{"cells":[{"cell_type":"markdown","metadata":{},"source":["# TFLite"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"colab_type":"code","executionInfo":{"elapsed":8885,"status":"ok","timestamp":1589834320368,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"M0T7S70LgfXE","outputId":"3409f5d7-5779-4582-fa9e-5743a48d0ff2"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2020-05-18 20:38:35--  https://video.udacity-data.com/topher/2019/September/5d8e8cb3_tflite-apps/tflite-apps.zip\n","Resolving video.udacity-data.com (video.udacity-data.com)... 104.31.14.178, 104.31.15.178, 2606:4700:3038::681f:eb2, ...\n","Connecting to video.udacity-data.com (video.udacity-data.com)|104.31.14.178|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 16833985 (16M) [application/zip]\n","Saving to: ‘tflite-apps.zip’\n","\n","tflite-apps.zip     100%[===================>]  16.05M  9.29MB/s    in 1.7s    \n","\n","2020-05-18 20:38:37 (9.29 MB/s) - ‘tflite-apps.zip’ saved [16833985/16833985]\n","\n"]}],"source":["!wget https://video.udacity-data.com/topher/2019/September/5d8e8cb3_tflite-apps/tflite-apps.zip"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"colab_type":"code","executionInfo":{"elapsed":5690,"status":"ok","timestamp":1589834346589,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"t5iJAQ9kght2","outputId":"eb575cdb-ca42-4956-d438-ca40936028d9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Archive:  tflite-apps.zip\n","   creating: tflite_apps/\n","  inflating: tflite_apps/.DS_Store   \n","   creating: __MACOSX/\n","   creating: __MACOSX/tflite_apps/\n","  inflating: __MACOSX/tflite_apps/._.DS_Store  \n","   creating: tflite_apps/lesson_5_iot_applications/\n","  inflating: 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tflite-apps.zip"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"colab_type":"code","executionInfo":{"elapsed":7564,"status":"ok","timestamp":1589835487857,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"UcMLhUnUgtmx","outputId":"714a6c87-35a3-4f6d-f3a3-2a0e54e18c3f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.1674\n","Epoch 2/500\n","1/1 [==============================] - 0s 4ms/step - loss: 4.2415\n","Epoch 3/500\n","1/1 [==============================] - 0s 925us/step - loss: 3.5095\n","Epoch 4/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.9301\n","Epoch 5/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.4707\n","Epoch 6/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.1059\n","Epoch 7/500\n","1/1 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1ms/step - loss: 0.2524\n","Epoch 64/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2472\n","Epoch 65/500\n","1/1 [==============================] - 0s 958us/step - loss: 0.2421\n","Epoch 66/500\n","1/1 [==============================] - 0s 899us/step - loss: 0.2371\n","Epoch 67/500\n","1/1 [==============================] - 0s 849us/step - loss: 0.2323\n","Epoch 68/500\n","1/1 [==============================] - 0s 876us/step - loss: 0.2275\n","Epoch 69/500\n","1/1 [==============================] - 0s 882us/step - loss: 0.2228\n","Epoch 70/500\n","1/1 [==============================] - 0s 870us/step - loss: 0.2182\n","Epoch 71/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2138\n","Epoch 72/500\n","1/1 [==============================] - 0s 4ms/step - loss: 0.2094\n","Epoch 73/500\n","1/1 [==============================] - 0s 840us/step - loss: 0.2051\n","Epoch 74/500\n","1/1 [==============================] - 0s 893us/step - loss: 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[==============================] - 0s 2ms/step - loss: 0.0197\n","Epoch 187/500\n","1/1 [==============================] - 0s 984us/step - loss: 0.0192\n","Epoch 188/500\n","1/1 [==============================] - 0s 927us/step - loss: 0.0189\n","Epoch 189/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0185\n","Epoch 190/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0181\n","Epoch 191/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0177\n","Epoch 192/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0173\n","Epoch 193/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0170\n","Epoch 194/500\n","1/1 [==============================] - 0s 985us/step - loss: 0.0166\n","Epoch 195/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0163\n","Epoch 196/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0160\n","Epoch 197/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0156\n","Epoch 198/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0153\n","Epoch 199/500\n","1/1 [==============================] - 0s 951us/step - loss: 0.0150\n","Epoch 200/500\n","1/1 [==============================] - 0s 901us/step - loss: 0.0147\n","Epoch 