TabNet for fastai

This is an adaptation of TabNet (Attention-based network for tabular data) for fastai (>=2.0) library. The original paper https://arxiv.org/pdf/1908.07442.pdf.

Install

pip install fast_tabnet

How to use

model = TabNetModel(emb_szs, n_cont, out_sz, embed_p=0., y_range=None, n_d=8, n_a=8, n_steps=3, gamma=1.5, n_independent=2, n_shared=2, epsilon=1e-15, virtual_batch_size=128, momentum=0.02)

Parameters emb_szs, n_cont, out_sz, embed_p, y_range are the same as for fastai TabularModel.

n_d : int Dimension of the prediction layer (usually between 4 and 64)
n_a : int Dimension of the attention layer (usually between 4 and 64)
n_steps: int Number of sucessive steps in the newtork (usually betwenn 3 and 10)
gamma : float Float above 1, scaling factor for attention updates (usually betwenn 1.0 to 2.0)
momentum : float Float value between 0 and 1 which will be used for momentum in all batch norm
n_independent : int Number of independent GLU layer in each GLU block (default 2)
n_shared : int Number of independent GLU layer in each GLU block (default 2)
epsilon: float Avoid log(0), this should be kept very low

Example

Below is an example from fastai library, but the model in use is TabNet

from fastai.basics import *
from fastai.tabular.all import *
from fast_tabnet.core import *

path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
df_main,df_test = df.iloc[:-1000].copy(),df.iloc[-1000:].copy()
df_main.head()

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	hours-per-week	native-country	salary
0	49	Private	101320	Assoc-acdm	12.0	Married-civ-spouse	NaN	Wife	White	Female	0	1902	40	United-States	>=50k
1	44	Private	236746	Masters	14.0	Divorced	Exec-managerial	Not-in-family	White	Male	10520	0	45	United-States	>=50k
2	38	Private	96185	HS-grad	NaN	Divorced	NaN	Unmarried	Black	Female	0	0	32	United-States	<50k
3	38	Self-emp-inc	112847	Prof-school	15.0	Married-civ-spouse	Prof-specialty	Husband	Asian-Pac-Islander	Male	0	0	40	United-States	>=50k
4	42	Self-emp-not-inc	82297	7th-8th	NaN	Married-civ-spouse	Other-service	Wife	Black	Female	0	0	50	United-States	<50k

cat_names = ['workclass', 'education', 'marital-status', 'occupation', 
             'relationship', 'race', 'native-country', 'sex']
cont_names = ['age', 'fnlwgt', 'education-num']
procs = [Categorify, FillMissing, Normalize]
splits = RandomSplitter()(range_of(df_main))

to = TabularPandas(df_main, procs, cat_names, cont_names, y_names="salary", 
                   y_block = CategoryBlock(), splits=splits)

dls = to.dataloaders(bs=32)

dls.valid.show_batch()

	workclass	education	marital-status	occupation	relationship	race	native-country	sex	education-num_na	age	fnlwgt	education-num	salary
0	Private	HS-grad	Married-civ-spouse	Other-service	Wife	White	United-States	Female	False	39.000000	196673.000115	9.0	<50k
1	Private	HS-grad	Married-civ-spouse	Craft-repair	Husband	White	United-States	Male	False	32.000000	198067.999771	9.0	<50k
2	State-gov	HS-grad	Never-married	Adm-clerical	Own-child	White	United-States	Female	False	18.999999	176633.999931	9.0	<50k
3	Private	Some-college	Married-civ-spouse	Prof-specialty	Husband	White	United-States	Male	False	67.999999	107626.998490	10.0	<50k
4	Private	Masters	Never-married	Exec-managerial	Not-in-family	Black	United-States	Male	False	29.000000	214925.000260	14.0	<50k
5	Private	HS-grad	Married-civ-spouse	Priv-house-serv	Wife	White	United-States	Female	False	22.000000	200109.000126	9.0	<50k
6	Private	Some-college	Never-married	Sales	Own-child	White	United-States	Female	False	18.000000	60980.998429	10.0	<50k
7	Private	Some-college	Separated	Adm-clerical	Not-in-family	White	United-States	Female	False	28.000000	334367.998199	10.0	<50k
8	Private	11th	Married-civ-spouse	Transport-moving	Husband	White	United-States	Male	False	49.000000	123584.001097	7.0	<50k
9	Private	Masters	Never-married	Prof-specialty	Not-in-family	White	United-States	Female	False	26.000000	397316.999922	14.0	<50k

to_tst = to.new(df_test)
to_tst.process()
to_tst.all_cols.head()

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	workclass	education	marital-status	occupation	relationship	race	native-country	sex	education-num_na	age	fnlwgt	education-num
31561	5	2	5	9	3	3	40	2	1	-1.505833	-0.559418	-1.202170
31562	2	12	5	2	5	3	40	1	1	-1.432653	0.421241	-0.418032
31563	5	7	3	4	1	5	40	2	1	-0.115406	0.132868	-1.986307
31564	8	12	3	9	1	5	40	2	1	1.494561	0.749805	-0.418032
31565	1	12	1	1	5	3	40	2	1	-0.481308	7.529798	-0.418032

emb_szs = get_emb_sz(to)

That's the use of the model

model = TabNetModel(emb_szs, len(to.cont_names), dls.c, n_d=8, n_a=8, n_steps=5, mask_type='entmax');

learn = Learner(dls, model, CrossEntropyLossFlat(), opt_func=Adam, lr=3e-2, metrics=[accuracy])

learn.lr_find()

SuggestedLRs(lr_min=0.2754228591918945, lr_steep=1.9054607491852948e-06)

learn.fit_one_cycle(5)

epoch	train_loss	valid_loss	accuracy	time
0	0.446274	0.414451	0.817015	00:30
1	0.380002	0.393030	0.818916	00:30
2	0.371149	0.359802	0.832066	00:30
3	0.349027	0.352255	0.835868	00:30
4	0.355339	0.349360	0.836819	00:30

Tabnet interpretability

# feature importance for 2k rows
dl = learn.dls.test_dl(df.iloc[:2000], bs=256)
feature_importances = tabnet_feature_importances(learn.model, dl)

# per sample interpretation
dl = learn.dls.test_dl(df.iloc[:20], bs=20)
res_explain, res_masks = tabnet_explain(learn.model, dl)

plt.xticks(rotation='vertical')
plt.bar(dl.x_names, feature_importances, color='g')
plt.show()

def plot_explain(masks, lbls, figsize=(12,12)):
    "Plots masks with `lbls` (`dls.x_names`)"
    fig = plt.figure(figsize=figsize)
    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
    plt.yticks(np.arange(0, len(masks), 1.0))
    plt.xticks(np.arange(0, len(masks[0]), 1.0))
    ax.set_xticklabels(lbls, rotation=90)
    plt.ylabel('Sample Number')
    plt.xlabel('Variable')
    plt.imshow(masks)

plot_explain(res_explain, dl.x_names)

Hyperparameter search with Bayesian Optimization

If your dataset isn't huge you can tune hyperparameters for tabular models with Bayesian Optimization. You can optimize directly your metric using this approach if the metric is sensitive enough (in our example it is not and we use validation loss instead). Also, you should create the second validation set, because you will use the first as a training set for Bayesian Optimization.

