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mprostock/earthquakes_tabular.ipynb Secret

Created November 21, 2018 16:12
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Tabular learner baseline for earthquakes
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earthquakes_tabular.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.0.28'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fastai import *\n",
"from fastai.tabular import *\n",
"import fastai\n",
"\n",
"import zipfile\n",
"import tempfile\n",
"from urllib.request import urlretrieve\n",
"from zipfile import BadZipFile as BadZipFile\n",
"\n",
"fastai.__version__"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def extract_from_zip_url(url, target_dir=None, verbose=False):\n",
" \"\"\"Download a zip file from its URL and unzip it.\n",
" code by mb4310\n",
" \"\"\"\n",
" fname = os.path.basename(url)\n",
" tmpdir = tempfile.mkdtemp()\n",
" local_zip_fname = os.path.join(tmpdir, fname)\n",
" urlretrieve(url, local_zip_fname)\n",
" try:\n",
" if not os.path.exists(target_dir):\n",
" os.makedirs(target_dir)\n",
" zipfile.ZipFile(local_zip_fname, \"r\").extractall(path=target_dir)\n",
" shutil.rmtree(tmpdir)\n",
" if verbose:\n",
" print(\"Successfully extracted file %s to path %s\" % (local_zip_fname, target_dir))\n",
" return target_dir\n",
" except BadZipFile:\n",
" shutil.rmtree(tmpdir)\n",
" if verbose:\n",
" sys.stderr.write(\"Corrupted zip file encountered, aborting.\\n\")\n",
" return None\n",
"\n",
"def get_dataset(dataset_name, full_path):\n",
" '''\n",
" Download selected UCR dataset, unzip files to target dir, \n",
" function adapted based on mb4310's code\n",
" '''\n",
"\n",
" #full_path = os.path.join(target_dir, dataset_name)\n",
" fname_train = dataset_name + \"_TRAIN.txt\"\n",
" fname_test = dataset_name + \"_TEST.txt\"\n",
" if not os.path.exists(os.path.join(full_path, fname_train)) or \\\n",
" not os.path.exists(os.path.join(full_path, fname_test)):\n",
" url = f\"http://www.timeseriesclassification.com/Downloads/{dataset_name}.zip\"\n",
" for fname in [fname_train, fname_test]:\n",
" if os.path.exists(os.path.join(full_path, fname)):\n",
" os.remove(os.path.join(full_path, fname))\n",
" extract_from_zip_url(url, target_dir=full_path, verbose=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"DATA_DIR = '/Data/Code/datasets/TSC/UCR_univariate/Test/'\n",
"SEL_DATASET = 'Earthquakes'\n",
"TGT_DIR = Path(DATA_DIR + SEL_DATASET)\n",
"trainfile = TGT_DIR/f'{SEL_DATASET}_TRAIN.txt'\n",
"testfile = TGT_DIR/f'{SEL_DATASET}_TEST.txt'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"get_dataset(SEL_DATASET, TGT_DIR)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(139, 513)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mpr/anaconda3/envs/fv1/lib/python3.6/site-packages/ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" \"\"\"Entry point for launching an IPython kernel.\n",
"/home/mpr/anaconda3/envs/fv1/lib/python3.6/site-packages/ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
" \n"
]
}
],
"source": [
"earthqs = pd.read_csv(trainfile, sep=' ', header=None)\n",
"earthqstest = pd.read_csv(testfile, sep=' ', header=None)\n",
"print(earthqstest.shape)\n",
"quakes = pd.concat([earthqs, earthqstest])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"quakes.columns = list(map(str, earthqs.columns)) # convert columns to str because fastai cannot work with int columns!\n",
"quakes['0'] = quakes['0'].astype('int').astype(str)\n",
"quakes.reset_index(drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"procs = [Normalize]\n",
"valid_idx = quakes[-139:].index\n",
"data = (TabularList.from_df(quakes, path=TGT_DIR, cont_names=quakes.columns[1:], procs=procs) # , cat_names=cat_names, cont_names=cont_names, procs=procs)\n",
" .split_by_idx(valid_idx)\n",
" .label_from_df(cols=['0'])\n",
" .databunch(bs=8))"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
