timeseriesis a Timeseries Classification and Regression package for fastai2.
timeseries package documentation
There are may ways to install
timeseriespackage. Sincetimeseriesis built usingfastai2, there are also different ways to install fastai2. We will show 2 differents ways to install them and explain the motivation behin each one of them.
Important :Only if you have not already installed
fastai2,install fastai2 by following the steps described there.
Note :Installing an editable version of a package means that you will install a package from its corresponding github repository on your local machine. By doing so, you can pull the latest version whenever a new version is pushed. To install
timeserieseditable package, follow the instructions here below:
git clone https://github.com/ai-fast-track/timeseries.git
cd timeseries
pip install -e .
Note :Everytime you run the
!pip install git+https:// ..., you are installing the package latest version stored on github. > Important :As both fastai2 andtimeseriesare still under development, this is an easy way to use them in Google Colab or any other online platform. You can also use it on your local machine.
# Run this cell to install the latest version of fastai shared on github
!pip install git+https://github.com/fastai/fastai2.git
# Run this cell to install the latest version of fastcore shared on github
!pip install git+https://github.com/fastai/fastcore.git
# Run this cell to install the latest version of timeseries shared on github
!pip install git+https://github.com/ai-fast-track/timeseries.git
%reload_ext autoreload
%autoreload 2
%matplotlib inline
The history saving thread hit an unexpected error (DatabaseError('database disk image is malformed',)).History will not be written to the database.
from fastai2.basics import *
from timeseries.all import *
The data is generated by sensors on the hands, elbows, wrists and thumbs. The data are the x,y,z coordinates for each of the eight locations. The order of the data is as follows:
Right Arm vs Left Arm time series for the 'Not clear' Command ((#3) (see picture here above)
| Hand | Elbow | Hand | Elbow |
|---|---|---|---|
| 0. Hand tip left, X | 6. Elbow left, X | 12. Wrist left, X | 18. Thumb left, X |
| 1. Hand tip left, Y | 7. Elbow left, Y | 13. Wrist left, X | 19. Thumb left, X |
| 2. Hand tip left, Z | 8. Elbow left, Z | 14. Wrist left, X | 20. Thumb left, X |
| 3. Hand tip righ, X | 9. Elbow righ, X | 15. Wrist righ, X | 21. Thumb righ, X |
| 4. Hand tip righ, Y | 10. Elbow righ, Y | 16. Wrist righ, X | 22. Thumb righ, X |
| 5. Hand tip righ, Z | 11. Elbow righ, Z | 17. Wrist righ, X | 23. Thumb righ, X |
The six classes are separate actions, with the following meaning:
| 1: I have command | 2: All clear | 3: Not clear | 4: Spread wings | 5: Fold wings | 6: Lock wings |
dsname = 'NATOPS' #'NATOPS', 'LSST', 'Wine', 'Epilepsy', 'HandMovementDirection'
# url = 'http://www.timeseriesclassification.com/Downloads/NATOPS.zip'
path = unzip_data(URLs_TS.NATOPS)
path
Path('/home/farid/.fastai/data/NATOPS')
Both Train and Train dataset contains 180 samples each. We concatenate them in order to have one big dataset and then split into train and valid dataset using our own split percentage (20%, 30%, or whatever number you see fit)
fname_train = f'{dsname}_TRAIN.arff'
fname_test = f'{dsname}_TEST.arff'
fnames = [path/fname_train, path/fname_test]
fnames
[Path('/home/farid/.fastai/data/NATOPS/NATOPS_TRAIN.arff'),
Path('/home/farid/.fastai/data/NATOPS/NATOPS_TEST.arff')]
data = TSData.from_arff(fnames)
print(data)
TSData:
Datasets names (concatenated): ['NATOPS_TRAIN', 'NATOPS_TEST']
Filenames: [Path('/home/farid/.fastai/data/NATOPS/NATOPS_TRAIN.arff'), Path('/home/farid/.fastai/data/NATOPS/NATOPS_TEST.arff')]
Data shape: (360, 24, 51)
Targets shape: (360,)
Nb Samples: 360
Nb Channels: 24
Sequence Length: 51
items = data.get_items()
idx = 1
x1, y1 = data.x[idx], data.y[idx]
y1
'3.0'
# You can select any channel to display buy supplying a list of channels and pass it to `chs` argument
# LEFT ARM
# show_timeseries(x1, title=y1, chs=[0,1,2,6,7,8,12,13,14,18,19,20])
# RIGHT ARM
# show_timeseries(x1, title=y1, chs=[3,4,5,9,10,11,15,16,17,21,22,23])
# ?show_timeseries(x1, title=y1, chs=range(0,24,3)) # Only the x axis coordinates
seed = 42
splits = RandomSplitter(seed=seed)(range_of(items)) #by default 80% for train split and 20% for valid split are chosen
splits
((#288) [304,281,114,329,115,130,338,294,94,310...],
(#72) [222,27,96,253,274,35,160,172,302,146...])