You may need to install the optimizer pip install bayesian-optimization

from functools import lru_cache

# The function we'll optimize
@lru_cache(1000)
def get_accuracy(n_d:Int, n_a:Int, n_steps:Int):
    model = TabNetModel(emb_szs, len(to.cont_names), dls.c, n_d=n_d, n_a=n_a, n_steps=n_steps, gamma=1.5)
    learn = Learner(dls, model, CrossEntropyLossFlat(), opt_func=opt_func, lr=3e-2, metrics=[accuracy])
    learn.fit_one_cycle(5)
    return float(learn.validate(dl=learn.dls.valid)[1])

This implementation of Bayesian Optimization doesn't work naturally with descreet values. That's why we use wrapper with lru_cache.

def fit_accuracy(pow_n_d, pow_n_a, pow_n_steps):
    n_d, n_a, n_steps = map(lambda x: 2**int(x), (pow_n_d, pow_n_a, pow_n_steps))
    return get_accuracy(n_d, n_a, n_steps)

from bayes_opt import BayesianOptimization

# Bounded region of parameter space
pbounds = {'pow_n_d': (0, 8), 'pow_n_a': (0, 8), 'pow_n_steps': (0, 4)}

optimizer = BayesianOptimization(
    f=fit_accuracy,
    pbounds=pbounds,
)

optimizer.maximize(
    init_points=15,
    n_iter=100,
)

|   iter    |  target   |  pow_n_a  |  pow_n_d  | pow_n_... |
-------------------------------------------------------------

epoch	train_loss	valid_loss	accuracy	time
0	0.404888	0.432834	0.793885	00:10
1	0.367979	0.384840	0.818600	00:09
2	0.366444	0.372005	0.819708	00:09
3	0.362771	0.366949	0.823511	00:10
4	0.353682	0.367132	0.823511	00:10

| �[0m 1       �[0m | �[0m 0.8235  �[0m | �[0m 0.9408  �[0m | �[0m 1.898   �[0m | �[0m 1.652   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.393301	0.449742	0.810836	00:08
1	0.379140	0.413773	0.815589	00:07
2	0.355790	0.388907	0.822560	00:07
3	0.349984	0.362671	0.828739	00:07
4	0.348000	0.360150	0.827313	00:07

| �[95m 2       �[0m | �[95m 0.8273  �[0m | �[95m 4.262   �[0m | �[95m 5.604   �[0m | �[95m 0.2437  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.451572	0.434189	0.781210	00:12
1	0.423763	0.413420	0.805450	00:12
2	0.398922	0.408688	0.814164	00:12
3	0.390981	0.392398	0.808935	00:12
4	0.376418	0.382250	0.817174	00:12

| �[0m 3       �[0m | �[0m 0.8172  �[0m | �[0m 7.233   �[0m | �[0m 6.471   �[0m | �[0m 2.508   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.403187	0.413986	0.798162	00:07
1	0.398544	0.390102	0.820184	00:07
2	0.390569	0.389703	0.825253	00:07
3	0.375426	0.385706	0.826996	00:07
4	0.370446	0.383366	0.831115	00:06

| �[95m 4       �[0m | �[95m 0.8311  �[0m | �[95m 5.935   �[0m | �[95m 1.241   �[0m | �[95m 0.3809  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.464145	0.458641	0.751267	00:18
1	0.424691	0.436968	0.788023	00:18
2	0.431576	0.436581	0.775824	00:18
3	0.432143	0.437062	0.759506	00:18
4	0.429915	0.438332	0.758555	00:18

| �[0m 5       �[0m | �[0m 0.7586  �[0m | �[0m 2.554   �[0m | �[0m 0.4992  �[0m | �[0m 3.111   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.470359	0.475826	0.748891	00:12
1	0.411564	0.409433	0.797053	00:12
2	0.392718	0.397363	0.809727	00:12
3	0.387564	0.380033	0.814322	00:12
4	0.374153	0.378258	0.818916	00:12

| �[0m 6       �[0m | �[0m 0.8189  �[0m | �[0m 4.592   �[0m | �[0m 2.138   �[0m | �[0m 2.824   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.547042	0.588752	0.754119	00:18
1	0.491731	0.469795	0.771863	00:18
2	0.454340	0.433961	0.775190	00:18
3	0.424386	0.432385	0.782953	00:18
4	0.397645	0.406420	0.805767	00:19

| �[0m 7       �[0m | �[0m 0.8058  �[0m | �[0m 6.186   �[0m | �[0m 7.016   �[0m | �[0m 3.316   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.485245	0.487635	0.751109	00:18
1	0.450832	0.446423	0.750317	00:18
2	0.448203	0.449419	0.755228	00:18
3	0.430258	0.443562	0.744297	00:18
4	0.429821	0.437173	0.761565	00:18

| �[0m 8       �[0m | �[0m 0.7616  �[0m | �[0m 2.018   �[0m | �[0m 1.316   �[0m | �[0m 3.675   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.458425	0.455733	0.751584	00:12
1	0.439781	0.467807	0.751109	00:12
2	0.420331	0.432216	0.775190	00:12
3	0.421012	0.421412	0.782319	00:12
4	0.401828	0.413434	0.801014	00:12

| �[0m 9       �[0m | �[0m 0.801   �[0m | �[0m 2.051   �[0m | �[0m 1.958   �[0m | �[0m 2.332   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.546997	0.506728	0.761407	00:18
1	0.489712	0.439324	0.799588	00:18
2	0.448558	0.448419	0.786122	00:18
3	0.436869	0.435375	0.801648	00:18
4	0.417128	0.421093	0.798321	00:18

| �[0m 10      �[0m | �[0m 0.7983  �[0m | �[0m 5.203   �[0m | �[0m 7.719   �[0m | �[0m 3.407   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.380781	0.463409	0.786439	00:07
1	0.359212	0.461147	0.798321	00:07
2	0.351414	0.368950	0.822719	00:07
3	0.347257	0.367056	0.829373	00:07
4	0.337212	0.362375	0.830799	00:07

| �[0m 11      �[0m | �[0m 0.8308  �[0m | �[0m 6.048   �[0m | �[0m 4.376   �[0m | �[0m 0.08141 �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.430772	0.430897	0.767744	00:12
1	0.402611	0.432137	0.764259	00:12
2	0.407579	0.409651	0.812104	00:12
3	0.374988	0.391822	0.816698	00:12
4	0.378011	0.389278	0.816065	00:12

| �[0m 12      �[0m | �[0m 0.8161  �[0m | �[0m 7.083   �[0m | �[0m 1.385   �[0m | �[0m 2.806   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.402018	0.412051	0.812262	00:09
1	0.372804	0.464937	0.811629	00:09
2	0.368274	0.384675	0.820184	00:09
3	0.364502	0.371920	0.820659	00:09
4	0.348998	0.369445	0.823828	00:09

| �[0m 13      �[0m | �[0m 0.8238  �[0m | �[0m 4.812   �[0m | �[0m 3.785   �[0m | �[0m 1.396   �[0m |
| �[0m 14      �[0m | �[0m 0.8172  �[0m | �[0m 7.672   �[0m | �[0m 6.719   �[0m | �[0m 2.72    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.476033	0.442598	0.803549	00:12
1	0.405236	0.414015	0.788973	00:11
2	0.406291	0.451269	0.789449	00:11
3	0.391013	0.393243	0.816065	00:12
4	0.374160	0.377635	0.821451	00:12