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" <th>198</th>\n",
" <th>199</th>\n",
" <th>200</th>\n",
" <th>201</th>\n",
" <th>202</th>\n",
" <th>203</th>\n",
" <th>204</th>\n",
" <th>205</th>\n",
" <th>206</th>\n",
" <th>207</th>\n",
" <th>208</th>\n",
" <th>209</th>\n",
" <th>210</th>\n",
" <th>211</th>\n",
" <th>212</th>\n",
" <th>213</th>\n",
" <th>214</th>\n",
" <th>215</th>\n",
" <th>216</th>\n",
" <th>217</th>\n",
" <th>218</th>\n",
" <th>219</th>\n",
" <th>220</th>\n",
" <th>221</th>\n",
" <th>222</th>\n",
" <th>223</th>\n",
" <th>224</th>\n",
" <th>225</th>\n",
" <th>226</th>\n",
" <th>227</th>\n",
" <th>228</th>\n",
" <th>229</th>\n",
" <th>230</th>\n",
" <th>231</th>\n",
" <th>232</th>\n",
" <th>233</th>\n",
" <th>234</th>\n",
" <th>235</th>\n",
" <th>236</th>\n",
" <th>237</th>\n",
" <th>238</th>\n",
" <th>239</th>\n",
" <th>240</th>\n",
" <th>241</th>\n",
" <th>242</th>\n",
" <th>243</th>\n",
" <th>244</th>\n",
" <th>245</th>\n",
" <th>246</th>\n",
" <th>247</th>\n",
" <th>248</th>\n",
" <th>249</th>\n",
" <th>250</th>\n",
" <th>251</th>\n",
" <th>252</th>\n",
" <th>253</th>\n",
" <th>254</th>\n",
" <th>255</th>\n",
" <th>256</th>\n",
" <th>257</th>\n",
" <th>258</th>\n",
" <th>259</th>\n",
" <th>260</th>\n",
" <th>261</th>\n",
" <th>262</th>\n",
" <th>263</th>\n",
" <th>264</th>\n",
" <th>265</th>\n",
" <th>266</th>\n",
" <th>267</th>\n",
" <th>268</th>\n",
" <th>269</th>\n",
" <th>270</th>\n",
" <th>271</th>\n",
" <th>272</th>\n",
" <th>273</th>\n",
" <th>274</th>\n",
" <th>275</th>\n",
" <th>276</th>\n",
" <th>277</th>\n",
" <th>278</th>\n",
" <th>279</th>\n",
" <th>280</th>\n",
" <th>281</th>\n",
" <th>282</th>\n",
" <th>283</th>\n",
" <th>284</th>\n",
" <th>285</th>\n",
" <th>286</th>\n",
" <th>287</th>\n",
" <th>288</th>\n",
" <th>289</th>\n",
" <th>290</th>\n",
" <th>291</th>\n",
" <th>292</th>\n",
" <th>293</th>\n",
" <th>294</th>\n",
" <th>295</th>\n",
" <th>296</th>\n",
" <th>297</th>\n",
" <th>298</th>\n",
" <th>299</th>\n",
" <th>300</th>\n",
" <th>301</th>\n",
" <th>302</th>\n",
" <th>303</th>\n",
" <th>304</th>\n",
" <th>305</th>\n",
" <th>306</th>\n",
" <th>307</th>\n",
" <th>308</th>\n",
" <th>309</th>\n",
" <th>310</th>\n",
" <th>311</th>\n",
" <th>312</th>\n",
" <th>313</th>\n",
" <th>314</th>\n",
" <th>315</th>\n",
" <th>316</th>\n",
" <th>317</th>\n",
" <th>318</th>\n",
" <th>319</th>\n",
" <th>320</th>\n",
" <th>321</th>\n",
" <th>322</th>\n",
" <th>323</th>\n",
" <th>324</th>\n",
" <th>325</th>\n",
" <th>326</th>\n",
" <th>327</th>\n",
" <th>328</th>\n",
" <th>329</th>\n",
" <th>330</th>\n",
" <th>331</th>\n",
" <th>332</th>\n",
" <th>333</th>\n",
" <th>334</th>\n",
" <th>335</th>\n",
" <th>336</th>\n",
" <th>337</th>\n",
" <th>338</th>\n",
" <th>339</th>\n",
" <th>340</th>\n",
" <th>341</th>\n",
" <th>342</th>\n",
" <th>343</th>\n",
" <th>344</th>\n",
" <th>345</th>\n",
" <th>346</th>\n",
" <th>347</th>\n",
" <th>348</th>\n",
" <th>349</th>\n",
" <th>350</th>\n",
" <th>351</th>\n",
" <th>352</th>\n",
" <th>353</th>\n",
" <th>354</th>\n",
" <th>355</th>\n",
" <th>356</th>\n",
" <th>357</th>\n",
" <th>358</th>\n",
" <th>359</th>\n",
" <th>360</th>\n",
" <th>361</th>\n",
" <th>362</th>\n",
" <th>363</th>\n",
" <th>364</th>\n",
" <th>365</th>\n",
" <th>366</th>\n",
" <th>367</th>\n",
" <th>368</th>\n",
" <th>369</th>\n",
" <th>370</th>\n",
" <th>371</th>\n",
" <th>372</th>\n",
" <th>373</th>\n",
" <th>374</th>\n",
" <th>375</th>\n",
" <th>376</th>\n",
" <th>377</th>\n",
" <th>378</th>\n",
" <th>379</th>\n",
" <th>380</th>\n",
" <th>381</th>\n",
" <th>382</th>\n",
" <th>383</th>\n",
" <th>384</th>\n",
" <th>385</th>\n",
" <th>386</th>\n",
" <th>387</th>\n",
" <th>388</th>\n",
" <th>389</th>\n",
" <th>390</th>\n",
" <th>391</th>\n",
" <th>392</th>\n",
" <th>393</th>\n",
" <th>394</th>\n",
" <th>395</th>\n",
" <th>396</th>\n",
" <th>397</th>\n",
