Hi John,
I am having the same issue. How did you fix that? Please let me know.
Best regards,
Simron
TypeError Traceback (most recent call last)
in
----> 1 lrf=learn.lr_find()
2 learn.sched.plot()
/media/ivlab/FAST-FROG/fastai/courses/dl1/fastai/learner.py in lr_find(self, start_lr, end_lr, wds, linear, **kwargs)
343 layer_opt = self.get_layer_opt(start_lr, wds)
344 self.sched = LR_Finder(layer_opt, len(self.data.trn_dl), end_lr, linear=linear)
–> 345 self.fit_gen(self.model, self.data, layer_opt, 1, **kwargs)
346 self.load(‘tmp’)
347
/media/ivlab/FAST-FROG/fastai/courses/dl1/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)
247 metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16,
248 swa_model=self.swa_model if use_swa else None, swa_start=swa_start,
–> 249 swa_eval_freq=swa_eval_freq, **kwargs)
250
251 def get_layer_groups(self): return self.models.get_layer_groups()
/media/ivlab/FAST-FROG/fastai/courses/dl1/fastai/model.py in fit(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, visualize, **kwargs)
160
161 if not all_val:
–> 162 vals = validate(model_stepper, cur_data.val_dl, metrics, epoch, seq_first=seq_first, validate_skip = validate_skip)
163 stop=False
164 for cb in callbacks: stop = stop or cb.on_epoch_end(vals)
/media/ivlab/FAST-FROG/fastai/courses/dl1/fastai/model.py in validate(stepper, dl, metrics, epoch, seq_first, validate_skip)
239 batch_cnts.append(batch_sz(x, seq_first=seq_first))
240 loss.append(to_np(l))
–> 241 res.append([to_np(f(datafy(preds), datafy(y))) for f in metrics])
242 return [np.average(loss, 0, weights=batch_cnts)] + list(np.average(np.stack(res), 0, weights=batch_cnts))
243
/media/ivlab/FAST-FROG/fastai/courses/dl1/fastai/model.py in (.0)
239 batch_cnts.append(batch_sz(x, seq_first=seq_first))
240 loss.append(to_np(l))
–> 241 res.append([to_np(f(datafy(preds), datafy(y))) for f in metrics])
242 return [np.average(loss, 0, weights=batch_cnts)] + list(np.average(np.stack(res), 0, weights=batch_cnts))
243
/media/ivlab/FAST-FROG/fastai/courses/dl1/planet.py in f2(preds, targs, start, end, step)
9 warnings.simplefilter(“ignore”)
10 return max([fbeta_score(targs, (preds>th), 2, average=‘samples’)
—> 11 for th in np.arange(start,end,step)])
12
13 def opt_th(preds, targs, start=0.17, end=0.24, step=0.01):
/media/ivlab/FAST-FROG/fastai/courses/dl1/planet.py in (.0)
9 warnings.simplefilter(“ignore”)
10 return max([fbeta_score(targs, (preds>th), 2, average=‘samples’)
—> 11 for th in np.arange(start,end,step)])
12
13 def opt_th(preds, targs, start=0.17, end=0.24, step=0.01):
~/anaconda2/envs/fastai/lib/python3.7/site-packages/sklearn/metrics/classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight)
832 average=average,
833 warn_for=(‘f-score’,),
–> 834 sample_weight=sample_weight)
835 return f
836
~/anaconda2/envs/fastai/lib/python3.7/site-packages/sklearn/metrics/classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight)
1029 raise ValueError(“beta should be >0 in the F-beta score”)
1030
-> 1031 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
1032 check_consistent_length(y_true, y_pred, sample_weight)
1033 present_labels = unique_labels(y_true, y_pred)
~/anaconda2/envs/fastai/lib/python3.7/site-packages/sklearn/metrics/classification.py in _check_targets(y_true, y_pred)
70 “”"
71 check_consistent_length(y_true, y_pred)
—> 72 type_true = type_of_target(y_true)
73 type_pred = type_of_target(y_pred)
74
~/anaconda2/envs/fastai/lib/python3.7/site-packages/sklearn/utils/multiclass.py in type_of_target(y)
247 raise ValueError(“y cannot be class ‘SparseSeries’.”)
248
–> 249 if is_multilabel(y):
250 return ‘multilabel-indicator’
251
~/anaconda2/envs/fastai/lib/python3.7/site-packages/sklearn/utils/multiclass.py in is_multilabel(y)
138 “”"
139 if hasattr(y, ‘array’):
–> 140 y = np.asarray(y)
141 if not (hasattr(y, “shape”) and y.ndim == 2 and y.shape[1] > 1):
142 return False
~/anaconda2/envs/fastai/lib/python3.7/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
499
500 “”"
–> 501 return array(a, dtype, copy=False, order=order)
502
503
~/anaconda2/envs/fastai/lib/python3.7/site-packages/torch/tensor.py in array(self, dtype)
448 def array(self, dtype=None):
449 if dtype is None:
–> 450 return self.numpy()
451 else:
452 return self.numpy().astype(dtype, copy=False)
TypeError: can’t convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