| �[0m 15      �[0m | �[0m 0.8215  �[0m | �[0m 6.464   �[0m | �[0m 7.954   �[0m | �[0m 2.647   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.390142	0.390678	0.810995	00:06
1	0.381717	0.382202	0.813055	00:06
2	0.368564	0.378705	0.823828	00:06
3	0.358858	0.368329	0.823511	00:07
4	0.353392	0.363913	0.825887	00:06

| �[0m 16      �[0m | �[0m 0.8259  �[0m | �[0m 0.1229  �[0m | �[0m 7.83    �[0m | �[0m 0.3708  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.381215	0.422651	0.800697	00:06
1	0.377345	0.380863	0.815906	00:06
2	0.366631	0.370579	0.822877	00:06
3	0.362745	0.366619	0.823352	00:07
4	0.356861	0.364835	0.825887	00:07

| �[0m 17      �[0m | �[0m 0.8259  �[0m | �[0m 0.03098 �[0m | �[0m 3.326   �[0m | �[0m 0.007025�[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.404604	0.443035	0.824461	00:07
1	0.361872	0.388880	0.823669	00:06
2	0.375164	0.369968	0.825095	00:06
3	0.352091	0.363823	0.827947	00:06
4	0.335458	0.362544	0.829373	00:07

| �[0m 18      �[0m | �[0m 0.8294  �[0m | �[0m 7.81    �[0m | �[0m 7.976   �[0m | �[0m 0.0194  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.679292	0.677299	0.248891	00:05
1	0.675403	0.678406	0.248891	00:05
2	0.673259	0.673374	0.248891	00:06
3	0.674996	0.673514	0.248891	00:07
4	0.668813	0.673671	0.248891	00:07

| �[0m 19      �[0m | �[0m 0.2489  �[0m | �[0m 0.4499  �[0m | �[0m 0.138   �[0m | �[0m 0.001101�[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.524201	0.528132	0.729880	00:30
1	0.419377	0.403198	0.812104	00:31
2	0.399398	0.418890	0.812421	00:31
3	0.381651	0.391744	0.819075	00:31
4	0.368742	0.377904	0.822085	00:31

| �[0m 20      �[0m | �[0m 0.8221  �[0m | �[0m 0.0     �[0m | �[0m 6.575   �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.681083	0.682397	0.248891	00:05
1	0.672935	0.679371	0.248891	00:06
2	0.675200	0.673466	0.248891	00:06
3	0.674251	0.673356	0.248891	00:06
4	0.668861	0.673186	0.248891	00:06

| �[0m 21      �[0m | �[0m 0.2489  �[0m | �[0m 8.0     �[0m | �[0m 0.0     �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.407246	0.432203	0.801331	00:10
1	0.385086	0.399513	0.811312	00:11
2	0.377365	0.384121	0.816065	00:12
3	0.366855	0.371010	0.823194	00:12
4	0.361931	0.368933	0.825095	00:12

| �[0m 22      �[0m | �[0m 0.8251  �[0m | �[0m 0.0     �[0m | �[0m 4.502   �[0m | �[0m 2.193   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.493623	0.476921	0.766001	00:30
1	0.441126	0.443774	0.776774	00:31
2	0.424523	0.437125	0.783904	00:31
3	0.402457	0.408628	0.795944	00:31
4	0.439420	0.431756	0.788973	00:32

| �[0m 23      �[0m | �[0m 0.789   �[0m | �[0m 8.0     �[0m | �[0m 3.702   �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.515615	0.513919	0.751109	00:31
1	0.462674	0.495322	0.751584	00:31
2	0.465430	0.483685	0.751267	00:31
3	0.481308	0.495375	0.755070	00:31
4	0.481324	0.491275	0.754911	00:32

| �[0m 24      �[0m | �[0m 0.7549  �[0m | �[0m 6.009   �[0m | �[0m 0.0     �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.422837	0.403953	0.819392	00:06
1	0.380753	0.367345	0.826838	00:06
2	0.353045	0.365174	0.830006	00:07
3	0.348628	0.364282	0.826362	00:07
4	0.343561	0.361509	0.829214	00:07

| �[0m 25      �[0m | �[0m 0.8292  �[0m | �[0m 3.522   �[0m | �[0m 8.0     �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.807766	1.307279	0.481622	00:31
1	0.513308	0.499470	0.783587	00:32
2	0.445906	0.492620	0.798004	00:31
3	0.385094	0.399986	0.807509	00:32
4	0.387228	0.384739	0.817015	00:31

| �[0m 26      �[0m | �[0m 0.817   �[0m | �[0m 0.0     �[0m | �[0m 8.0     �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.442076	0.491338	0.755387	00:31
1	0.441078	0.443674	0.760773	00:31
2	0.417575	0.418758	0.792142	00:31
3	0.410825	0.417581	0.788498	00:34
4	0.403407	0.410941	0.798321	00:46

| �[0m 27      �[0m | �[0m 0.7983  �[0m | �[0m 0.0     �[0m | �[0m 0.0     �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.407006	0.419679	0.792142	00:08
1	0.390913	0.392631	0.810520	00:08
2	0.365560	0.394330	0.817491	00:08
3	0.378459	0.387244	0.820659	00:08
4	0.375275	0.385417	0.828897	00:08

| �[0m 28      �[0m | �[0m 0.8289  �[0m | �[0m 3.379   �[0m | �[0m 2.848   �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.430604	0.469592	0.781210	00:45
1	0.423074	0.429704	0.797529	00:45
2	0.400120	0.393398	0.810995	00:45
3	0.382361	0.390651	0.816065	00:46
4	0.389520	0.401878	0.807193	00:46

| �[0m 29      �[0m | �[0m 0.8072  �[0m | �[0m 0.0     �[0m | �[0m 2.588   �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.396348	0.397454	0.806717	00:08
1	0.383342	0.386023	0.819550	00:07
2	0.369493	0.374401	0.820025	00:07
3	0.356015	0.366535	0.826204	00:08
4	0.341073	0.365241	0.826204	00:08

| �[0m 30      �[0m | �[0m 0.8262  �[0m | �[0m 1.217   �[0m | �[0m 5.622   �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.809077	0.480591	0.782795	00:45
1	0.571318	0.497731	0.739068	00:45
2	0.514562	0.461726	0.781527	00:45
3	0.439822	0.451722	0.787231	00:44
4	0.419881	0.422125	0.801648	00:45

| �[0m 31      �[0m | �[0m 0.8016  �[0m | �[0m 8.0     �[0m | �[0m 8.0     �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.410521	0.435045	0.810044	00:08
1	0.363098	0.378001	0.821926	00:08
2	0.359525	0.364477	0.827788	00:08
3	0.354005	0.366507	0.821610	00:08
4	0.347293	0.362657	0.829373	00:08

| �[0m 32      �[0m | �[0m 0.8294  �[0m | �[0m 5.864   �[0m | �[0m 8.0     �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.498376	0.436696	0.794043	00:16
1	0.411699	0.435537	0.801331	00:16
2	0.385327	0.396916	0.820184	00:16
3	0.382020	0.389856	0.813371	00:16
4	0.373869	0.377804	0.820817	00:15

| �[0m 33      �[0m | �[0m 0.8208  �[0m | �[0m 1.776   �[0m | �[0m 8.0     �[0m | �[0m 2.212   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.404653	0.440106	0.772180	00:11
1	0.377931	0.393715	0.817332	00:11
2	0.373221	0.379273	0.826838	00:11
3	0.359682	0.362844	0.828422	00:11
4	0.340384	0.363072	0.828897	00:11