" <th>398</th>\n",
" <th>399</th>\n",
" <th>400</th>\n",
" <th>401</th>\n",
" <th>402</th>\n",
" <th>403</th>\n",
" <th>404</th>\n",
" <th>405</th>\n",
" <th>406</th>\n",
" <th>407</th>\n",
" <th>408</th>\n",
" <th>409</th>\n",
" <th>410</th>\n",
" <th>411</th>\n",
" <th>412</th>\n",
" <th>413</th>\n",
" <th>414</th>\n",
" <th>415</th>\n",
" <th>416</th>\n",
" <th>417</th>\n",
" <th>418</th>\n",
" <th>419</th>\n",
" <th>420</th>\n",
" <th>421</th>\n",
" <th>422</th>\n",
" <th>423</th>\n",
" <th>424</th>\n",
" <th>425</th>\n",
" <th>426</th>\n",
" <th>427</th>\n",
" <th>428</th>\n",
" <th>429</th>\n",
" <th>430</th>\n",
" <th>431</th>\n",
" <th>432</th>\n",
" <th>433</th>\n",
" <th>434</th>\n",
" <th>435</th>\n",
" <th>436</th>\n",
" <th>437</th>\n",
" <th>438</th>\n",
" <th>439</th>\n",
" <th>440</th>\n",
" <th>441</th>\n",
" <th>442</th>\n",
" <th>443</th>\n",
" <th>444</th>\n",
" <th>445</th>\n",
" <th>446</th>\n",
" <th>447</th>\n",
" <th>448</th>\n",
" <th>449</th>\n",
" <th>450</th>\n",
" <th>451</th>\n",
" <th>452</th>\n",
" <th>453</th>\n",
" <th>454</th>\n",
" <th>455</th>\n",
" <th>456</th>\n",
" <th>457</th>\n",
" <th>458</th>\n",
" <th>459</th>\n",
" <th>460</th>\n",
" <th>461</th>\n",
" <th>462</th>\n",
" <th>463</th>\n",
" <th>464</th>\n",
" <th>465</th>\n",
" <th>466</th>\n",
" <th>467</th>\n",
" <th>468</th>\n",
" <th>469</th>\n",
" <th>470</th>\n",
" <th>471</th>\n",
" <th>472</th>\n",
" <th>473</th>\n",
" <th>474</th>\n",
" <th>475</th>\n",
" <th>476</th>\n",
" <th>477</th>\n",
" <th>478</th>\n",
" <th>479</th>\n",
" <th>480</th>\n",
" <th>481</th>\n",
" <th>482</th>\n",
" <th>483</th>\n",
" <th>484</th>\n",
" <th>485</th>\n",
" <th>486</th>\n",
" <th>487</th>\n",
" <th>488</th>\n",
" <th>489</th>\n",
" <th>490</th>\n",
" <th>491</th>\n",
" <th>492</th>\n",
" <th>493</th>\n",
" <th>494</th>\n",
" <th>495</th>\n",
" <th>496</th>\n",
" <th>497</th>\n",
" <th>498</th>\n",
" <th>499</th>\n",
" <th>500</th>\n",
" <th>501</th>\n",
" <th>502</th>\n",
" <th>503</th>\n",
" <th>504</th>\n",
" <th>505</th>\n",
" <th>506</th>\n",
" <th>507</th>\n",
" <th>508</th>\n",
" <th>509</th>\n",
" <th>510</th>\n",
" <th>511</th>\n",
" <th>512</th>\n",
" <th>target</th>\n",
" </tr>\n",
" <tr>\n",
" <th>-0.5545</th>\n",
" <th>-0.5413</th>\n",
" <th>1.7624</th>\n",
" <th>-0.5674</th>\n",
" <th>-0.5593</th>\n",
" <th>-0.5457</th>\n",
" <th>2.2369</th>\n",
" <th>-0.5241</th>\n",
" <th>1.9235</th>\n",
" <th>-0.5065</th>\n",
" <th>-0.5116</th>\n",
" <th>-0.5920</th>\n",
" <th>-0.5896</th>\n",
" <th>-0.5168</th>\n",
" <th>-0.5423</th>\n",
" <th>-0.5231</th>\n",
" <th>1.2754</th>\n",
" <th>-0.5547</th>\n",
" <th>-0.4891</th>\n",
" <th>-0.5439</th>\n",
" <th>-0.5288</th>\n",
" <th>-0.5191</th>\n",
" <th>-0.5585</th>\n",
" <th>-0.5851</th>\n",
" <th>-0.5371</th>\n",
" <th>-0.5412</th>\n",
" <th>-0.5774</th>\n",
" <th>1.6932</th>\n",
" <th>-0.5453</th>\n",
" <th>1.5766</th>\n",
" <th>-0.5544</th>\n",
" <th>-0.4945</th>\n",
" <th>-0.5306</th>\n",
" <th>-0.5736</th>\n",
" <th>-0.5571</th>\n",
" <th>-0.5828</th>\n",
" <th>-0.5809</th>\n",
" <th>-0.5328</th>\n",
" <th>-0.5658</th>\n",
" <th>-0.5213</th>\n",
" <th>-0.5494</th>\n",
" <th>1.5264</th>\n",
" <th>-0.5416</th>\n",
" <th>-0.5480</th>\n",
" <th>-0.5727</th>\n",
" <th>1.9417</th>\n",
" <th>-0.5551</th>\n",
" <th>-0.5701</th>\n",
" <th>-0.5142</th>\n",
" <th>1.5119</th>\n",
" <th>-0.5465</th>\n",
" <th>3.4043</th>\n",
" <th>-0.5178</th>\n",
" <th>-0.5369</th>\n",
" <th>1.5279</th>\n",
" <th>-0.5547</th>\n",
" <th>1.1433</th>\n",
" <th>-0.5767</th>\n",
" <th>-0.4985</th>\n",
" <th>1.6933</th>\n",
" <th>1.2497</th>\n",
" <th>-0.5351</th>\n",
" <th>1.9322</th>\n",
" <th>-0.5933</th>\n",
" <th>1.6911</th>\n",
" <th>1.2764</th>\n",
" <th>-0.5673</th>\n",
" <th>-0.5667</th>\n",
" <th>-0.5387</th>\n",
" <th>1.2209</th>\n",
" <th>1.4256</th>\n",
" <th>-0.4870</th>\n",
" <th>-0.5546</th>\n",
" <th>-0.5171</th>\n",
