本文整理汇总了Python中mxnet.ndarray.argmax方法的典型用法代码示例。如果您正苦于以下问题:Python ndarray.argmax方法的具体用法?Python ndarray.argmax怎么用?Python ndarray.argmax使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mxnet.ndarray
的用法示例。
在下文中一共展示了ndarray.argmax方法的12个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: update
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def update(self, labels, preds):
"""Updates the internal evaluation result.
Parameters
----------
labels : list of `NDArray`
The labels of the data with class indices as values, one per sample.
preds : list of `NDArray`
Prediction values for samples. Each prediction value can either be the class index,
or a vector of likelihoods for all classes.
"""
labels, preds = check_label_shapes(labels, preds, True)
for label, pred_label in zip(labels, preds):
if pred_label.shape != label.shape:
pred_label = ndarray.argmax(pred_label, axis=self.axis)
pred_label = pred_label.asnumpy().astype('int32')
label = label.asnumpy().astype('int32')
labels, preds = check_label_shapes(label, pred_label)
valid = (labels.reshape(-1, 1) != self.ignore_labels).all(axis=-1)
self.sum_metric += np.logical_and(pred_label.flat == label.flat, valid).sum()
self.num_inst += np.sum(valid)
示例2: test_model_save_load
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def test_model_save_load(gluon_model, model_data, model_path):
_, _, test_data = model_data
expected = nd.argmax(gluon_model(test_data), axis=1)
mlflow.gluon.save_model(gluon_model, model_path)
# Loading Gluon model
model_loaded = mlflow.gluon.load_model(model_path, ctx.cpu())
actual = nd.argmax(model_loaded(test_data), axis=1)
assert all(expected == actual)
# Loading pyfunc model
pyfunc_loaded = mlflow.pyfunc.load_model(model_path)
test_pyfunc_data = pd.DataFrame(test_data.asnumpy())
pyfunc_preds = pyfunc_loaded.predict(test_pyfunc_data)
assert all(
np.argmax(pyfunc_preds.values, axis=1)
== expected.asnumpy())
示例3: test_model_log_load
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def test_model_log_load(gluon_model, model_data, model_path):
_, _, test_data = model_data
expected = nd.argmax(gluon_model(test_data), axis=1)
artifact_path = "model"
with mlflow.start_run():
mlflow.gluon.log_model(gluon_model, artifact_path=artifact_path)
model_uri = "runs:/{run_id}/{artifact_path}".format(
run_id=mlflow.active_run().info.run_id, artifact_path=artifact_path)
# Loading Gluon model
model_loaded = mlflow.gluon.load_model(model_uri, ctx.cpu())
actual = nd.argmax(model_loaded(test_data), axis=1)
assert all(expected == actual)
# Loading pyfunc model
pyfunc_loaded = mlflow.pyfunc.load_model(model_uri)
test_pyfunc_data = pd.DataFrame(test_data.asnumpy())
pyfunc_preds = pyfunc_loaded.predict(test_pyfunc_data)
assert all(
np.argmax(pyfunc_preds.values, axis=1)
== expected.asnumpy())
示例4: test_gluon_model_serving_and_scoring_as_pyfunc
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def test_gluon_model_serving_and_scoring_as_pyfunc(gluon_model, model_data):
_, _, test_data = model_data
expected = nd.argmax(gluon_model(test_data), axis=1)
artifact_path = "model"
with mlflow.start_run():
mlflow.gluon.log_model(gluon_model, artifact_path=artifact_path)
model_uri = "runs:/{run_id}/{artifact_path}".format(
run_id=mlflow.active_run().info.run_id, artifact_path=artifact_path)
scoring_response = pyfunc_serve_and_score_model(
model_uri=model_uri,
data=pd.DataFrame(test_data.asnumpy()),
content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON_SPLIT_ORIENTED)
response_values = \
pd.read_json(scoring_response.content, orient="records").values.astype(np.float32)
assert all(
np.argmax(response_values, axis=1)
== expected.asnumpy())
示例5: batch_intersection_union
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def batch_intersection_union(output, target, nclass):
"""mIoU"""
# inputs are NDarray, output 4D, target 3D
predict = F.argmax(output, 1)
target = target.astype(predict.dtype)
mini = 1
maxi = nclass
nbins = nclass
predict = predict.asnumpy() + 1
target = target.asnumpy() + 1
predict = predict * (target > 0).astype(predict.dtype)
#intersection = predict * (F.equal(predict, target)).astype(predict.dtype)
intersection = predict * (predict == target)
# areas of intersection and union
area_inter, _ = np.histogram(intersection, bins=nbins, range=(mini, maxi))
area_pred, _ = np.histogram(predict, bins=nbins, range=(mini, maxi))
area_lab, _ = np.histogram(target, bins=nbins, range=(mini, maxi))
area_union = area_pred + area_lab - area_inter
return area_inter, area_union
示例6: argmax
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def argmax(vec):
# return the argmax as a python int
idx = nd.argmax(vec, axis=1)
return to_scalar(idx)
示例7: _viterbi_decode
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def _viterbi_decode(self, feats):
backpointers = []
# Initialize the viterbi variables in log space
vvars = nd.full((1, self.tagset_size), -10000.)
