|
23 | 23 | "CPython 3.6.8\n",
|
24 | 24 | "IPython 7.2.0\n",
|
25 | 25 | "\n",
|
26 |
| - "torch 1.0.0\n" |
| 26 | + "torch 1.1.0\n" |
27 | 27 | ]
|
28 | 28 | }
|
29 | 29 | ],
|
|
171 | 171 | " \n",
|
172 | 172 | " # 28x28x1 => 28x28x4\n",
|
173 | 173 | " self.conv_1 = torch.nn.Conv2d(in_channels=1,\n",
|
174 |
| - " out_channels=4,\n", |
| 174 | + " out_channels=8,\n", |
175 | 175 | " kernel_size=(3, 3),\n",
|
176 | 176 | " stride=(1, 1),\n",
|
177 | 177 | " padding=1) # (1(28-1) - 28 + 3) / 2 = 1\n",
|
|
180 | 180 | " stride=(2, 2),\n",
|
181 | 181 | " padding=0) # (2(14-1) - 28 + 2) = 0 \n",
|
182 | 182 | " # 14x14x4 => 14x14x8\n",
|
183 |
| - " self.conv_2 = torch.nn.Conv2d(in_channels=4,\n", |
184 |
| - " out_channels=8,\n", |
| 183 | + " self.conv_2 = torch.nn.Conv2d(in_channels=8,\n", |
| 184 | + " out_channels=16,\n", |
185 | 185 | " kernel_size=(3, 3),\n",
|
186 | 186 | " stride=(1, 1),\n",
|
187 | 187 | " padding=1) # (1(14-1) - 14 + 3) / 2 = 1 \n",
|
188 | 188 | " # 14x14x8 => 7x7x8 \n",
|
189 | 189 | " self.pool_2 = torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
|
190 | 190 | " stride=(2, 2),\n",
|
191 | 191 | " padding=0) # (2(7-1) - 14 + 2) = 0\n",
|
192 |
| - " \n", |
193 |
| - " self.linear_1 = torch.nn.Linear(7*7*8, num_classes)\n", |
194 | 192 | "\n",
|
| 193 | + " self.linear_1 = torch.nn.Linear(7*7*16, num_classes)\n", |
| 194 | + "\n", |
| 195 | + " # optionally initialize weights from Gaussian;\n", |
| 196 | + " # Guassian weight init is not recommended and only for demonstration purposes\n", |
| 197 | + " for m in self.modules():\n", |
| 198 | + " if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Linear):\n", |
| 199 | + " m.weight.data.normal_(0.0, 0.01)\n", |
| 200 | + " m.bias.data.zero_()\n", |
| 201 | + " if m.bias is not None:\n", |
| 202 | + " m.bias.detach().zero_()\n", |
| 203 | + " \n", |
195 | 204 | " \n",
|
196 | 205 | " def forward(self, x):\n",
|
197 | 206 | " out = self.conv_1(x)\n",
|
|
202 | 211 | " out = F.relu(out)\n",
|
203 | 212 | " out = self.pool_2(out)\n",
|
204 | 213 | " \n",
|
205 |
| - " logits = self.linear_1(out.view(-1, 7*7*8))\n", |
| 214 | + " logits = self.linear_1(out.view(-1, 7*7*16))\n", |
206 | 215 | " probas = F.softmax(logits, dim=1)\n",
|
207 | 216 | " return logits, probas\n",
|
208 | 217 | "\n",
|
|
231 | 240 | "name": "stdout",
|
232 | 241 | "output_type": "stream",
|
233 | 242 | "text": [
|
234 |
| - "Epoch: 001/010 | Batch 000/469 | Cost: 2.3016\n", |
235 |
| - "Epoch: 001/010 | Batch 050/469 | Cost: 2.2714\n", |
236 |
| - "Epoch: 001/010 | Batch 100/469 | Cost: 1.6118\n", |
237 |
| - "Epoch: 001/010 | Batch 150/469 | Cost: 0.7966\n", |
238 |
| - "Epoch: 001/010 | Batch 200/469 | Cost: 0.5077\n", |
239 |
| - "Epoch: 001/010 | Batch 250/469 | Cost: 0.3221\n", |
240 |
| - "Epoch: 001/010 | Batch 300/469 | Cost: 0.2850\n", |
241 |
| - "Epoch: 001/010 | Batch 350/469 | Cost: 0.3116\n", |
242 |
| - "Epoch: 001/010 | Batch 400/469 | Cost: 0.2836\n", |
243 |
| - "Epoch: 001/010 | Batch 450/469 | Cost: 0.3169\n", |
244 |
| - "Epoch: 001/010 training accuracy: 92.72%\n", |
245 |
| - "Time elapsed: 0.21 min\n", |
246 |
| - "Epoch: 002/010 | Batch 000/469 | Cost: 0.2469\n", |
247 |
| - "Epoch: 002/010 | Batch 050/469 | Cost: 0.2342\n", |
248 |
| - "Epoch: 002/010 | Batch 100/469 | Cost: 0.2883\n", |
249 |
| - "Epoch: 002/010 | Batch 150/469 | Cost: 0.2920\n", |
250 |
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251 |
| - "Epoch: 002/010 | Batch 250/469 | Cost: 0.2277\n", |