" <th>-0.4726</th>\n",
" <th>-0.5340</th>\n",
" <th>-0.6279</th>\n",
" <th>-0.5017</th>\n",
" <th>-0.5405</th>\n",
" <th>-0.4821</th>\n",
" <th>2.0137</th>\n",
" <th>-0.5451</th>\n",
" <th>-0.5241</th>\n",
" <th>-0.5301</th>\n",
" <th>-0.4900</th>\n",
" <th>-0.5537</th>\n",
" <th>-0.5696</th>\n",
" <th>1.5910</th>\n",
" <th>-0.5609</th>\n",
" <th>-0.5302</th>\n",
" <th>-0.5261</th>\n",
" <th>-0.5293</th>\n",
" <th>-0.5331</th>\n",
" <th>-0.5473</th>\n",
" <th>-0.5718</th>\n",
" <th>1.5134</th>\n",
" <th>-0.5187</th>\n",
" <th>-0.5764</th>\n",
" <th>-0.5657</th>\n",
" <th>-0.5152</th>\n",
" <th>-0.5171</th>\n",
" <th>-0.5039</th>\n",
" <th>-0.4950</th>\n",
" <th>-0.5181</th>\n",
" <th>-0.5719</th>\n",
" <th>1.6136</th>\n",
" <th>-0.5521</th>\n",
" <th>-0.5579</th>\n",
" <th>1.0197</th>\n",
" <th>-0.5695</th>\n",
" <th>-0.5661</th>\n",
" <th>-0.5467</th>\n",
" <th>-0.5779</th>\n",
" <th>-0.5553</th>\n",
" <th>-0.5198</th>\n",
" <th>-0.5307</th>\n",
" <th>-0.5308</th>\n",
" <th>-0.5853</th>\n",
" <th>-0.4971</th>\n",
" <th>-0.5590</th>\n",
" <th>-0.5196</th>\n",
" <th>-0.5502</th>\n",
" <th>2.1490</th>\n",
" <th>-0.5356</th>\n",
" <th>-0.5790</th>\n",
" <th>-0.5494</th>\n",
" <th>1.3298</th>\n",
" <th>-0.5762</th>\n",
" <th>-0.4583</th>\n",
" <th>2.6994</th>\n",
" <th>-0.4971</th>\n",
" <th>1.8084</th>\n",
" <th>1.7526</th>\n",
" <th>-0.5714</th>\n",
" <th>1.9921</th>\n",
" <th>-0.5778</th>\n",
" <th>2.2511</th>\n",
" <th>1.6662</th>\n",
" <th>-0.5647</th>\n",
" <th>-0.5234</th>\n",
" <th>-0.5420</th>\n",
" <th>-0.5810</th>\n",
" <th>-0.5462</th>\n",
" <th>-0.5491</th>\n",
" <th>-0.5773</th>\n",
" <th>1.2345</th>\n",
" <th>-0.5274</th>\n",
" <th>-0.6001</th>\n",
" <th>-0.5392</th>\n",
" <th>-0.5578</th>\n",
" <th>-0.5184</th>\n",
" <th>-0.5324</th>\n",
" <th>-0.5744</th>\n",
" <th>-0.5026</th>\n",
" <th>-0.5413</th>\n",
" <th>2.0695</th>\n",
" <th>1.9830</th>\n",
" <th>-0.4700</th>\n",
" <th>-0.5082</th>\n",
" <th>-0.5526</th>\n",
" <th>-0.5575</th>\n",
" <th>-0.5394</th>\n",
" <th>-0.5769</th>\n",
" <th>-0.5232</th>\n",
" <th>-0.6097</th>\n",
" <th>-0.5820</th>\n",
" <th>-0.5428</th>\n",
" <th>-0.4883</th>\n",
" <th>1.2579</th>\n",
" <th>-0.5392</th>\n",
" <th>1.3690</th>\n",
" <th>1.3578</th>\n",
" <th>-0.5735</th>\n",
" <th>-0.5882</th>\n",
" <th>-0.5636</th>\n",
" <th>2.4140</th>\n",
" <th>1.6207</th>\n",
" <th>1.3876</th>\n",
" <th>-0.5650</th>\n",
" <th>-0.5348</th>\n",
" <th>1.9976</th>\n",
" <th>2.0986</th>\n",
" <th>-0.5475</th>\n",
" <th>2.4948</th>\n",
" <th>3.5307</th>\n",
" <th>2.1783</th>\n",
" <th>-0.5160</th>\n",
" <th>1.6857</th>\n",
" <th>2.6295</th>\n",
" <th>2.0779</th>\n",
" <th>2.2049</th>\n",
" <th>1.2599</th>\n",
" <th>-0.5419</th>\n",
" <th>1.4337</th>\n",
" <th>-0.4775</th>\n",
" <th>-0.5491</th>\n",
" <th>-0.5362</th>\n",
" <th>-0.4961</th>\n",
" <th>-0.5048</th>\n",
" <th>-0.5354</th>\n",
" <th>2.2765</th>\n",
" <th>1.6356</th>\n",
" <th>1.3280</th>\n",
" <th>-0.5452</th>\n",
" <th>-0.5301</th>\n",
" <th>-0.4994</th>\n",
" <th>-0.5670</th>\n",
" <th>-0.5023</th>\n",
" <th>-0.5319</th>\n",
" <th>1.2718</th>\n",
" <th>1.5321</th>\n",
" <th>1.4058</th>\n",
" <th>-0.5407</th>\n",
" <th>-0.5797</th>\n",
" <th>-0.5508</th>\n",
" <th>-0.5622</th>\n",
" <th>1.9850</th>\n",
" <th>-0.5594</th>\n",
" <th>-0.5378</th>\n",
" <th>-0.6014</th>\n",
" <th>-0.5545</th>\n",
" <th>-0.5313</th>\n",
" <th>-0.5027</th>\n",
" <th>-0.5498</th>\n",
" <th>-0.5602</th>\n",
" <th>1.9331</th>\n",
" <th>2.0256</th>\n",
" <th>2.4851</th>\n",
" <th>1.6961</th>\n",
" <th>1.5003</th>\n",
" <th>1.6683</th>\n",
" <th>1.4984</th>\n",
" <th>-0.5499</th>\n",
" <th>-0.5813</th>\n",
" <th>-0.5594</th>\n",
" <th>1.1986</th>\n",
" <th>-0.5265</th>\n",
" <th>1.4468</th>\n",
" <th>1.4526</th>\n",
" <th>-0.5164</th>\n",
" <th>-0.5563</th>\n",
" <th>-0.6037</th>\n",
" <th>-0.5354</th>\n",
" <th>1.0796</th>\n",
" <th>-0.5292</th>\n",