vvars[0, self.tag2idx[START_TAG]] = 0
for feat in feats:
bptrs_t = [] # holds the backpointers for this step
viterbivars_t = [] # holds the viterbi variables for this step
for next_tag in range(self.tagset_size):
# next_tag_var[i] holds the viterbi variable for tag i at the
# previous step, plus the score of transitioning
# from tag i to next_tag.
# We don't include the emission scores here because the max
# does not depend on them (we add them in below)
next_tag_var = vvars + self.transitions.data()[next_tag]
best_tag_id = argmax(next_tag_var)
bptrs_t.append(best_tag_id)
viterbivars_t.append(next_tag_var[0, best_tag_id])
# Now add in the emission scores, and assign vvars to the set
# of viterbi variables we just computed
vvars = (nd.concat(*viterbivars_t, dim=0) + feat).reshape((1, -1))
backpointers.append(bptrs_t)
# Transition to STOP_TAG
terminal_var = vvars + self.transitions.data()[self.tag2idx[STOP_TAG]]
best_tag_id = argmax(terminal_var)
path_score = terminal_var[0, best_tag_id]
# Follow the back pointers to decode the best path.
best_path = [best_tag_id]
for bptrs_t in reversed(backpointers):
best_tag_id = bptrs_t[best_tag_id]
best_path.append(best_tag_id)
# Pop off the start tag (we dont want to return that to the caller)
start = best_path.pop()
assert start == self.tag2idx[START_TAG] # Sanity check
best_path.reverse()
return path_score, best_path
示例8: _evaluate_accuracy
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def _evaluate_accuracy(self, data_iterator, net, layer_params):
numerator = 0.
denominator = 0.
for i, (data, label) in enumerate(data_iterator):
data = data.as_in_context(self._context_bnn).reshape((-1, data.shape[1]))
label = label.as_in_context(self._context_bnn)
replace_params_net(layer_params, net, self._context_bnn)
output = net(data)
predictions = nd.argmax(output, axis=1)
numerator += nd.sum(predictions == label)
denominator += data.shape[0]
return (numerator / denominator).asscalar()
示例9: batch_pix_accuracy
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def batch_pix_accuracy(output, target):
"""PixAcc"""
# inputs are NDarray, output 4D, target 3D
predict = F.argmax(output, 1)
predict = predict.asnumpy() + 1
target = target.asnumpy().astype(predict.dtype) + 1
pixel_labeled = np.sum(target > 0)
pixel_correct = np.sum((predict == target)*(target > 0))
assert pixel_correct <= pixel_labeled, "Correct area should be smaller than Labeled"
return pixel_correct, pixel_labeled
示例10: argmax
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def argmax(vec):
idx = nd.argmax(vec, axis=1)
return to_scalar(idx)
示例11: _viterbi_decode
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def _viterbi_decode(self, feats):
backpointers = []
vvars = nd.full((1, self.tagset_size), -10000.,ctx=self.ctx)
vvars[0, self.tag2idx[self.START_TAG]] = 0
for feat in feats:
bptrs_t = []
viterbivars_t = []
for next_tag in range(self.tagset_size):
next_tag_var = vvars + self.transitions[next_tag]
best_tag_id = argmax(next_tag_var)
bptrs_t.append(best_tag_id)
viterbivars_t.append(next_tag_var[0, best_tag_id])
vvars = (nd.concat(*viterbivars_t, dim=0) + feat).reshape((1, -1))
backpointers.append(bptrs_t)
terminal_var = vvars + self.transitions[self.tag2idx[self.STOP_TAG]]
best_tag_id = argmax(terminal_var)
path_score = terminal_var[0, best_tag_id]
best_path = [best_tag_id]
for bptrs_t in reversed(backpointers):
best_tag_id = bptrs_t[best_tag_id]
best_path.append(best_tag_id)
start = best_path.pop()
assert start == self.tag2idx[self.START_TAG]
best_path.reverse()
return path_score, best_path
示例12: _viterbi_decode
# 需要导入模块: from mxnet import ndarray [as 别名]
# 或者: from mxnet.ndarray import argmax [as 别名]
def _viterbi_decode(self, feats):
backpointers = []
# Initialize the viterbi variables in log space
vvars = nd.full((1, self.tagset_size), -10000.)
vvars[0, self.tag2idx[START_TAG]] = 0
for feat in feats:
bptrs_t = [] # holds the backpointers for this step
viterbivars_t = [] # holds the viterbi variables for this step
for next_tag in range(self.tagset_size):
# next_tag_var[i] holds the viterbi variable for tag i at the
# previous step, plus the score of transitioning
# from tag i to next_tag.
# We don't include the emission scores here because the max
# does not depend on them (we add them in below)
next_tag_var = vvars + self.transitions[next_tag]
best_tag_id = argmax(next_tag_var)
bptrs_t.append(best_tag_id)
viterbivars_t.append(next_tag_var[0, best_tag_id])
# Now add in the emission scores, and assign vvars to the set
# of viterbi variables we just computed
vvars = (nd.concat(*viterbivars_t, dim=0) + feat).reshape((1, -1))
backpointers.append(bptrs_t)
# Transition to STOP_TAG
terminal_var = vvars + self.transitions[self.tag2idx[STOP_TAG]]
best_tag_id = argmax(terminal_var)
path_score = terminal_var[0, best_tag_id]
# Follow the back pointers to decode the best path.
best_path = [best_tag_id]
for bptrs_t in reversed(backpointers):
best_tag_id = bptrs_t[best_tag_id]
best_path.append(best_tag_id)
# Pop off the start tag (we dont want to return that to the caller)
start = best_path.pop()
assert start == self.tag2idx[START_TAG] # Sanity check
best_path.reverse()
return path_score, best_path