| �[0m 34      �[0m | �[0m 0.8289  �[0m | �[0m 5.777   �[0m | �[0m 2.2     �[0m | �[0m 1.31    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.520308	0.503207	0.749208	00:45
1	0.472501	0.451469	0.780418	00:45
2	0.454686	0.429175	0.784854	00:45
3	0.400800	0.413727	0.795469	00:44
4	0.405604	0.409770	0.801648	00:45

| �[0m 35      �[0m | �[0m 0.8016  �[0m | �[0m 2.748   �[0m | �[0m 5.915   �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.504537	0.501541	0.750317	00:45
1	0.465937	0.477715	0.773289	00:45
2	0.435364	0.481415	0.766635	00:45
3	0.425434	0.442198	0.772814	00:45
4	0.425779	0.458947	0.771863	00:45

| �[0m 36      �[0m | �[0m 0.7719  �[0m | �[0m 6.251   �[0m | �[0m 2.532   �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.420782	0.420721	0.791350	00:10
1	0.403576	0.408376	0.800222	00:10
2	0.390236	0.393624	0.820342	00:11
3	0.377777	0.389657	0.821610	00:11
4	0.382809	0.386011	0.820976	00:11

| �[0m 37      �[0m | �[0m 0.821   �[0m | �[0m 5.093   �[0m | �[0m 0.172   �[0m | �[0m 1.64    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.393575	0.397811	0.812262	00:08
1	0.378272	0.381915	0.815748	00:08
2	0.364799	0.369214	0.824620	00:08
3	0.355757	0.364554	0.826996	00:08
4	0.342090	0.362723	0.824303	00:08

| �[0m 38      �[0m | �[0m 0.8243  �[0m | �[0m 8.0     �[0m | �[0m 5.799   �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.393693	0.396980	0.822085	00:11
1	0.361231	0.393146	0.813847	00:11
2	0.345645	0.379510	0.823986	00:11
3	0.349778	0.367077	0.826679	00:11
4	0.342390	0.362027	0.827788	00:11

| �[0m 39      �[0m | �[0m 0.8278  �[0m | �[0m 1.62    �[0m | �[0m 3.832   �[0m | �[0m 1.151   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.832737	0.491002	0.771546	00:43
1	0.627948	0.553552	0.764734	00:43
2	0.498901	0.467162	0.791984	00:46
3	0.431196	0.444576	0.785646	00:43
4	0.399745	0.427060	0.796578	00:42

| �[0m 40      �[0m | �[0m 0.7966  �[0m | �[0m 2.198   �[0m | �[0m 8.0     �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.511301	0.514401	0.751267	00:43
1	0.447332	0.445157	0.751109	00:43
2	0.451125	0.438327	0.750951	00:42
3	0.445883	0.443266	0.751267	00:42
4	0.444816	0.438459	0.764100	00:42

| �[0m 41      �[0m | �[0m 0.7641  �[0m | �[0m 8.0     �[0m | �[0m 1.03    �[0m | �[0m 4.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.408504	0.413275	0.797212	00:15
1	0.392707	0.399085	0.805767	00:15
2	0.379938	0.395550	0.817807	00:15
3	0.375288	0.383186	0.820817	00:15
4	0.360417	0.375098	0.823194	00:16

| �[0m 42      �[0m | �[0m 0.8232  �[0m | �[0m 0.0     �[0m | �[0m 2.504   �[0m | �[0m 2.135   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.399371	0.415196	0.801014	00:07
1	0.367804	0.392020	0.810995	00:06
2	0.362288	0.385124	0.820659	00:07
3	0.344728	0.371339	0.823669	00:07
4	0.345769	0.362059	0.829373	00:07

| �[0m 43      �[0m | �[0m 0.8294  �[0m | �[0m 0.0     �[0m | �[0m 5.441   �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.397157	0.431003	0.803866	00:06
1	0.394964	0.396448	0.810361	00:06
2	0.378584	0.387943	0.820659	00:07
3	0.371601	0.386186	0.818283	00:07
4	0.369759	0.384339	0.827630	00:07

| �[0m 44      �[0m | �[0m 0.8276  �[0m | �[0m 4.636   �[0m | �[0m 1.476   �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.408654	0.426806	0.791191	00:12
1	0.394184	0.406586	0.786439	00:12
2	0.369625	0.372680	0.822560	00:12
3	0.349444	0.368142	0.823828	00:12
4	0.351684	0.363406	0.826204	00:12

| �[0m 45      �[0m | �[0m 0.8262  �[0m | �[0m 0.0     �[0m | �[0m 7.071   �[0m | �[0m 2.071   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.400293	0.416098	0.811629	00:08
1	0.377387	0.433395	0.807034	00:08
2	0.368131	0.395448	0.796420	00:08
3	0.367750	0.376879	0.817174	00:08
4	0.362124	0.371432	0.821134	00:08

| �[0m 46      �[0m | �[0m 0.8211  �[0m | �[0m 4.26    �[0m | �[0m 6.934   �[0m | �[0m 1.79    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.404579	0.437443	0.814797	00:07
1	0.375342	0.380416	0.824937	00:07
2	0.365835	0.377617	0.812738	00:07
3	0.354619	0.364503	0.827471	00:07
4	0.340603	0.363488	0.827947	00:07

| �[0m 47      �[0m | �[0m 0.8279  �[0m | �[0m 6.579   �[0m | �[0m 6.485   �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.384890	0.440342	0.812579	00:08
1	0.371483	0.387200	0.813847	00:09
2	0.365951	0.378071	0.818283	00:09
3	0.362965	0.369994	0.821610	00:09
4	0.356483	0.365151	0.826521	00:09

| �[0m 48      �[0m | �[0m 0.8265  �[0m | �[0m 8.0     �[0m | �[0m 4.293   �[0m | �[0m 1.74    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.386308	0.389250	0.815431	00:08
1	0.368402	0.389338	0.814956	00:09
2	0.362211	0.377196	0.824778	00:09
3	0.356135	0.362951	0.829531	00:09
4	0.341577	0.362476	0.830799	00:09

| �[0m 49      �[0m | �[0m 0.8308  �[0m | �[0m 7.909   �[0m | �[0m 7.827   �[0m | �[0m 1.323   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.426044	0.422882	0.791984	00:08
1	0.375756	0.381810	0.817491	00:09
2	0.363932	0.375904	0.818916	00:09
3	0.349442	0.365052	0.823986	00:09
4	0.344509	0.363027	0.830323	00:09

| �[0m 50      �[0m | �[0m 0.8303  �[0m | �[0m 4.946   �[0m | �[0m 1.246   �[0m | �[0m 1.589   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.388522	0.431909	0.820976	00:07
1	0.372532	0.448644	0.751109	00:07
2	0.358823	0.373322	0.823669	00:07
3	0.352838	0.362424	0.831591	00:07
4	0.352949	0.361356	0.831432	00:07

| �[95m 51      �[0m | �[95m 0.8314  �[0m | �[95m 5.664   �[0m | �[95m 2.626   �[0m | �[95m 0.003048�[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.389195	0.390032	0.817332	00:06
1	0.369993	0.382199	0.819708	00:07
2	0.362801	0.373282	0.826521	00:06
3	0.359760	0.363597	0.824303	00:06
4	0.344525	0.362097	0.828897	00:07