" <th>2.0004</th>\n",
" <th>-0.5722</th>\n",
" <th>1.2152</th>\n",
" <th>1.5187</th>\n",
" <th>-0.4832</th>\n",
" <th>-0.4978</th>\n",
" <th>-0.5790</th>\n",
" <th>-0.5351</th>\n",
" <th>-0.5623</th>\n",
" <th>-0.5231</th>\n",
" <th>-0.5954</th>\n",
" <th>-0.5430</th>\n",
" <th>-0.5133</th>\n",
" <th>-0.6051</th>\n",
" <th>-0.5610</th>\n",
" <th>1.2920</th>\n",
" <th>-0.5182</th>\n",
" <th>-0.5202</th>\n",
" <th>-0.5055</th>\n",
" <th>-0.5072</th>\n",
" <th>2.2915</th>\n",
" <th>1.4793</th>\n",
" <th>-0.5127</th>\n",
" <th>-0.5484</th>\n",
" <th>-0.5969</th>\n",
" <th>-0.5220</th>\n",
" <th>-0.6138</th>\n",
" <th>-0.5373</th>\n",
" <th>-0.5313</th>\n",
" <th>-0.5488</th>\n",
" <th>-0.5054</th>\n",
" <th>-0.5197</th>\n",
" <th>1.3737</th>\n",
" <th>-0.5739</th>\n",
" <th>-0.5153</th>\n",
" <th>-0.5574</th>\n",
" <th>2.8485</th>\n",
" <th>1.4081</th>\n",
" <th>1.9254</th>\n",
" <th>1.7654</th>\n",
" <th>-0.4994</th>\n",
" <th>-0.5412</th>\n",
" <th>-0.5627</th>\n",
" <th>2.3922</th>\n",
" <th>-0.5431</th>\n",
" <th>-0.5382</th>\n",
" <th>1.5048</th>\n",
" <th>-0.5911</th>\n",
" <th>-0.5245</th>\n",
" <th>-0.5472</th>\n",
" <th>-0.5594</th>\n",
" <th>2.2875</th>\n",
" <th>2.3579</th>\n",
" <th>-0.5673</th>\n",
" <th>1.7781</th>\n",
" <th>1.4425</th>\n",
" <th>-0.5668</th>\n",
" <th>2.7676</th>\n",
" <th>-0.4995</th>\n",
" <th>-0.5431</th>\n",
" <th>-0.5260</th>\n",
" <th>-0.5655</th>\n",
" <th>-0.6201</th>\n",
" <th>-0.5270</th>\n",
" <th>-0.5602</th>\n",
" <th>1.3496</th>\n",
" <th>-0.5395</th>\n",
" <th>-0.5273</th>\n",
" <th>-0.5482</th>\n",
" <th>-0.5312</th>\n",
" <th>-0.5303</th>\n",
" <th>-0.5297</th>\n",
" <th>-0.5787</th>\n",
" <th>1.4236</th>\n",
" <th>1.6564</th>\n",
" <th>1.3529</th>\n",
" <th>-0.5561</th>\n",
" <th>-0.5710</th>\n",
" <th>-0.5719</th>\n",
" <th>-0.5749</th>\n",
" <th>-0.5536</th>\n",
" <th>-0.5492</th>\n",
" <th>-0.5067</th>\n",
" <th>-0.5443</th>\n",
" <th>-0.5309</th>\n",
" <th>-0.5467</th>\n",
" <th>1.4633</th>\n",
" <th>-0.5742</th>\n",
" <th>-0.5621</th>\n",
" <th>-0.5360</th>\n",
" <th>-0.5424</th>\n",
" <th>1.8152</th>\n",
" <th>-0.5541</th>\n",
" <th>1.1026</th>\n",
" <th>1.1600</th>\n",
" <th>-0.5792</th>\n",
" <th>-0.5031</th>\n",
" <th>-0.5401</th>\n",
" <th>-0.5738</th>\n",
" <th>-0.5399</th>\n",
" <th>-0.5361</th>\n",
" <th>-0.5280</th>\n",
" <th>-0.5504</th>\n",
" <th>-0.5691</th>\n",
" <th>-0.5580</th>\n",
" <th>-0.5479</th>\n",
" <th>-0.4943</th>\n",
" <th>-0.4855</th>\n",
" <th>-0.4716</th>\n",
" <th>-0.5382</th>\n",
" <th>-0.5218</th>\n",
" <th>-0.5375</th>\n",
" <th>-0.4349</th>\n",
" <th>-0.4813</th>\n",
" <th>-0.5401</th>\n",
" <th>-0.5436</th>\n",
" <th>-0.5576</th>\n",
" <th>1.6350</th>\n",
" <th>-0.5627</th>\n",
" <th>-0.5549</th>\n",
" <th>-0.5687</th>\n",
" <th>-0.5985</th>\n",
" <th>-0.5310</th>\n",
" <th>1.1797</th>\n",
" <th>1.6579</th>\n",
" <th>-0.5582</th>\n",
" <th>-0.4811</th>\n",
" <th>-0.5109</th>\n",
" <th>1.1529</th>\n",
" <th>-0.5171</th>\n",
" <th>-0.5988</th>\n",
" <th>-0.5671</th>\n",
" <th>-0.5107</th>\n",
" <th>-0.5204</th>\n",
" <th>1.1692</th>\n",
" <th>-0.5063</th>\n",
" <th>1.3834</th>\n",
" <th>-0.5303</th>\n",
" <th>-0.5457</th>\n",
" <th>2.0653</th>\n",
" <th>-0.5291</th>\n",
" <th>-0.4809</th>\n",
" <th>-0.5129</th>\n",
" <th>-0.4984</th>\n",
" <th>-0.5053</th>\n",
" <th>-0.5020</th>\n",
" <th>-0.5318</th>\n",
" <th>1.5978</th>\n",
" <th>-0.5240</th>\n",
" <th>1.4252</th>\n",
" <th>-0.4986</th>\n",
" <th>-0.4894</th>\n",
" <th>-0.5145</th>\n",
" <th>-0.5623</th>\n",
" <th>-0.5535</th>\n",
" <th>-0.5432</th>\n",
" <th>-0.5845</th>\n",
" <th>-0.5229</th>\n",
" <th>-0.5360</th>\n",
" <th>1.3298</th>\n",
" <th>-0.5592</th>\n",
" <th>-0.5779</th>\n",
" <th>-0.5590</th>\n",
" <th>-0.5378</th>\n",
" <th>-0.5386</th>\n",
" <th>1.2617</th>\n",
" <th>-0.5512</th>\n",
" <th>-0.5380</th>\n",
" <th>-0.5272</th>\n",
" <th>-0.6381</th>\n",
" <th>1.8915</th>\n",
" <th>4.0284</th>\n",