201/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0144\n","Epoch 202/500\n","1/1 [==============================] - 0s 934us/step - loss: 0.0141\n","Epoch 203/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0138\n","Epoch 204/500\n","1/1 [==============================] - 0s 932us/step - loss: 0.0135\n","Epoch 205/500\n","1/1 [==============================] - 0s 899us/step - loss: 0.0132\n","Epoch 206/500\n","1/1 [==============================] - 0s 993us/step - loss: 0.0130\n","Epoch 207/500\n","1/1 [==============================] - 0s 928us/step - loss: 0.0127\n","Epoch 208/500\n","1/1 [==============================] - 0s 843us/step - loss: 0.0124\n","Epoch 209/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0122\n","Epoch 210/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0119\n","Epoch 211/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0117\n","Epoch 212/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0115\n","Epoch 213/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0112\n","Epoch 214/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0110\n","Epoch 215/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0108\n","Epoch 216/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0105\n","Epoch 217/500\n","1/1 [==============================] - 0s 907us/step - loss: 0.0103\n","Epoch 218/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0101\n","Epoch 219/500\n","1/1 [==============================] - 0s 856us/step - loss: 0.0099\n","Epoch 220/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0097\n","Epoch 221/500\n","1/1 [==============================] - 0s 890us/step - loss: 0.0095\n","Epoch 222/500\n","1/1 [==============================] - 0s 989us/step - loss: 0.0093\n","Epoch 223/500\n","1/1 [==============================] - 0s 886us/step - loss: 0.0091\n","Epoch 224/500\n","1/1 [==============================] - 0s 855us/step - loss: 0.0089\n","Epoch 225/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0087\n","Epoch 226/500\n","1/1 [==============================] - 0s 982us/step - loss: 0.0086\n","Epoch 227/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0084\n","Epoch 228/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0082\n","Epoch 229/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0080\n","Epoch 230/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0079\n","Epoch 231/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0077\n","Epoch 232/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0076\n","Epoch 233/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0074\n","Epoch 234/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0073\n","Epoch 235/500\n","1/1 [==============================] - 0s 999us/step - loss: 0.0071\n","Epoch 236/500\n","1/1 [==============================] - 0s 3ms/step - loss: 0.0070\n","Epoch 237/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0068\n","Epoch 238/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0067\n","Epoch 239/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0065\n","Epoch 240/500\n","1/1 [==============================] - 0s 6ms/step - loss: 0.0064\n","Epoch 241/500\n","1/1 [==============================] - 0s 955us/step - loss: 0.0063\n","Epoch 242/500\n","1/1 [==============================] - 0s 943us/step - loss: 0.0061\n","Epoch 243/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0060\n","Epoch 244/500\n","1/1 [==============================] - 0s 851us/step - loss: 0.0059\n","Epoch 245/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0058\n","Epoch 246/500\n","1/1 [==============================] - 0s 886us/step - loss: 0.0057\n","Epoch 247/500\n","1/1 [==============================] - 0s 937us/step - loss: 0.0055\n","Epoch 248/500\n","1/1 [==============================] - 0s 999us/step - loss: 0.0054\n","Epoch 249/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0053\n","Epoch 250/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0052\n","Epoch 251/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0051\n","Epoch 252/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0050\n","Epoch 253/500\n","1/1 [==============================] - 0s 5ms/step - loss: 0.0049\n","Epoch 254/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0048\n","Epoch 255/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0047\n","Epoch 256/500\n","1/1 [==============================] - 0s 982us/step - loss: 0.0046\n","Epoch 257/500\n","1/1 [==============================] - 0s 979us/step - loss: 0.0045\n","Epoch 258/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0044\n","Epoch 259/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0043\n","Epoch 260/500\n","1/1 [==============================] - 0s 997us/step - loss: 0.0042\n","Epoch 