252 |
| - "Epoch: 002/010 | Batch 300/469 | Cost: 0.1747\n", |
253 |
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254 |
| - "Epoch: 002/010 | Batch 400/469 | Cost: 0.1578\n", |
255 |
| - "Epoch: 002/010 | Batch 450/469 | Cost: 0.1279\n", |
256 |
| - "Epoch: 002/010 training accuracy: 95.07%\n", |
257 |
| - "Time elapsed: 0.41 min\n", |
258 |
| - "Epoch: 003/010 | Batch 000/469 | Cost: 0.1223\n", |
259 |
| - "Epoch: 003/010 | Batch 050/469 | Cost: 0.1999\n", |
260 |
| - "Epoch: 003/010 | Batch 100/469 | Cost: 0.2212\n", |
261 |
| - "Epoch: 003/010 | Batch 150/469 | Cost: 0.0905\n", |
262 |
| - "Epoch: 003/010 | Batch 200/469 | Cost: 0.1502\n", |
263 |
| - "Epoch: 003/010 | Batch 250/469 | Cost: 0.2391\n", |
264 |
| - "Epoch: 003/010 | Batch 300/469 | Cost: 0.1108\n", |
265 |
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266 |
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267 |
| - "Epoch: 003/010 | Batch 450/469 | Cost: 0.1253\n", |
268 |
| - "Epoch: 003/010 training accuracy: 96.21%\n", |
| 243 | + "Epoch: 001/010 | Batch 000/469 | Cost: 2.3026\n", |
| 244 | + "Epoch: 001/010 | Batch 050/469 | Cost: 2.3036\n", |
| 245 | + "Epoch: 001/010 | Batch 100/469 | Cost: 2.3001\n", |
| 246 | + "Epoch: 001/010 | Batch 150/469 | Cost: 2.3050\n", |
| 247 | + "Epoch: 001/010 | Batch 200/469 | Cost: 2.2984\n", |
| 248 | + "Epoch: 001/010 | Batch 250/469 | Cost: 2.2986\n", |
| 249 | + "Epoch: 001/010 | Batch 300/469 | Cost: 2.2983\n", |
| 250 | + "Epoch: 001/010 | Batch 350/469 | Cost: 2.2941\n", |
| 251 | + "Epoch: 001/010 | Batch 400/469 | Cost: 2.2962\n", |
| 252 | + "Epoch: 001/010 | Batch 450/469 | Cost: 2.2265\n", |
| 253 | + "Epoch: 001/010 training accuracy: 65.38%\n", |
| 254 | + "Time elapsed: 0.29 min\n", |
| 255 | + "Epoch: 002/010 | Batch 000/469 | Cost: 1.8989\n", |
| 256 | + "Epoch: 002/010 | Batch 050/469 | Cost: 0.6029\n", |
| 257 | + "Epoch: 002/010 | Batch 100/469 | Cost: 0.6099\n", |
| 258 | + "Epoch: 002/010 | Batch 150/469 | Cost: 0.4786\n", |
| 259 | + "Epoch: 002/010 | Batch 200/469 | Cost: 0.4518\n", |
| 260 | + "Epoch: 002/010 | Batch 250/469 | Cost: 0.3553\n", |
| 261 | + "Epoch: 002/010 | Batch 300/469 | Cost: 0.3167\n", |
| 262 | + "Epoch: 002/010 | Batch 350/469 | Cost: 0.2241\n", |
| 263 | + "Epoch: 002/010 | Batch 400/469 | Cost: 0.2259\n", |
| 264 | + "Epoch: 002/010 | Batch 450/469 | Cost: 0.3056\n", |
| 265 | + "Epoch: 002/010 training accuracy: 93.11%\n", |
269 | 266 | "Time elapsed: 0.62 min\n",
|
270 |
| - "Epoch: 004/010 | Batch 000/469 | Cost: 0.1368\n", |
271 |
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272 |
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273 |
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274 |
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275 |
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276 |
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277 |
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278 |
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279 |
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280 |
| - "Epoch: 004/010 training accuracy: 96.77%\n", |
281 |
| - "Time elapsed: 0.82 min\n", |
282 |
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283 |
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284 |
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285 |
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286 |
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287 |