lbl_dict = dict([
('1.0', 'I have command'),
('2.0', 'All clear'),
('3.0', 'Not clear'),
('4.0', 'Spread wings'),
('5.0', 'Fold wings'),
('6.0', 'Lock wings')]
)
tfms = [[ItemGetter(0), ToTensorTS()], [ItemGetter(1), lbl_dict.get, Categorize()]]
# Create a dataset
ds = Datasets(items, tfms, splits=splits)
ax = show_at(ds, 2, figsize=(1,1))
Not clear
bs = 128
# Normalize at batch time
tfm_norm = Normalize(scale_subtype = 'per_sample_per_channel', scale_range=(0, 1)) # per_sample , per_sample_per_channel
# tfm_norm = Standardize(scale_subtype = 'per_sample')
batch_tfms = [tfm_norm]
dls1 = ds.dataloaders(bs=bs, val_bs=bs * 2, after_batch=batch_tfms, num_workers=0, device=default_device())
dls1.show_batch(max_n=9, chs=range(0,12,3))
tsdb = DataBlock(blocks=(TSBlock, CategoryBlock),
get_items=get_ts_items,
get_x = ItemGetter(0),
get_y = Pipeline([ItemGetter(1), lbl_dict.get]),
splitter=RandomSplitter(seed=seed),
batch_tfms = batch_tfms)
tsdb.summary(fnames)
Setting-up type transforms pipelines
Collecting items from [Path('/home/farid/.fastai/data/NATOPS/NATOPS_TRAIN.arff'), Path('/home/farid/.fastai/data/NATOPS/NATOPS_TEST.arff')]
Found 360 items
2 datasets of sizes 288,72
Setting up Pipeline: ItemGetter -> ToTensorTS
Setting up Pipeline: ItemGetter -> dict.get -> Categorize
Building one sample
Pipeline: ItemGetter -> ToTensorTS
starting from
([[-0.540579 -0.54101 -0.540603 ... -0.56305 -0.566314 -0.553712]
[-1.539567 -1.540042 -1.538992 ... -1.532014 -1.534645 -1.536015]
[-0.608539 -0.604609 -0.607679 ... -0.593769 -0.592854 -0.599014]
...
[ 0.454542 0.449924 0.453195 ... 0.480281 0.45537 0.457275]
[-1.411445 -1.363464 -1.390869 ... -1.468123 -1.368706 -1.386574]
[-0.473406 -0.453322 -0.463813 ... -0.440582 -0.427211 -0.435581]], 2.0)
applying ItemGetter gives
[[-0.540579 -0.54101 -0.540603 ... -0.56305 -0.566314 -0.553712]
[-1.539567 -1.540042 -1.538992 ... -1.532014 -1.534645 -1.536015]
[-0.608539 -0.604609 -0.607679 ... -0.593769 -0.592854 -0.599014]
...
[ 0.454542 0.449924 0.453195 ... 0.480281 0.45537 0.457275]
[-1.411445 -1.363464 -1.390869 ... -1.468123 -1.368706 -1.386574]
[-0.473406 -0.453322 -0.463813 ... -0.440582 -0.427211 -0.435581]]
applying ToTensorTS gives
TensorTS of size 24x51
Pipeline: ItemGetter -> dict.get -> Categorize
starting from
([[-0.540579 -0.54101 -0.540603 ... -0.56305 -0.566314 -0.553712]
[-1.539567 -1.540042 -1.538992 ... -1.532014 -1.534645 -1.536015]
[-0.608539 -0.604609 -0.607679 ... -0.593769 -0.592854 -0.599014]
...