| �[0m 52      �[0m | �[0m 0.8289  �[0m | �[0m 1.287   �[0m | �[0m 3.505   �[0m | �[0m 0.06804 �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.394940	0.403165	0.814639	00:06
1	0.371518	0.452118	0.806876	00:06
2	0.364734	0.377214	0.824461	00:06
3	0.347968	0.365335	0.823511	00:07
4	0.345476	0.363670	0.827155	00:07

| �[0m 53      �[0m | �[0m 0.8272  �[0m | �[0m 1.606   �[0m | �[0m 7.998   �[0m | �[0m 0.2009  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.500322	0.519261	0.757129	00:10
1	0.413270	0.423630	0.801965	00:11
2	0.380234	0.395588	0.813371	00:12
3	0.361677	0.378123	0.817174	00:12
4	0.374629	0.373772	0.820025	00:12

| �[0m 54      �[0m | �[0m 0.82    �[0m | �[0m 4.579   �[0m | �[0m 5.017   �[0m | �[0m 2.928   �[0m |
| �[0m 55      �[0m | �[0m 0.8259  �[0m | �[0m 0.02565 �[0m | �[0m 3.699   �[0m | �[0m 0.9808  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.452787	0.443697	0.768695	00:11
1	0.428332	0.415454	0.800697	00:11
2	0.396522	0.402850	0.807668	00:12
3	0.424802	0.414648	0.783587	00:12
4	0.385055	0.392359	0.801489	00:12

| �[0m 56      �[0m | �[0m 0.8015  �[0m | �[0m 1.927   �[0m | �[0m 5.92    �[0m | �[0m 2.53    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.435597	0.438222	0.810836	00:19
1	0.399920	0.531189	0.770754	00:19
2	0.403408	0.409382	0.804816	00:18
3	0.363519	0.383823	0.815906	00:19
4	0.360030	0.377621	0.819708	00:19

| �[0m 57      �[0m | �[0m 0.8197  �[0m | �[0m 0.7796  �[0m | �[0m 4.576   �[0m | �[0m 3.952   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.388445	0.420243	0.800539	00:07
1	0.372912	0.369659	0.827630	00:07
2	0.354443	0.366757	0.828105	00:07
3	0.352468	0.366038	0.822560	00:07
4	0.347822	0.362001	0.829690	00:07

| �[0m 58      �[0m | �[0m 0.8297  �[0m | �[0m 3.525   �[0m | �[0m 4.198   �[0m | �[0m 0.02314 �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.444627	0.431739	0.787072	00:12
1	0.392637	0.412985	0.799747	00:12
2	0.369733	0.396133	0.802440	00:12
3	0.365821	0.373095	0.820342	00:12
4	0.370486	0.371560	0.819392	00:12

| �[0m 59      �[0m | �[0m 0.8194  �[0m | �[0m 6.711   �[0m | �[0m 3.848   �[0m | �[0m 2.395   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.396831	0.389045	0.809569	00:07
1	0.371171	0.375065	0.818600	00:07
2	0.350309	0.371795	0.824620	00:07
3	0.359700	0.363041	0.828739	00:07
4	0.345735	0.361556	0.830799	00:07

| �[0m 60      �[0m | �[0m 0.8308  �[0m | �[0m 4.914   �[0m | �[0m 7.944   �[0m | �[0m 0.9998  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.422853	0.412691	0.804341	00:09
1	0.375209	0.394692	0.817174	00:09
2	0.365574	0.380376	0.820184	00:08
3	0.359143	0.363607	0.831115	00:08
4	0.347991	0.361650	0.827947	00:08

| �[0m 61      �[0m | �[0m 0.8279  �[0m | �[0m 7.962   �[0m | �[0m 6.151   �[0m | �[0m 1.119   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.388147	0.405885	0.810678	00:07
1	0.367743	0.391867	0.807826	00:07
2	0.366964	0.362980	0.828739	00:07
3	0.363402	0.363396	0.829531	00:07
4	0.351094	0.362245	0.829214	00:07

| �[0m 62      �[0m | �[0m 0.8292  �[0m | �[0m 2.583   �[0m | �[0m 6.996   �[0m | �[0m 0.008348�[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.432724	0.390516	0.815114	00:11
1	0.407100	0.401564	0.811153	00:11
2	0.359414	0.384463	0.820342	00:11
3	0.358061	0.371844	0.826362	00:12
4	0.345357	0.362986	0.831115	00:12

| �[0m 63      �[0m | �[0m 0.8311  �[0m | �[0m 0.2249  �[0m | �[0m 8.0     �[0m | �[0m 2.823   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.418990	0.420463	0.808618	00:07
1	0.389830	0.398110	0.816223	00:08
2	0.382975	0.387620	0.814956	00:06
3	0.384093	0.379607	0.819392	00:06
4	0.358019	0.371140	0.823828	00:08

| �[0m 64      �[0m | �[0m 0.8238  �[0m | �[0m 5.764   �[0m | �[0m 5.509   �[0m | �[0m 1.482   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.385147	0.399680	0.806242	00:09
1	0.376032	0.381131	0.822560	00:09
2	0.363870	0.378227	0.822402	00:09
3	0.351089	0.368790	0.826838	00:09
4	0.340404	0.361807	0.829214	00:09

| �[0m 65      �[0m | �[0m 0.8292  �[0m | �[0m 1.048   �[0m | �[0m 2.939   �[0m | �[0m 1.922   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.526628	0.507236	0.746673	00:17
1	0.460229	0.455675	0.765684	00:19
2	0.417427	0.421368	0.785963	00:19
3	0.462800	0.458844	0.773923	00:19
4	0.449479	0.456627	0.783587	00:19

| �[0m 66      �[0m | �[0m 0.7836  �[0m | �[0m 3.68    �[0m | �[0m 3.977   �[0m | �[0m 3.919   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.621678	0.559080	0.749049	00:10
1	0.457104	0.473610	0.758397	00:11
2	0.416287	0.416622	0.764575	00:13
3	0.388107	0.403844	0.811945	00:13
4	0.384231	0.396397	0.813055	00:13

| �[0m 67      �[0m | �[0m 0.8131  �[0m | �[0m 5.907   �[0m | �[0m 0.9452  �[0m | �[0m 2.168   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.410105	0.416171	0.808618	00:11
1	0.381669	0.400109	0.809094	00:13
2	0.377539	0.403879	0.803074	00:13
3	0.374653	0.389122	0.808618	00:13
4	0.366356	0.380526	0.814005	00:13

| �[0m 68      �[0m | �[0m 0.814   �[0m | �[0m 7.981   �[0m | �[0m 2.796   �[0m | �[0m 2.78    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.380539	0.420856	0.815589	00:07
1	0.368232	0.424276	0.803866	00:05
2	0.358194	0.378501	0.827155	00:06
3	0.353427	0.362224	0.829690	00:07
4	0.344443	0.361554	0.826838	00:07

| �[0m 69      �[0m | �[0m 0.8268  �[0m | �[0m 7.223   �[0m | �[0m 3.762   �[0m | �[0m 0.6961  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.445924	0.488581	0.752693	00:17
1	0.410709	0.400962	0.813688	00:18
2	0.373518	0.393235	0.820184	00:18
3	0.364160	0.378920	0.820817	00:17
4	0.357551	0.371629	0.825412	00:17