261/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0041\n","Epoch 262/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0041\n","Epoch 263/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0040\n","Epoch 264/500\n","1/1 [==============================] - 0s 973us/step - loss: 0.0039\n","Epoch 265/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0038\n","Epoch 266/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0037\n","Epoch 267/500\n","1/1 [==============================] - 0s 921us/step - loss: 0.0037\n","Epoch 268/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0036\n","Epoch 269/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0035\n","Epoch 270/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0034\n","Epoch 271/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0034\n","Epoch 272/500\n","1/1 [==============================] - 0s 980us/step - loss: 0.0033\n","Epoch 273/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0032\n","Epoch 274/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0032\n","Epoch 275/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0031\n","Epoch 276/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0030\n","Epoch 277/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0030\n","Epoch 278/500\n","1/1 [==============================] - 0s 928us/step - loss: 0.0029\n","Epoch 279/500\n","1/1 [==============================] - 0s 923us/step - loss: 0.0029\n","Epoch 280/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0028\n","Epoch 281/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0027\n","Epoch 282/500\n","1/1 [==============================] - 0s 938us/step - loss: 0.0027\n","Epoch 283/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0026\n","Epoch 284/500\n","1/1 [==============================] - 0s 989us/step - loss: 0.0026\n","Epoch 285/500\n","1/1 [==============================] - 0s 3ms/step - loss: 0.0025\n","Epoch 286/500\n","1/1 [==============================] - 0s 4ms/step - loss: 0.0025\n","Epoch 287/500\n","1/1 [==============================] - 0s 3ms/step - loss: 0.0024\n","Epoch 288/500\n","1/1 [==============================] - 0s 996us/step - loss: 0.0024\n","Epoch 289/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0023\n","Epoch 290/500\n","1/1 [==============================] - 0s 995us/step - loss: 0.0023\n","Epoch 291/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0022\n","Epoch 292/500\n","1/1 [==============================] - 0s 931us/step - loss: 0.0022\n","Epoch 293/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n","Epoch 294/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0021\n","Epoch 295/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0020\n","Epoch 296/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n","Epoch 297/500\n","1/1 [==============================] - 0s 859us/step - loss: 0.0020\n","Epoch 298/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0019\n","Epoch 299/500\n","1/1 [==============================] - 0s 858us/step - loss: 0.0019\n","Epoch 300/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0018\n","Epoch 301/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0018\n","Epoch 302/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0018\n","Epoch 303/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0017\n","Epoch 304/500\n","1/1 [==============================] - 0s 878us/step - loss: 0.0017\n","Epoch 305/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0017\n","Epoch 306/500\n","1/1 [==============================] - 0s 939us/step - loss: 0.0016\n","Epoch 307/500\n","1/1 [==============================] - 0s 860us/step - loss: 0.0016\n","Epoch 308/500\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n","Epoch 309/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0015\n","Epoch 310/500\n","1/1 [==============================] - 0s 912us/step - loss: 0.0015\n","Epoch 311/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0015\n","Epoch 312/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n","Epoch 313/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n","Epoch 314/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n","Epoch 315/500\n","1/1 [==============================] - 0s 875us/step - loss: 0.0014\n","Epoch 316/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0013\n","Epoch 317/500\n","1/1 [==============================] - 0s 941us/step - loss: 0.0013\n","Epoch 318/500\n","1/1 [==============================] - 0s 985us/step - loss: 0.0013\n","Epoch 319/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0012\n","Epoch 320/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0012\n","Epoch 321/500\n","1/1 [==============================] - 0s 995us/step - loss: 0.0012\n","Epoch 322/500\n","1/1 [==============================] - 