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288 |
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289 |
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290 |
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291 |
| - "Epoch: 005/010 | Batch 450/469 | Cost: 0.0599\n", |
292 |
| - "Epoch: 005/010 training accuracy: 97.15%\n", |
293 |
| - "Time elapsed: 1.03 min\n", |
294 |
| - "Epoch: 006/010 | Batch 000/469 | Cost: 0.0721\n", |
295 |
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296 |
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297 |
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298 |
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299 |
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300 |
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301 |
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302 |
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303 |
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304 |
| - "Epoch: 006/010 training accuracy: 97.43%\n", |
305 |
| - "Time elapsed: 1.23 min\n", |
306 |
| - "Epoch: 007/010 | Batch 000/469 | Cost: 0.1986\n", |
307 |
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308 |
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309 |
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310 |
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311 |
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312 |
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313 |
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314 |
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315 |
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316 |
| - "Epoch: 007/010 training accuracy: 97.77%\n", |
317 |
| - "Time elapsed: 1.43 min\n", |
318 |
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319 |
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320 |
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321 |
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322 |
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323 |
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324 |
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325 |
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326 |
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327 |
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328 |
| - "Epoch: 008/010 training accuracy: 97.55%\n", |
329 |
| - "Time elapsed: 1.64 min\n", |
330 |
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331 |
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332 |
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333 |
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334 |
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335 |
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336 |
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337 |
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338 |
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339 |
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340 |
| - "Epoch: 009/010 training accuracy: 97.82%\n", |
341 |
| - "Time elapsed: 1.84 min\n", |
342 |
| - "Epoch: 010/010 | Batch 000/469 | Cost: 0.0183\n", |
343 |
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344 |
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345 |
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346 |
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347 |
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348 |