| �[0m 70      �[0m | �[0m 0.8254  �[0m | �[0m 0.009375�[0m | �[0m 5.081   �[0m | �[0m 3.79    �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.410323	0.442670	0.814797	00:08
1	0.379279	0.405500	0.807034	00:08
2	0.387576	0.392448	0.819708	00:09
3	0.371622	0.389167	0.823035	00:09
4	0.374182	0.386964	0.825095	00:09

| �[0m 71      �[0m | �[0m 0.8251  �[0m | �[0m 3.293   �[0m | �[0m 2.76    �[0m | �[0m 1.061   �[0m |
| �[0m 72      �[0m | �[0m 0.8308  �[0m | �[0m 4.589   �[0m | �[0m 7.13    �[0m | �[0m 0.003179�[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.391509	0.399966	0.806400	00:06
1	0.366694	0.405719	0.823828	00:07
2	0.359751	0.375496	0.822877	00:07
3	0.347678	0.361711	0.830799	00:07
4	0.336896	0.361922	0.828580	00:07

| �[0m 73      �[0m | �[0m 0.8286  �[0m | �[0m 7.118   �[0m | �[0m 5.204   �[0m | �[0m 0.5939  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.435485	0.414883	0.808143	00:08
1	0.373591	0.417138	0.814005	00:09
2	0.369590	0.375724	0.820184	00:09
3	0.370829	0.368655	0.829531	00:09
4	0.346463	0.366307	0.825412	00:09

| �[0m 74      �[0m | �[0m 0.8254  �[0m | �[0m 6.837   �[0m | �[0m 7.988   �[0m | �[0m 1.055   �[0m |
| �[0m 75      �[0m | �[0m 0.8259  �[0m | �[0m 0.6629  �[0m | �[0m 7.012   �[0m | �[0m 0.03222 �[0m |
| �[0m 76      �[0m | �[0m 0.8311  �[0m | �[0m 5.177   �[0m | �[0m 1.457   �[0m | �[0m 0.5857  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.455741	0.530008	0.794994	00:11
1	0.421961	0.423317	0.805292	00:11
2	0.405799	0.405729	0.807351	00:12
3	0.383895	0.395092	0.816857	00:12
4	0.378882	0.386044	0.818758	00:12

| �[0m 77      �[0m | �[0m 0.8188  �[0m | �[0m 3.308   �[0m | �[0m 4.533   �[0m | �[0m 2.048   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.412261	0.410950	0.798954	00:08
1	0.382314	0.408471	0.803390	00:09
2	0.347488	0.387500	0.815273	00:09
3	0.343408	0.372050	0.821451	00:09
4	0.344963	0.366158	0.822719	00:09

| �[0m 78      �[0m | �[0m 0.8227  �[0m | �[0m 0.4036  �[0m | �[0m 7.997   �[0m | �[0m 1.439   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.412856	0.410016	0.799113	00:08
1	0.410852	0.416405	0.788498	00:08
2	0.373897	0.384385	0.824303	00:09
3	0.353164	0.366129	0.822719	00:09
4	0.353253	0.362269	0.826362	00:09

| �[0m 79      �[0m | �[0m 0.8264  �[0m | �[0m 3.438   �[0m | �[0m 7.982   �[0m | �[0m 1.829   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.419316	0.408936	0.798162	00:08
1	0.393826	0.390526	0.820184	00:08
2	0.372879	0.374823	0.822719	00:08
3	0.358019	0.370913	0.820342	00:08
4	0.346020	0.362252	0.829690	00:08

| �[0m 80      �[0m | �[0m 0.8297  �[0m | �[0m 6.88    �[0m | �[0m 2.404   �[0m | �[0m 1.666   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.433481	0.437320	0.790082	00:18
1	0.415280	0.402946	0.814164	00:18
2	0.365575	0.376285	0.822877	00:18
3	0.363206	0.371865	0.820501	00:18
4	0.356401	0.370252	0.823828	00:18

| �[0m 81      �[0m | �[0m 0.8238  �[0m | �[0m 0.03221 �[0m | �[0m 1.306   �[0m | �[0m 3.909   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.393150	0.420964	0.783745	00:07
1	0.375065	0.371380	0.823986	00:07
2	0.362952	0.387037	0.813688	00:07
3	0.347245	0.370225	0.824937	00:07
4	0.348406	0.361420	0.830640	00:07

| �[0m 82      �[0m | �[0m 0.8306  �[0m | �[0m 1.575   �[0m | �[0m 2.689   �[0m | �[0m 0.8684  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.395530	0.397430	0.818600	00:06
1	0.358679	0.396773	0.818283	00:07
2	0.349305	0.372877	0.823828	00:07
3	0.347346	0.363006	0.828422	00:07
4	0.335652	0.362567	0.830957	00:07

| �[0m 83      �[0m | �[0m 0.831   �[0m | �[0m 2.765   �[0m | �[0m 5.439   �[0m | �[0m 0.04047 �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.380831	0.386072	0.814322	00:05
1	0.369778	0.392521	0.810520	00:07
2	0.368286	0.383131	0.816857	00:07
3	0.356585	0.367839	0.821768	00:07
4	0.344722	0.366639	0.825253	00:07

| �[0m 84      �[0m | �[0m 0.8253  �[0m | �[0m 0.1961  �[0m | �[0m 4.123   �[0m | �[0m 0.02039 �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.502605	0.463703	0.772180	00:11
1	0.407231	0.404386	0.804499	00:13
2	0.406849	0.411254	0.817491	00:12
3	0.379910	0.389118	0.817174	00:12
4	0.370964	0.379835	0.821293	00:12

| �[0m 85      �[0m | �[0m 0.8213  �[0m | �[0m 7.937   �[0m | �[0m 7.939   �[0m | �[0m 2.895   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.402574	0.431237	0.786755	00:11
1	0.380494	0.392253	0.817966	00:11
2	0.363284	0.393907	0.815748	00:11
3	0.355680	0.368488	0.822560	00:12
4	0.362978	0.367755	0.823511	00:12

| �[0m 86      �[0m | �[0m 0.8235  �[0m | �[0m 0.06921 �[0m | �[0m 5.7     �[0m | �[0m 2.778   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.448572	0.451255	0.789766	00:10
1	0.417093	0.411838	0.808143	00:11
2	0.400799	0.400185	0.816223	00:12
3	0.378641	0.385082	0.820342	00:12
4	0.365278	0.380320	0.818441	00:11

| �[0m 87      �[0m | �[0m 0.8184  �[0m | �[0m 7.965   �[0m | �[0m 5.261   �[0m | �[0m 2.661   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.399453	0.429288	0.783112	00:07
1	0.368987	0.376985	0.825729	00:07
2	0.358226	0.372103	0.830165	00:07
3	0.348940	0.362069	0.831115	00:07
4	0.341437	0.361470	0.830482	00:07

| �[0m 88      �[0m | �[0m 0.8305  �[0m | �[0m 2.792   �[0m | �[0m 7.917   �[0m | �[0m 0.761   �[0m |
| �[0m 89      �[0m | �[0m 0.8294  �[0m | �[0m 7.995   �[0m | �[0m 7.186   �[0m | �[0m 0.1199  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.390914	0.403780	0.799905	00:07
1	0.375241	0.400406	0.821610	00:07
2	0.359710	0.373233	0.826046	00:07
3	0.351108	0.367255	0.823986	00:07
4	0.348230	0.362827	0.830482	00:07