0s 891us/step - loss: 0.0012\n","Epoch 323/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0011\n","Epoch 324/500\n","1/1 [==============================] - 0s 926us/step - loss: 0.0011\n","Epoch 325/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0011\n","Epoch 326/500\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0011\n","Epoch 327/500\n","1/1 [==============================] - 0s 959us/step - loss: 0.0011\n","Epoch 328/500\n","1/1 [==============================] - 0s 833us/step - loss: 0.0010\n","Epoch 329/500\n","1/1 [==============================] - 0s 923us/step - loss: 0.0010\n","Epoch 330/500\n","1/1 [==============================] - 0s 1ms/step - loss: 9.8950e-04\n","Epoch 331/500\n","1/1 [==============================] - 0s 995us/step - loss: 9.6918e-04\n","Epoch 332/500\n","1/1 [==============================] - 0s 886us/step - loss: 9.4927e-04\n","Epoch 333/500\n","1/1 [==============================] - 0s 951us/step - loss: 9.2977e-04\n","Epoch 334/500\n","1/1 [==============================] - 0s 994us/step - loss: 9.1067e-04\n","Epoch 335/500\n","1/1 [==============================] - 0s 884us/step - loss: 8.9196e-04\n","Epoch 336/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.7364e-04\n","Epoch 337/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.5569e-04\n","Epoch 338/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.3812e-04\n","Epoch 339/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.2090e-04\n","Epoch 340/500\n","1/1 [==============================] - 0s 966us/step - loss: 8.0404e-04\n","Epoch 341/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.8753e-04\n","Epoch 342/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.7135e-04\n","Epoch 343/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.5551e-04\n","Epoch 344/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.3999e-04\n","Epoch 345/500\n","1/1 [==============================] - 0s 6ms/step - loss: 7.2479e-04\n","Epoch 346/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.0990e-04\n","Epoch 347/500\n","1/1 [==============================] - 0s 3ms/step - loss: 6.9532e-04\n","Epoch 348/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.8103e-04\n","Epoch 349/500\n","1/1 [==============================] - 0s 920us/step - loss: 6.6704e-04\n","Epoch 350/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.5334e-04\n","Epoch 351/500\n","1/1 [==============================] - 0s 874us/step - loss: 6.3992e-04\n","Epoch 352/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.2678e-04\n","Epoch 353/500\n","1/1 [==============================] - 0s 2ms/step - loss: 6.1390e-04\n","Epoch 354/500\n","1/1 [==============================] - 0s 993us/step - loss: 6.0129e-04\n","Epoch 355/500\n","1/1 [==============================] - 0s 975us/step - loss: 5.8894e-04\n","Epoch 356/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.7684e-04\n","Epoch 357/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.6500e-04\n","Epoch 358/500\n","1/1 [==============================] - 0s 2ms/step - loss: 5.5339e-04\n","Epoch 359/500\n","1/1 [==============================] - 0s 2ms/step - loss: 5.4202e-04\n","Epoch 360/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.3089e-04\n","Epoch 361/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.1999e-04\n","Epoch 362/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.0930e-04\n","Epoch 363/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.9884e-04\n","Epoch 364/500\n","1/1 [==============================] - 0s 978us/step - loss: 4.8860e-04\n","Epoch 365/500\n","1/1 [==============================] - 0s 2ms/step - loss: 4.7856e-04\n","Epoch 366/500\n","1/1 [==============================] - 0s 2ms/step - loss: 4.6873e-04\n","Epoch 367/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.5910e-04\n","Epoch 368/500\n","1/1 [==============================] - 0s 2ms/step - loss: 4.4967e-04\n","Epoch 369/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.4044e-04\n","Epoch 370/500\n","1/1 [==============================] - 0s 2ms/step - loss: 4.3139e-04\n","Epoch 371/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.2253e-04\n","Epoch 372/500\n","1/1 [==============================] - 0s 2ms/step - loss: 4.1385e-04\n","Epoch 373/500\n","1/1 [==============================] - 0s 6ms/step - loss: 4.0535e-04\n","Epoch 374/500\n","1/1 [==============================] - 0s 2ms/step - loss: 3.9703e-04\n","Epoch 375/500\n","1/1 [==============================] - 0s 3ms/step - loss: 3.8887e-04\n","Epoch 376/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.8088e-04\n","Epoch 377/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.7306e-04\n","Epoch 378/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.6539e-04\n","Epoch 