" <th>2.4364</th>\n",
" <th>-0.5114</th>\n",
" <th>-0.4961</th>\n",
" <th>-0.5530</th>\n",
" <th>-0.5466</th>\n",
" <th>-0.5549</th>\n",
" <th>-0.5355</th>\n",
" <th>-0.5495</th>\n",
" <th>-0.5442</th>\n",
" <th>-0.5503</th>\n",
" <th>-0.5828</th>\n",
" <th>-0.6027</th>\n",
" <th>-0.5430</th>\n",
" <th>-0.5664</th>\n",
" <th>-0.5566</th>\n",
" <th>2.8722</th>\n",
" <th>2.8836</th>\n",
" <th>-0.5500</th>\n",
" <th>-0.5172</th>\n",
" <th>-0.5256</th>\n",
" <th>-0.5299</th>\n",
" <th>-0.5672</th>\n",
" <th>-0.5321</th>\n",
" <th>-0.5399</th>\n",
" <th>1.2670</th>\n",
" <th>1.4332</th>\n",
" <th>-0.5756</th>\n",
" <th>-0.5519</th>\n",
" <th>-0.5490</th>\n",
" <th>-0.5595</th>\n",
" <th>-0.5552</th>\n",
" <th>-0.5306</th>\n",
" <th>1.2864</th>\n",
" <th>-0.5461</th>\n",
" <th>-0.5218</th>\n",
" <th>2.5309</th>\n",
" <th>1.8898</th>\n",
" <th>-0.5789</th>\n",
" <th>-0.5824</th>\n",
" <th>-0.5782</th>\n",
" <th>-0.5445</th>\n",
" <th>-0.5548</th>\n",
" <th>-0.5210</th>\n",
" <th>-0.5457</th>\n",
" <th>-0.5468</th>\n",
" <th>-0.5104</th>\n",
" <th>-0.5083</th>\n",
" <th>-0.4959</th>\n",
" <th>-0.4995</th>\n",
" <th>-0.5966</th>\n",
" <th>-0.5313</th>\n",
" <th>-0.5782</th>\n",
" <th>-0.5278</th>\n",
" <th>1.8836</th>\n",
" <th>-0.5372</th>\n",
" <th>-0.5222</th>\n",
" <th>-0.5462</th>\n",
" <th>-0.5813</th>\n",
" <th>-0.4924</th>\n",
" <th>-0.5799</th>\n",
" <th>-0.5776</th>\n",
" <th>-0.5314</th>\n",
" <th>-0.5334</th>\n",
" <th>-0.5122</th>\n",
" <th>-0.5952</th>\n",
" <th>-0.5546</th>\n",
" <th>-0.5282</th>\n",
" <th>-0.5103</th>\n",
" <th>1.6171</th>\n",
" <th>-0.4982</th>\n",
" <th>-0.5900</th>\n",
" <th>1.3531</th>\n",
" <th>1.4511</th>\n",
" <th>-0.5631</th>\n",
" <th>-0.5326</th>\n",
" <th>-0.5113</th>\n",
" <th>-0.5102</th>\n",
" <th>-0.5717</th>\n",
" <th>-0.4977</th>\n",
" <th>-0.5418</th>\n",
" <th>-0.4840</th>\n",
" <th>-0.4919</th>\n",
" <th>-0.5310</th>\n",
" <th>-0.5322</th>\n",
" <th>-0.4891</th>\n",
" <th>-0.5103</th>\n",
" <th>-0.5077</th>\n",
" <th>-0.5336</th>\n",
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" <th>-0.5120</th>\n",
" <th>-0.4619</th>\n",
" <th>-0.4641</th>\n",
" <th>-0.4367</th>\n",
" <th>-0.4247</th>\n",
" <th>-0.4094</th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>1.5315</th>\n",
" <th>2.5019</th>\n",
" <th>-0.5686</th>\n",
" <th>-0.5579</th>\n",
" <th>1.4973</th>\n",
" <th>2.2159</th>\n",
" <th>1.1846</th>\n",
" <th>-0.5141</th>\n",
" <th>1.2458</th>\n",
" <th>-0.4958</th>\n",
" <th>-0.5014</th>\n",
" <th>1.3990</th>\n",
" <th>-0.5799</th>\n",
" <th>1.2159</th>\n",
" <th>1.0581</th>\n",
" <th>2.4625</th>\n",
" <th>-0.5432</th>\n",
" <th>-0.5452</th>\n",
" <th>-0.4791</th>\n",
" <th>1.2612</th>\n",
" <th>1.6702</th>\n",
" <th>-0.5091</th>\n",
" <th>1.1160</th>\n",
" <th>-0.5749</th>\n",
" <th>-0.5262</th>\n",
" <th>-0.5312</th>\n",
" <th>-0.5676</th>\n",
" <th>1.5739</th>\n",
" <th>1.6172</th>\n",
" <th>-0.5276</th>\n",
" <th>1.7735</th>\n",
" <th>2.5626</th>\n",
" <th>-0.5189</th>\n",
" <th>-0.5630</th>\n",
" <th>-0.5479</th>\n",
" <th>1.2887</th>\n",
" <th>-0.5719</th>\n",
" <th>-0.5217</th>\n",
" <th>-0.5554</th>\n",
" <th>-0.5109</th>\n",
" <th>-0.5402</th>\n",
" <th>1.2514</th>\n",
" <th>-0.5308</th>\n",
" <th>-0.5379</th>\n",
" <th>-0.5615</th>\n",
" <th>-0.5399</th>\n",
" <th>-0.5450</th>\n",
" <th>-0.5597</th>\n",
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" <th>-0.5359</th>\n",
" <th>2.3146</th>\n",
" <th>-0.5069</th>\n",
" <th>-0.5269</th>\n",
" <th>1.7494</th>\n",
" <th>2.7450</th>\n",
" <th>-0.5676</th>\n",
" <th>1.7484</th>\n",
" <th>-0.4878</th>\n",
" <th>-0.4974</th>\n",
" <th>-0.5264</th>\n",
" <th>2.7701</th>\n",
" <th>1.8309</th>\n",
" <th>-0.5836</th>\n",
" <th>-0.5179</th>\n",
" <th>-0.5427</th>\n",
" <th>1.5089</th>\n",
" <th>-0.5577</th>\n",
" <th>-0.5276</th>\n",
" <th>-0.5818</th>\n",
" <th>-0.5575</th>\n",
" <th>-0.4771</th>\n",