| �[0m 90      �[0m | �[0m 0.8305  �[0m | �[0m 2.526   �[0m | �[0m 3.741   �[0m | �[0m 0.1186  �[0m |
| �[0m 91      �[0m | �[0m 0.8294  �[0m | �[0m 0.03285 �[0m | �[0m 5.742   �[0m | �[0m 0.9747  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.384485	0.413711	0.816857	00:06
1	0.358383	0.382125	0.813055	00:06
2	0.349632	0.380644	0.825570	00:07
3	0.350005	0.363857	0.833492	00:07
4	0.341342	0.362533	0.829848	00:07

| �[0m 92      �[0m | �[0m 0.8298  �[0m | �[0m 4.112e-0�[0m | �[0m 8.0     �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.461806	0.454314	0.752218	00:11
1	0.429429	0.452721	0.768061	00:13
2	0.408792	0.422315	0.785013	00:12
3	0.400290	0.411134	0.805292	00:12
4	0.396974	0.409337	0.804658	00:12

| �[0m 93      �[0m | �[0m 0.8047  �[0m | �[0m 4.563   �[0m | �[0m 0.6868  �[0m | �[0m 2.461   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.464920	0.440214	0.772655	00:11
1	0.401611	0.400240	0.805767	00:11
2	0.391905	0.414512	0.798638	00:13
3	0.391561	0.407271	0.805608	00:13
4	0.387596	0.397586	0.814639	00:13

| �[0m 94      �[0m | �[0m 0.8146  �[0m | �[0m 4.697   �[0m | �[0m 3.412   �[0m | �[0m 2.514   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.417917	0.423588	0.755070	00:05
1	0.390158	0.396916	0.817649	00:07
2	0.374562	0.389764	0.828422	00:07
3	0.368317	0.385268	0.822719	00:07
4	0.373367	0.385092	0.827471	00:07

| �[0m 95      �[0m | �[0m 0.8275  �[0m | �[0m 5.04    �[0m | �[0m 0.4492  �[0m | �[0m 0.5899  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.368991	0.388251	0.822402	00:07
1	0.363111	0.399670	0.814797	00:08
2	0.370390	0.377726	0.818441	00:10
3	0.353493	0.368603	0.823828	00:10
4	0.355493	0.368281	0.823669	00:09

| �[0m 96      �[0m | �[0m 0.8237  �[0m | �[0m 0.6025  �[0m | �[0m 2.712   �[0m | �[0m 1.166   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.426161	0.417567	0.793726	00:07
1	0.397633	0.392852	0.818124	00:08
2	0.371073	0.371678	0.821768	00:09
3	0.351716	0.361607	0.831749	00:10
4	0.347291	0.359881	0.832066	00:09

| �[95m 97      �[0m | �[95m 0.8321  �[0m | �[95m 6.389   �[0m | �[95m 3.648   �[0m | �[95m 1.016   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.408937	0.440755	0.785805	00:12
1	0.369800	0.379622	0.819867	00:12
2	0.356048	0.370119	0.827630	00:11
3	0.354271	0.366091	0.826362	00:11
4	0.359324	0.366020	0.827313	00:12

| �[0m 98      �[0m | �[0m 0.8273  �[0m | �[0m 0.5927  �[0m | �[0m 1.715   �[0m | �[0m 2.847   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.443831	0.469775	0.753802	00:11
1	0.411636	0.423362	0.787864	00:11
2	0.411669	0.421874	0.797529	00:13
3	0.392242	0.395377	0.816382	00:12
4	0.383947	0.390270	0.818441	00:12

| �[0m 99      �[0m | �[0m 0.8184  �[0m | �[0m 6.602   �[0m | �[0m 2.266   �[0m | �[0m 2.535   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.413688	0.469193	0.811153	00:08
1	0.383908	0.401520	0.815589	00:08
2	0.363908	0.375178	0.824937	00:08
3	0.360694	0.366536	0.828739	00:08
4	0.341851	0.362886	0.830165	00:08

| �[0m 100     �[0m | �[0m 0.8302  �[0m | �[0m 3.099   �[0m | �[0m 6.058   �[0m | �[0m 1.058   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.404324	0.402310	0.807668	00:09
1	0.383628	0.428126	0.780577	00:09
2	0.360917	0.375198	0.826996	00:09
3	0.349922	0.367114	0.828264	00:09
4	0.338715	0.363762	0.830165	00:09

| �[0m 101     �[0m | �[0m 0.8302  �[0m | �[0m 4.268   �[0m | �[0m 5.28    �[0m | �[0m 1.397   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.381317	0.410546	0.815431	00:06
1	0.355632	0.424577	0.802440	00:06
2	0.361193	0.377675	0.818124	00:07
3	0.355751	0.363205	0.827313	00:07
4	0.338802	0.362403	0.827471	00:07

| �[0m 102     �[0m | �[0m 0.8275  �[0m | �[0m 4.735   �[0m | �[0m 3.398   �[0m | �[0m 0.01904 �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.381150	0.398499	0.813213	00:07
1	0.370997	0.413040	0.798162	00:07
2	0.364154	0.369066	0.823352	00:07
3	0.354434	0.362925	0.825095	00:07
4	0.348736	0.362125	0.824461	00:07

| �[0m 103     �[0m | �[0m 0.8245  �[0m | �[0m 0.0     �[0m | �[0m 6.371   �[0m | �[0m 9.721e-0�[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.388378	0.412839	0.810361	00:06
1	0.377533	0.380432	0.822560	00:06
2	0.356242	0.372250	0.824937	00:07
3	0.346274	0.364236	0.830323	00:07
4	0.349273	0.362597	0.830482	00:07

| �[0m 104     �[0m | �[0m 0.8305  �[0m | �[0m 1.683   �[0m | �[0m 6.381   �[0m | �[0m 0.8564  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.410286	0.413506	0.801648	00:07
1	0.386787	0.397328	0.812262	00:07
2	0.365268	0.390419	0.825570	00:07
3	0.368566	0.386955	0.828264	00:07
4	0.362751	0.383124	0.830482	00:07

| �[0m 105     �[0m | �[0m 0.8305  �[0m | �[0m 6.387   �[0m | �[0m 2.333   �[0m | �[0m 0.4877  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.432347	0.450028	0.780894	00:17
1	0.414766	0.402565	0.809569	00:17
2	0.382495	0.382281	0.812421	00:18
3	0.366852	0.373158	0.822085	00:18
4	0.353692	0.368471	0.824461	00:18

| �[0m 106     �[0m | �[0m 0.8245  �[0m | �[0m 0.9452  �[0m | �[0m 6.902   �[0m | �[0m 3.991   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.397604	0.407966	0.814797	00:06
1	0.386755	0.382698	0.800063	00:07
2	0.361672	0.373610	0.823194	00:07
3	0.345655	0.363278	0.829848	00:07
4	0.349931	0.362171	0.830957	00:07

| �[0m 107     �[0m | �[0m 0.831   �[0m | �[0m 5.475   �[0m | �[0m 5.721   �[0m | �[0m 0.02659 �[0m |
| �[0m 108     �[0m | �[0m 0.8308  �[0m | �[0m 4.597   �[0m | �[0m 7.994   �[0m | �[0m 0.1318  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.391720	0.395161	0.819550	00:06
1	0.372859	0.387859	0.810203	00:07
2	0.367385	0.376952	0.812262	00:07
3	0.346810	0.365312	0.827155	00:07
4	0.341221	0.363528	0.829056	00:07

