)
dY = get_gradient(goldY, Y, model.ops)
get_dX(dY) get_dX(dY)
model.finish_update(optimizer) model.finish_update(optimizer)
if print_settings.get("print_after_training"): if print_settings.get("print_after_training"):
@ -128,11 +130,20 @@ def debug_model(nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] =
msg.divider(f"STEP 3 - prediction") msg.divider(f"STEP 3 - prediction")
msg.info(str(prediction)) msg.info(str(prediction))
msg.good(f"Succesfully ended analysis - model looks good!") msg.good(f"Succesfully ended analysis - model looks good.")
def get_gradient(goldY, Y): def _simulate_gold(element, counter=1):
return Y - goldY if isinstance(element, Iterable):
for i in range(len(element)):
element[i] = _simulate_gold(element[i], counter+i)
return element
else:
return 1/counter
def get_gradient(goldY, Y, ops):
return ops.asarray(Y) - ops.asarray(goldY)
def _sentences(): def _sentences():
@ -149,18 +160,13 @@ def _get_docs(lang: str = "en"):
return list(nlp.pipe(_sentences())) return list(nlp.pipe(_sentences()))
def _get_output(ops): def _set_output_dim(model, nO):
docs = len(_get_docs()) # simulating dim inference by directly setting the nO argument of the model
labels = 6 if model.has_dim("nO") is None:
output = ops.alloc2f(d0=docs, d1=labels) model.set_dim("nO", nO)
for i in range(docs): if model.has_ref("output_layer"):
for j in range(labels): if model.get_ref("output_layer").has_dim("nO") is None:
output[i, j] = 1 / (i+j+0.01) model.get_ref("output_layer").set_dim("nO", nO)
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): def _print_model(model, print_settings):