" <th>-0.5450</th>\n",
" <th>2.1293</th>\n",
" <th>-0.4632</th>\n",
" <th>-0.5241</th>\n",
" <th>-0.6197</th>\n",
" <th>-0.4918</th>\n",
" <th>1.6754</th>\n",
" <th>1.5032</th>\n",
" <th>1.3224</th>\n",
" <th>2.5173</th>\n",
" <th>-0.5137</th>\n",
" <th>1.4705</th>\n",
" <th>-0.4788</th>\n",
" <th>-0.5440</th>\n",
" <th>-0.5596</th>\n",
" <th>-0.5265</th>\n",
" <th>-0.5507</th>\n",
" <th>-0.5202</th>\n",
" <th>1.8159</th>\n",
" <th>-0.5192</th>\n",
" <th>1.5847</th>\n",
" <th>-0.5369</th>\n",
" <th>-0.5619</th>\n",
" <th>-0.5755</th>\n",
" <th>-0.5085</th>\n",
" <th>-0.5671</th>\n",
" <th>-0.5561</th>\n",
" <th>1.6252</th>\n",
" <th>-0.5065</th>\n",
" <th>-0.4939</th>\n",
" <th>-0.4852</th>\n",
" <th>1.4287</th>\n",
" <th>-0.5627</th>\n",
" <th>-0.5380</th>\n",
" <th>-0.5412</th>\n",
" <th>-0.5471</th>\n",
" <th>1.3126</th>\n",
" <th>1.1647</th>\n",
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" <th>-0.5368</th>\n",
" <th>-0.5674</th>\n",
" <th>-0.5444</th>\n",
" <th>-0.5086</th>\n",
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" <th>-0.5199</th>\n",
" <th>1.1438</th>\n",
" <th>-0.4854</th>\n",
" <th>-0.5484</th>\n",
" <th>-0.5085</th>\n",
" <th>-0.5403</th>\n",
" <th>1.2583</th>\n",
" <th>-0.5256</th>\n",
" <th>-0.5695</th>\n",
" <th>-0.5403</th>\n",
" <th>1.5416</th>\n",
" <th>1.1513</th>\n",
" <th>-0.4470</th>\n",
" <th>-0.4506</th>\n",
" <th>-0.4852</th>\n",
" <th>-0.5041</th>\n",
" <th>-0.5220</th>\n",
" <th>2.3866</th>\n",
" <th>1.6995</th>\n",
" <th>-0.5682</th>\n",
" <th>-0.4845</th>\n",
" <th>2.1565</th>\n",
" <th>0.9501</th>\n",
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" <th>-0.5318</th>\n",
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" <th>-0.5026</th>\n",
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" <th>-0.5914</th>\n",
" <th>1.7409</th>\n",
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" <th>-0.5220</th>\n",
" <th>-0.5644</th>\n",
" <th>3.3680</th>\n",
" <th>-0.5295</th>\n",
" <th>2.6048</th>\n",
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" <th>-0.5135</th>\n",
" <th>1.1514</th>\n",
" <th>1.8457</th>\n",
" <th>-0.5325</th>\n",
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" <th>-0.5618</th>\n",
" <th>-0.5287</th>\n",
" <th>1.6742</th>\n",
" <th>1.8132</th>\n",
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" <th>-0.5093</th>\n",
" <th>-0.4973</th>\n",
" <th>-0.5642</th>\n",
" <th>-0.5538</th>\n",
" <th>-0.5237</th>\n",
" <th>-0.5350</th>\n",
" <th>1.5006</th>\n",
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" <th>-0.5122</th>\n",
" <th>-0.5248</th>\n",
" <th>-0.5049</th>\n",
" <th>-0.5704</th>\n",
" <th>2.2112</th>\n",
" <th>-0.4791</th>\n",
" <th>-0.5239</th>\n",
" <th>-0.5750</th>\n",
" <th>-0.5308</th>\n",
" <th>-0.5378</th>\n",
" <th>-0.4669</th>\n",
" <th>-0.5386</th>\n",
" <th>-0.5261</th>\n",
" <th>-0.4860</th>\n",
" <th>-0.4944</th>\n",
" <th>-0.5260</th>\n",
" <th>-0.5342</th>\n",
" <th>-0.5339</th>\n",
" <th>-0.5541</th>\n",
" <th>-0.5353</th>\n",
" <th>1.8575</th>\n",
" <th>-0.4896</th>\n",
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" <th>-0.5231</th>\n",
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" <th>-0.5724</th>\n",
" <th>1.7975</th>\n",
" <th>1.3321</th>\n",
" <th>-0.5705</th>\n",
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" <th>-0.5511</th>\n",
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" <th>-0.5500</th>\n",
" <th>-0.5272</th>\n",
" <th>-0.5918</th>\n",
" <th>-0.5442</th>\n",
" <th>2.2009</th>\n",
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" <th>-0.5502</th>\n",
" <th>1.4616</th>\n",
" <th>1.1978</th>\n",
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" <th>-0.5272</th>\n",
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" <th>1.3193</th>\n",
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" <th>1.4233</th>\n",
" <th>-0.5131</th>\n",