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349 |
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350 |
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351 |
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352 |
| - "Epoch: 010/010 training accuracy: 97.77%\n", |
353 |
| - "Time elapsed: 2.04 min\n", |
354 |
| - "Total Training Time: 2.04 min\n" |
| 267 | + "Epoch: 003/010 | Batch 000/469 | Cost: 0.3313\n", |
| 268 | + "Epoch: 003/010 | Batch 050/469 | Cost: 0.1042\n", |
| 269 | + "Epoch: 003/010 | Batch 100/469 | Cost: 0.1328\n", |
| 270 | + "Epoch: 003/010 | Batch 150/469 | Cost: 0.2803\n", |
| 271 | + "Epoch: 003/010 | Batch 200/469 | Cost: 0.0975\n", |
| 272 | + "Epoch: 003/010 | Batch 250/469 | Cost: 0.1839\n", |
| 273 | + "Epoch: 003/010 | Batch 300/469 | Cost: 0.1774\n", |
| 274 | + "Epoch: 003/010 | Batch 350/469 | Cost: 0.1143\n", |
| 275 | + "Epoch: 003/010 | Batch 400/469 | Cost: 0.1753\n", |
| 276 | + "Epoch: 003/010 | Batch 450/469 | Cost: 0.1543\n", |
| 277 | + "Epoch: 003/010 training accuracy: 95.68%\n", |
| 278 | + "Time elapsed: 0.93 min\n", |
| 279 | + "Epoch: 004/010 | Batch 000/469 | Cost: 0.1057\n", |
| 280 | + "Epoch: 004/010 | Batch 050/469 | Cost: 0.1035\n", |
| 281 | + "Epoch: 004/010 | Batch 100/469 | Cost: 0.1851\n", |
| 282 | + "Epoch: 004/010 | Batch 150/469 | Cost: 0.1608\n", |
| 283 | + "Epoch: 004/010 | Batch 200/469 | Cost: 0.1458\n", |
| 284 | + "Epoch: 004/010 | Batch 250/469 | Cost: 0.1913\n", |
| 285 | + "Epoch: 004/010 | Batch 300/469 | Cost: 0.1295\n", |
| 286 | + "Epoch: 004/010 | Batch 350/469 | Cost: 0.1518\n", |
| 287 | + "Epoch: 004/010 | Batch 400/469 | Cost: 0.1717\n", |
| 288 | + "Epoch: 004/010 | Batch 450/469 | Cost: 0.0792\n", |
| 289 | + "Epoch: 004/010 training accuracy: 96.46%\n", |
| 290 | + "Time elapsed: 1.24 min\n", |
| 291 | + "Epoch: 005/010 | Batch 000/469 | Cost: 0.0905\n", |
| 292 | + "Epoch: 005/010 | Batch 050/469 | Cost: 0.1622\n", |
| 293 | + "Epoch: 005/010 | Batch 100/469 | Cost: 0.1934\n", |
| 294 | + "Epoch: 005/010 | Batch 150/469 | Cost: 0.1874\n", |
| 295 | + "Epoch: 005/010 | Batch 200/469 | Cost: 0.0742\n", |
| 296 | + "Epoch: 005/010 | Batch 250/469 | Cost: 0.1056\n", |
| 297 | + "Epoch: 005/010 | Batch 300/469 | Cost: 0.0997\n", |
| 298 | + "Epoch: 005/010 | Batch 350/469 | Cost: 0.0948\n", |
| 299 | + "Epoch: 005/010 | Batch 400/469 | Cost: 0.0575\n", |
| 300 | + "Epoch: 005/010 | Batch 450/469 | Cost: 0.1157\n", |
| 301 | + "Epoch: 005/010 training accuracy: 96.97%\n", |
| 302 | + "Time elapsed: 1.56 min\n", |
| 303 | + "Epoch: 006/010 | Batch 000/469 | Cost: 0.1326\n", |
| 304 | + "Epoch: 006/010 | Batch 050/469 | Cost: 0.1549\n", |
| 305 | + "Epoch: 006/010 | Batch 100/469 | Cost: 0.0784\n", |
| 306 | + "Epoch: 006/010 | Batch 150/469 | Cost: 0.0898\n", |
| 307 | + "Epoch: 006/010 | Batch 200/469 | Cost: 0.0991\n", |
| 308 | + "Epoch: 006/010 | Batch 250/469 | Cost: 0.0965\n", |
| 309 | + "Epoch: 006/010 | Batch 300/469 | Cost: 0.0477\n", |
| 310 | + "Epoch: 006/010 | Batch 350/469 | Cost: 0.0712\n", |
| 311 | + "Epoch: 006/010 | Batch 400/469 | Cost: 0.1109\n", |
| 312 | + "Epoch: 006/010 | Batch 450/469 | Cost: 0.0325\n", |
| 313 | + "Epoch: 006/010 training accuracy: 97.60%\n", |
| 314 | + "Time elapsed: 1.88 min\n", |
| 315 | + "Epoch: 007/010 | Batch 000/469 | Cost: 0.0665\n", |
| 316 | + "Epoch: 007/010 | Batch 050/469 | Cost: 0.0868\n", |
| 317 | + "Epoch: 007/010 | Batch 100/469 | Cost: 0.0427\n", |
| 318 | + "Epoch: 007/010 | Batch 150/469 | Cost: 0.0385\n", |