[ 0.454542 0.449924 0.453195 ... 0.480281 0.45537 0.457275]
[-1.411445 -1.363464 -1.390869 ... -1.468123 -1.368706 -1.386574]
[-0.473406 -0.453322 -0.463813 ... -0.440582 -0.427211 -0.435581]], 2.0)
applying ItemGetter gives
2.0
applying dict.get gives
All clear
applying Categorize gives
TensorCategory(0)
Final sample: (TensorTS([[-0.5406, -0.5410, -0.5406, ..., -0.5630, -0.5663, -0.5537],
[-1.5396, -1.5400, -1.5390, ..., -1.5320, -1.5346, -1.5360],
[-0.6085, -0.6046, -0.6077, ..., -0.5938, -0.5929, -0.5990],
...,
[ 0.4545, 0.4499, 0.4532, ..., 0.4803, 0.4554, 0.4573],
[-1.4114, -1.3635, -1.3909, ..., -1.4681, -1.3687, -1.3866],
[-0.4734, -0.4533, -0.4638, ..., -0.4406, -0.4272, -0.4356]]), TensorCategory(0))
Setting up after_item: Pipeline: ToTensor
Setting up before_batch: Pipeline:
Setting up after_batch: Pipeline: Normalize
Building one batch
Applying item_tfms to the first sample:
Pipeline: ToTensor
starting from
(TensorTS of size 24x51, TensorCategory(0))
applying ToTensor gives
(TensorTS of size 24x51, TensorCategory(0))
Adding the next 3 samples
No before_batch transform to apply
Collating items in a batch
Applying batch_tfms to the batch built
Pipeline: Normalize
starting from
(TensorTS of size 4x24x51, TensorCategory([0, 3, 1, 3]))
applying Normalize gives
(TensorTS of size 4x24x51, TensorCategory([0, 3, 1, 3]))
# num_workers=0 is Microsoft Windows
dls2 = tsdb.dataloaders(fnames, num_workers=0, device=default_device())
dls2.show_batch(max_n=9, chs=range(0,12,3))
# getters = [ItemGetter(0), ItemGetter(1)]
tsdb = DataBlock(blocks=(TSBlock, CategoryBlock),
get_x = ItemGetter(0),
get_y = Pipeline([ItemGetter(1), lbl_dict.get]),
splitter=RandomSplitter(seed=seed))
dls3 = tsdb.dataloaders(data.get_items(), batch_tfms=batch_tfms, num_workers=0, device=default_device())
dls3.show_batch(max_n=9, chs=range(0,12,3))
dls4 = TSDataLoaders.from_files(fnames=fnames, path=path, batch_tfms=batch_tfms, lbl_dict=lbl_dict, num_workers=0, device=default_device())
dls4.show_batch(max_n=9, chs=range(0,12,3))
# Number of channels (i.e. dimensions in ARFF and TS files jargon)
c_in = get_n_channels(dls2.train) # data.n_channels
# Number of classes
c_out= dls2.c
c_in,c_out
(24, 6)
model = inception_time(c_in, c_out).to(device=default_device())
model
Sequential(
(0): SequentialEx(
(layers): ModuleList(
(0): InceptionModule(
(convs): ModuleList(
(0): Conv1d(24, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
(1): Conv1d(24, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
(2): Conv1d(24, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
)
(maxpool_bottleneck): Sequential(
(0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv1d(24, 32, kernel_size=(1,), stride=(1,), bias=False)
)
(bn_relu): Sequential(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU()
)
)
)
)
(1): SequentialEx(
(layers): ModuleList(
(0): InceptionModule(
(bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))
(convs): ModuleList(
(0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
(1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
(2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
)
(maxpool_bottleneck): Sequential(
(0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv1d(128, 32, kernel_size=(1,), stride=(1,), bias=False)
)
(bn_relu): Sequential(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU()
)
)
)
)
(2): SequentialEx(
(layers): ModuleList(
(0): InceptionModule(
(bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))
(convs): ModuleList(
(0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
(1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
(2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
)
(maxpool_bottleneck): Sequential(
(0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv1d(128, 32, kernel_size=(1,), stride=(1,), bias=False)
)
(bn_relu): Sequential(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU()
)
)
(1): Shortcut(
(act_fn): ReLU(inplace=True)
(conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
(bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(3): SequentialEx(
(layers): ModuleList(
(0): InceptionModule(
(bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))
(convs): ModuleList(
(0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
(1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