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" <th>-0.5217</th>\n",
" <th>-0.5387</th>\n",
" <th>1.4168</th>\n",
" <th>-0.5100</th>\n",
" <th>-0.5173</th>\n",
" <th>-0.5639</th>\n",
" <th>1.9533</th>\n",
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" <th>-0.4947</th>\n",
" <th>-0.5186</th>\n",
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" <th>-0.5056</th>\n",
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" <th>-0.5320</th>\n",
" <th>1.2850</th>\n",
" <th>-0.5319</th>\n",
" <th>2.3951</th>\n",
" <th>-0.5129</th>\n",
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" <th>-0.5502</th>\n",
" <th>2.5254</th>\n",
" <th>-0.4985</th>\n",
" <th>-0.5579</th>\n",
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" <th>-0.5559</th>\n",
" <th>-0.4719</th>\n",
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" <th>-0.5329</th>\n",
" <th>2.7560</th>\n",
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" <th>2.9655</th>\n",
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" <th>-0.5347</th>\n",
" <th>-0.5308</th>\n",
" <th>-0.5230</th>\n",
" <th>-0.5458</th>\n",
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" <th>1.1832</th>\n",
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" <th>-0.3961</th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>1.8752</th>\n",
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" <th>1.7500</th>\n",
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" <th>1.0821</th>\n",
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" <th>2.2788</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
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" <tr>\n",
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" <th>1.4028</th>\n",
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" <th>2.6453</th>\n",
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" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
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" </tr>\n",
" <tr>\n",
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" <th>-0.3660</th>\n",
" <th>3.5449</th>\n",
" <th>-0.3516</th>\n",
" <th>-0.3636</th>\n",
" <th>-0.3698</th>\n",
" <th>-0.3360</th>\n",
" <th>-0.3407</th>\n",
" <th>2.7428</th>\n",
" <th>-0.2721</th>\n",
" <th>-0.2372</th>\n",
" <th>-0.2326</th>\n",
" <th>-0.2132</th>\n",
" <th>1</th>\n",
" </tr>\n",
"</table>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.show_batch(rows=10)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"learn = tabular_learner(data, layers=[1024, 512, 256], metrics=accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TabularModel(\n",
" (embeds): ModuleList()\n",
" (emb_drop): Dropout(p=0.0)\n",
" (bn_cont): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (layers): Sequential(\n",
" (0): Linear(in_features=512, out_features=1024, bias=True)\n",
" (1): ReLU(inplace)\n",
" (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (3): Linear(in_features=1024, out_features=512, bias=True)\n",
" (4): ReLU(inplace)\n",
" (5): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (6): Linear(in_features=512, out_features=256, bias=True)\n",
" (7): ReLU(inplace)\n",
" (8): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (9): Linear(in_features=256, out_features=2, bias=True)\n",
" )\n",
")"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.model"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
]
}
],
"source": [
"learn.lr_find()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn.recorder.plot()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total time: 00:00\n",
"epoch train_loss valid_loss accuracy\n",
"1 0.604293 0.692538 0.748201 (00:00)\n",
"\n"
]
}
],
"source": [
"learn = tabular_learner(data, layers=[512, 512, 512, 256, ], metrics=accuracy)\n",
"learn.fit_one_cycle(1, 1e-2, wd=0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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