| �[0m 109     �[0m | �[0m 0.8291  �[0m | �[0m 2.391   �[0m | �[0m 4.915   �[0m | �[0m 0.8695  �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.391635	0.402138	0.804658	00:08
1	0.402425	0.468013	0.803866	00:09
2	0.368443	0.372927	0.823669	00:09
3	0.361579	0.364913	0.826679	00:09
4	0.347989	0.363416	0.828580	00:09

| �[0m 110     �[0m | �[0m 0.8286  �[0m | �[0m 5.711   �[0m | �[0m 7.031   �[0m | �[0m 1.157   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.393225	0.414461	0.812738	00:07
1	0.377641	0.381910	0.820659	00:07
2	0.363979	0.375042	0.826838	00:07
3	0.349867	0.363547	0.830165	00:07
4	0.350035	0.364254	0.830006	00:07

| �[0m 111     �[0m | �[0m 0.83    �[0m | �[0m 3.867   �[0m | �[0m 7.351   �[0m | �[0m 0.7373  �[0m |
| �[0m 112     �[0m | �[0m 0.8275  �[0m | �[0m 5.568   �[0m | �[0m 0.8565  �[0m | �[0m 0.9522  �[0m |
| �[0m 113     �[0m | �[0m 0.8311  �[0m | �[0m 5.553   �[0m | �[0m 1.45    �[0m | �[0m 0.0     �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.404215	0.421293	0.817807	00:10
1	0.380753	0.388533	0.803074	00:09
2	0.362475	0.381199	0.820342	00:09
3	0.353416	0.368431	0.824620	00:09
4	0.335854	0.365448	0.825570	00:09

| �[0m 114     �[0m | �[0m 0.8256  �[0m | �[0m 3.949   �[0m | �[0m 1.125   �[0m | �[0m 1.109   �[0m |

epoch	train_loss	valid_loss	accuracy	time
0	0.397351	0.482881	0.766160	00:08
1	0.373190	0.372999	0.819075	00:08
2	0.354075	0.368455	0.826046	00:08
3	0.350617	0.362793	0.830323	00:08
4	0.347381	0.361980	0.831274	00:08

| �[0m 115     �[0m | �[0m 0.8313  �[0m | �[0m 7.258   �[0m | �[0m 4.724   �[0m | �[0m 1.639   �[0m |
=============================================================

optimizer.max['target']

0.8428390622138977

{key: 2**int(value)
  for key, value in optimizer.max['params'].items()}

{'pow_n_a': 2, 'pow_n_d': 64, 'pow_n_steps': 1}

Out of memory dataset

If your dataset is so big it doesn't fit in memory, you can load a chunk of it each epoch.

df = pd.read_csv(path/'adult.csv')
df_main,df_valid = df.iloc[:-1000].copy(),df.iloc[-1000:].copy()

# choose size that fit in memory
dataset_size = 1000

# load chunk with your own code
def load_chunk():
    return df_main.sample(dataset_size).copy()

df_small = load_chunk()
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 
             'relationship', 'race', 'native-country', 'sex']
cont_names = ['age', 'fnlwgt', 'education-num']
procs = [Categorify, FillMissing, Normalize]
splits = RandomSplitter()(range_of(df_small))

to = TabularPandas(df_small, procs, cat_names, cont_names, y_names="salary", y_block = CategoryBlock(), 
                   splits=None, do_setup=True)

# save the validation set
to_valid = to.new(df_valid)
to_valid.process()
val_dl = TabDataLoader(to_valid.train)

len(to.train)

class ReloadCallback(Callback):
    def begin_epoch(self): 
        df_small = load_chunk()
        to_new = to.new(df_small)
        to_new.process()
        trn_dl = TabDataLoader(to_new.train)
        self.learn.dls = DataLoaders(trn_dl, val_dl).cuda()

dls = to.dataloaders()
emb_szs = get_emb_sz(to)
model = TabNetModel(emb_szs, len(to.cont_names), dls.c, n_d=8, n_a=32, n_steps=1);

opt_func = partial(Adam, wd=0.01, eps=1e-5)
learn = Learner(dls, model, CrossEntropyLossFlat(), opt_func=opt_func, lr=3e-2, metrics=[accuracy])
learn.add_cb(ReloadCallback());

learn.fit_one_cycle(10)

epoch	train_loss	valid_loss	accuracy	time
0	0.587740	0.550544	0.756000	00:01
1	0.545411	0.515772	0.782000	00:01
2	0.484289	0.468586	0.813000	00:01
3	0.447111	0.435774	0.817000	00:01
4	0.449050	0.394715	0.819000	00:01
5	0.428863	0.382005	0.835000	00:01
6	0.382100	0.404258	0.826000	00:01
7	0.383915	0.376179	0.833000	00:01
8	0.389460	0.367857	0.834000	00:01
9	0.376486	0.367577	0.834000	00:01

TabNet for fastai

Related tags

Overview

TabNet for fastai

Install

How to use

Example

Tabnet interpretability

Hyperparameter search with Bayesian Optimization

Out of memory dataset

Owner

Mikhail Grankin

This repo generates the training data and the model for Morpheus-Deblend

Denoising Diffusion Implicit Models

A PyTorch-based Semi-Supervised Learning (SSL) Codebase for Pixel-wise (Pixel) Vision Tasks

The official PyTorch implementation of the paper: Xili Dai, Xiaojun Yuan, Haigang Gong, Yi Ma. "Fully Convolutional Line Parsing." .

Stock-Prediction - prediction of stock market movements using sentiment analysis and deep learning.

GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)

Dense Unsupervised Learning for Video Segmentation (NeurIPS*2021)

Code for the paper "Implicit Representations of Meaning in Neural Language Models"

AAAI 2022: Stationary diffusion state neural estimation

Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data

This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning

[CoRL 2021] A robotics benchmark for cross-embodiment imitation.

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

Code for: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Discord bot for notifying on github events

Code for Environment Dynamics Decomposition (ED2).

Pydantic models for pywttr and aiopywttr.

LSTM-VAE Implementation and Relevant Evaluations

TabNet for fastai

Related tags

Overview

TabNet for fastai

Install

How to use

Example

Tabnet interpretability

Hyperparameter search with Bayesian Optimization

Out of memory dataset

Owner

Mikhail Grankin

This repo generates the training data and the model for Morpheus-Deblend

Denoising Diffusion Implicit Models

A PyTorch-based Semi-Supervised Learning (SSL) Codebase for Pixel-wise (Pixel) Vision Tasks

The official PyTorch implementation of the paper: *Xili Dai, Xiaojun Yuan, Haigang Gong, Yi Ma. "Fully Convolutional Line Parsing." *.

Stock-Prediction - prediction of stock market movements using sentiment analysis and deep learning.

GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)

Dense Unsupervised Learning for Video Segmentation (NeurIPS*2021)

Code for the paper "Implicit Representations of Meaning in Neural Language Models"

AAAI 2022: Stationary diffusion state neural estimation

Repository for the paper : Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data

This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning

[CoRL 2021] A robotics benchmark for cross-embodiment imitation.

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

Code for: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Discord bot for notifying on github events

Code for Environment Dynamics Decomposition (ED2).

Pydantic models for pywttr and aiopywttr.

LSTM-VAE Implementation and Relevant Evaluations

The official PyTorch implementation of the paper: Xili Dai, Xiaojun Yuan, Haigang Gong, Yi Ma. "Fully Convolutional Line Parsing." .