(2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
)
(maxpool_bottleneck): Sequential(
(0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv1d(128, 32, kernel_size=(1,), stride=(1,), bias=False)
)
(bn_relu): Sequential(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU()
)
)
)
)
(4): SequentialEx(
(layers): ModuleList(
(0): InceptionModule(
(bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))
(convs): ModuleList(
(0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
(1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
(2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
)
(maxpool_bottleneck): Sequential(
(0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv1d(128, 32, kernel_size=(1,), stride=(1,), bias=False)
)
(bn_relu): Sequential(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU()
)
)
)
)
(5): SequentialEx(
(layers): ModuleList(
(0): InceptionModule(
(bottleneck): Conv1d(128, 32, kernel_size=(1,), stride=(1,))
(convs): ModuleList(
(0): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), bias=False)
(1): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), bias=False)
(2): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), bias=False)
)
(maxpool_bottleneck): Sequential(
(0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
(1): Conv1d(128, 32, kernel_size=(1,), stride=(1,), bias=False)
)
(bn_relu): Sequential(
(0): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU()
)
)
(1): Shortcut(
(act_fn): ReLU(inplace=True)
(conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
(bn): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(6): AdaptiveConcatPool1d(
(ap): AdaptiveAvgPool1d(output_size=1)
(mp): AdaptiveMaxPool1d(output_size=1)
)
(7): Flatten(full=False)
(8): Linear(in_features=256, out_features=6, bias=True)
)
# opt_func = partial(Adam, lr=3e-3, wd=0.01)
#Or use Ranger
def opt_func(p, lr=slice(3e-3)): return Lookahead(RAdam(p, lr=lr, mom=0.95, wd=0.01))
#Learner
loss_func = LabelSmoothingCrossEntropy()
learn = Learner(dls2, model, opt_func=opt_func, loss_func=loss_func, metrics=accuracy)
print(learn.summary())
Sequential (Input shape: ['64 x 24 x 51'])
================================================================
Layer (type) Output Shape Param # Trainable
================================================================
Conv1d 64 x 32 x 51 29,952 True
________________________________________________________________
Conv1d 64 x 32 x 51 14,592 True
________________________________________________________________
Conv1d 64 x 32 x 51 6,912 True
________________________________________________________________
MaxPool1d 64 x 24 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 768 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,128 True
________________________________________________________________
Conv1d 64 x 32 x 51 39,936 True
________________________________________________________________
Conv1d 64 x 32 x 51 19,456 True
________________________________________________________________
Conv1d 64 x 32 x 51 9,216 True
________________________________________________________________
MaxPool1d 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,096 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,128 True
________________________________________________________________
Conv1d 64 x 32 x 51 39,936 True
________________________________________________________________
Conv1d 64 x 32 x 51 19,456 True
________________________________________________________________
Conv1d 64 x 32 x 51 9,216 True
________________________________________________________________
MaxPool1d 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,096 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 128 x 51 16,384 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
Conv1d 64 x 32 x 51 4,128 True
________________________________________________________________
Conv1d 64 x 32 x 51 39,936 True
________________________________________________________________
Conv1d 64 x 32 x 51 19,456 True
________________________________________________________________
Conv1d 64 x 32 x 51 9,216 True
________________________________________________________________
MaxPool1d 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,096 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,128 True
________________________________________________________________
Conv1d 64 x 32 x 51 39,936 True