| 319 | + "Epoch: 007/010 | Batch 200/469 | Cost: 0.0611\n", |
| 320 | + "Epoch: 007/010 | Batch 250/469 | Cost: 0.0484\n", |
| 321 | + "Epoch: 007/010 | Batch 300/469 | Cost: 0.1288\n", |
| 322 | + "Epoch: 007/010 | Batch 350/469 | Cost: 0.0309\n", |
| 323 | + "Epoch: 007/010 | Batch 400/469 | Cost: 0.0359\n", |
| 324 | + "Epoch: 007/010 | Batch 450/469 | Cost: 0.0139\n", |
| 325 | + "Epoch: 007/010 training accuracy: 97.64%\n", |
| 326 | + "Time elapsed: 2.19 min\n", |
| 327 | + "Epoch: 008/010 | Batch 000/469 | Cost: 0.0939\n", |
| 328 | + "Epoch: 008/010 | Batch 050/469 | Cost: 0.1478\n", |
| 329 | + "Epoch: 008/010 | Batch 100/469 | Cost: 0.0769\n", |
| 330 | + "Epoch: 008/010 | Batch 150/469 | Cost: 0.0713\n", |
| 331 | + "Epoch: 008/010 | Batch 200/469 | Cost: 0.1272\n", |
| 332 | + "Epoch: 008/010 | Batch 250/469 | Cost: 0.0446\n", |
| 333 | + "Epoch: 008/010 | Batch 300/469 | Cost: 0.0525\n", |
| 334 | + "Epoch: 008/010 | Batch 350/469 | Cost: 0.1729\n", |
| 335 | + "Epoch: 008/010 | Batch 400/469 | Cost: 0.0672\n", |
| 336 | + "Epoch: 008/010 | Batch 450/469 | Cost: 0.0754\n", |
| 337 | + "Epoch: 008/010 training accuracy: 96.67%\n", |
| 338 | + "Time elapsed: 2.50 min\n", |
| 339 | + "Epoch: 009/010 | Batch 000/469 | Cost: 0.0988\n", |
| 340 | + "Epoch: 009/010 | Batch 050/469 | Cost: 0.0409\n", |
| 341 | + "Epoch: 009/010 | Batch 100/469 | Cost: 0.1046\n", |
| 342 | + "Epoch: 009/010 | Batch 150/469 | Cost: 0.0523\n", |
| 343 | + "Epoch: 009/010 | Batch 200/469 | Cost: 0.0815\n", |
| 344 | + "Epoch: 009/010 | Batch 250/469 | Cost: 0.0811\n", |
| 345 | + "Epoch: 009/010 | Batch 300/469 | Cost: 0.0416\n", |
| 346 | + "Epoch: 009/010 | Batch 350/469 | Cost: 0.0747\n", |
| 347 | + "Epoch: 009/010 | Batch 400/469 | Cost: 0.0467\n", |
| 348 | + "Epoch: 009/010 | Batch 450/469 | Cost: 0.0669\n", |
| 349 | + "Epoch: 009/010 training accuracy: 97.90%\n", |
| 350 | + "Time elapsed: 2.78 min\n", |
| 351 | + "Epoch: 010/010 | Batch 000/469 | Cost: 0.0257\n", |
| 352 | + "Epoch: 010/010 | Batch 050/469 | Cost: 0.0357\n", |
| 353 | + "Epoch: 010/010 | Batch 100/469 | Cost: 0.1469\n", |
| 354 | + "Epoch: 010/010 | Batch 150/469 | Cost: 0.0170\n", |
| 355 | + "Epoch: 010/010 | Batch 200/469 | Cost: 0.0493\n", |
| 356 | + "Epoch: 010/010 | Batch 250/469 | Cost: 0.0489\n", |
| 357 | + "Epoch: 010/010 | Batch 300/469 | Cost: 0.1348\n", |
| 358 | + "Epoch: 010/010 | Batch 350/469 | Cost: 0.0815\n", |
| 359 | + "Epoch: 010/010 | Batch 400/469 | Cost: 0.0552\n", |
| 360 | + "Epoch: 010/010 | Batch 450/469 | Cost: 0.0422\n", |
| 361 | + "Epoch: 010/010 training accuracy: 97.99%\n", |
| 362 | + "Time elapsed: 3.02 min\n", |
| 363 | + "Total Training Time: 3.02 min\n" |
355 | 364 | ]
|
356 | 365 | }
|
357 | 366 | ],
|
|
418 | 427 | "name": "stdout",
|
419 | 428 | "output_type": "stream",
|
420 | 429 | "text": [
|
421 |
| - "Test accuracy: 97.77%\n" |
| 430 | + "Test accuracy: 97.97%\n" |
422 | 431 | ]
|
423 | 432 | }
|
424 | 433 | ],
|
|
437 | 446 | "output_type": "stream",
|
438 | 447 | "text": [
|
439 | 448 | "numpy 1.15.4\n",
|
440 |
| - "torch 1.0.0\n", |
| 449 | + "torch 1.1.0\n", |
441 | 450 | "\n"
|
442 | 451 | ]
|
443 | 452 | }
|
|
463 | 472 | "name": "python",
|
464 | 473 | "nbconvert_exporter": "python",
|
465 | 474 | "pygments_lexer": "ipython3",
|
466 |
| - "version": "3.7.1" |
| 475 | + "version": "3.6.8" |
467 | 476 | },
|
468 | 477 | "toc": {
|
469 | 478 | "nav_menu": {},
|
|
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