________________________________________________________________
Conv1d 64 x 32 x 51 19,456 True
________________________________________________________________
Conv1d 64 x 32 x 51 9,216 True
________________________________________________________________
MaxPool1d 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,096 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,128 True
________________________________________________________________
Conv1d 64 x 32 x 51 39,936 True
________________________________________________________________
Conv1d 64 x 32 x 51 19,456 True
________________________________________________________________
Conv1d 64 x 32 x 51 9,216 True
________________________________________________________________
MaxPool1d 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 32 x 51 4,096 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
ReLU 64 x 128 x 51 0 False
________________________________________________________________
Conv1d 64 x 128 x 51 16,384 True
________________________________________________________________
BatchNorm1d 64 x 128 x 51 256 True
________________________________________________________________
AdaptiveAvgPool1d 64 x 128 x 1 0 False
________________________________________________________________
AdaptiveMaxPool1d 64 x 128 x 1 0 False
________________________________________________________________
Flatten 64 x 256 0 False
________________________________________________________________
Linear 64 x 6 1,542 True
________________________________________________________________
Total params: 472,742
Total trainable params: 472,742
Total non-trainable params: 0
Optimizer used: <function opt_func at 0x7fb11c99f400>
Loss function: LabelSmoothingCrossEntropy()
Callbacks:
- TrainEvalCallback
- Recorder
- ProgressCallback
lr_min, lr_steep = learn.lr_find()
lr_min, lr_steep
<style>
/* Turns off some styling */
progress {
/* gets rid of default border in Firefox and Opera. */
border: none;
/* Needs to be in here for Safari polyfill so background images work as expected. */
background-size: auto;
}
.progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {
background: #F44336;
}
</style>
(0.00831763744354248, 0.0006918309954926372)
learn.fit_one_cycle(25, lr_max=1e-3)
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 0 | 3.001498 | 1.795478 | 0.222222 | 00:01 |
| 1 | 2.909164 | 1.799713 | 0.222222 | 00:01 |
| 2 | 2.758937 | 1.805732 | 0.222222 | 00:01 |
| 3 | 2.552927 | 1.810526 | 0.222222 | 00:01 |
| 4 | 2.272452 | 1.817920 | 0.180556 | 00:02 |
| 5 | 1.995428 | 1.829209 | 0.111111 | 00:02 |
| 6 | 1.776214 | 1.749636 | 0.222222 | 00:01 |
| 7 | 1.597963 | 1.653429 | 0.347222 | 00:02 |
| 8 | 1.453098 | 1.463801 | 0.444444 | 00:02 |
| 9 | 1.337819 | 1.185544 | 0.666667 | 00:01 |
| 10 | 1.241440 | 0.982497 | 0.777778 | 00:02 |
| 11 | 1.160481 | 0.845832 | 0.819444 | 00:02 |
| 12 | 1.089517 | 0.751684 | 0.833333 | 00:02 |
| 13 | 1.026505 | 0.733695 | 0.833333 | 00:02 |
| 14 | 0.973174 | 0.693617 | 0.861111 | 00:02 |
| 15 | 0.926334 | 0.686428 | 0.805556 | 00:02 |
| 16 | 0.884449 | 0.684725 | 0.875000 | 00:02 |
| 17 | 0.848235 | 0.659447 | 0.833333 | 00:02 |
| 18 | 0.814864 | 0.654701 | 0.847222 | 00:02 |
| 19 | 0.784517 | 0.654098 | 0.875000 | 00:02 |
| 20 | 0.757529 | 0.648219 | 0.875000 | 00:02 |
| 21 | 0.732877 | 0.649778 | 0.861111 | 00:02 |
| 22 | 0.710833 | 0.644054 | 0.875000 | 00:02 |
| 23 | 0.691595 | 0.641094 | 0.875000 | 00:02 |
| 24 | 0.674118 | 0.639970 | 0.861111 | 00:02 |
learn.recorder.plot_loss()
learn.show_results(max_n=9, chs=range(0,12,3))
<style>
/* Turns off some styling */
progress {
/* gets rid of default border in Firefox and Opera. */
border: none;
/* Needs to be in here for Safari polyfill so background images work as expected. */
background-size: auto;
}
.progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {
background: #F44336;
}
</style>
interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix(figsize=(10,8))
<style>
/* Turns off some styling */
progress {
/* gets rid of default border in Firefox and Opera. */
border: none;
/* Needs to be in here for Safari polyfill so background images work as expected. */
background-size: auto;
}
.progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {
background: #F44336;
}
</style>