379/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.5789e-04\n","Epoch 380/500\n","1/1 [==============================] - 0s 2ms/step - loss: 3.5054e-04\n","Epoch 381/500\n","1/1 [==============================] - 0s 3ms/step - loss: 3.4334e-04\n","Epoch 382/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.3629e-04\n","Epoch 383/500\n","1/1 [==============================] - 0s 896us/step - loss: 3.2938e-04\n","Epoch 384/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.2261e-04\n","Epoch 385/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.1599e-04\n","Epoch 386/500\n","1/1 [==============================] - 0s 877us/step - loss: 3.0949e-04\n","Epoch 387/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.0314e-04\n","Epoch 388/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.9691e-04\n","Epoch 389/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.9081e-04\n","Epoch 390/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.8484e-04\n","Epoch 391/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.7899e-04\n","Epoch 392/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.7326e-04\n","Epoch 393/500\n","1/1 [==============================] - 0s 2ms/step - loss: 2.6765e-04\n","Epoch 394/500\n","1/1 [==============================] - 0s 2ms/step - loss: 2.6215e-04\n","Epoch 395/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.5676e-04\n","Epoch 396/500\n","1/1 [==============================] - 0s 2ms/step - loss: 2.5149e-04\n","Epoch 397/500\n","1/1 [==============================] - 0s 3ms/step - loss: 2.4633e-04\n","Epoch 398/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.4127e-04\n","Epoch 399/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.3631e-04\n","Epoch 400/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.3145e-04\n","Epoch 401/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.2670e-04\n","Epoch 402/500\n","1/1 [==============================] - 0s 917us/step - loss: 2.2204e-04\n","Epoch 403/500\n","1/1 [==============================] - 0s 927us/step - loss: 2.1748e-04\n","Epoch 404/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.1301e-04\n","Epoch 405/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.0864e-04\n","Epoch 406/500\n","1/1 [==============================] - 0s 886us/step - loss: 2.0435e-04\n","Epoch 407/500\n","1/1 [==============================] - 0s 2ms/step - loss: 2.0015e-04\n","Epoch 408/500\n","1/1 [==============================] - 0s 1ms/step - loss: 1.9604e-04\n","Epoch 409/500\n","1/1 [==============================] - 0s 901us/step - loss: 1.9202e-04\n","Epoch 410/500\n","1/1 [==============================] - 0s 933us/step - loss: 1.8807e-04\n","Epoch 411/500\n","1/1 [==============================] - 0s 909us/step - loss: 1.8421e-04\n","Epoch 412/500\n","1/1 [==============================] - 0s 931us/step - loss: 1.8043e-04\n","Epoch 413/500\n","1/1 [==============================] - 0s 2ms/step - loss: 1.7672e-04\n","Epoch 414/500\n","1/1 [==============================] - 0s 2ms/step - loss: 1.7309e-04\n","Epoch 415/500\n","1/1 [==============================] - 0s 3ms/step - loss: 1.6953e-04\n","Epoch 416/500\n","1/1 [==============================] - 0s 857us/step - loss: 1.6605e-04\n","Epoch 417/500\n","1/1 [==============================] - 0s 1ms/step - loss: 1.6264e-04\n","Epoch 418/500\n","1/1 [==============================] - 0s 999us/step - loss: 1.5930e-04\n","Epoch 419/500\n","1/1 [==============================] - 0s 922us/step - loss: 1.5603e-04\n","Epoch 420/500\n","1/1 [==============================] - 0s 1ms/step - loss: 1.5282e-04\n","Epoch 421/500\n","1/1 [==============================] - 0s 907us/step - loss: 1.4969e-04\n","Epoch 422/500\n","1/1 [==============================] - 0s 951us/step - loss: 1.4661e-04\n","Epoch 423/500\n","1/1 [==============================] - 0s 921us/step - loss: 1.4360e-04\n","Epoch 424/500\n","1/1 [==============================] - 0s 977us/step - loss: 1.4065e-04\n","Epoch 425/500\n","1/1 [==============================] - 0s 2ms/step - loss: 1.3776e-04\n","Epoch 426/500\n","1/1 [==============================] - 0s 938us/step - loss: 1.3493e-04\n","Epoch 427/500\n","1/1 [==============================] - 0s 932us/step - loss: 1.3216e-04\n","Epoch 428/500\n","1/1 [==============================] - 0s 1ms/step - loss: 1.2944e-04\n","Epoch 429/500\n","1/1 [==============================] - 0s 932us/step - loss: 1.2679e-04\n","Epoch 430/500\n","1/1 [==============================] - 0s 1ms/step - loss: 1.2418e-04\n","Epoch 431/500\n","1/1 [==============================] - 0s 1ms/step - loss: 1.2163e-04\n","Epoch 432/500\n","1/1 [==============================] - 0s 2ms/step - loss: 1.1913e-04\n","Epoch 433/500\n","1/1 [==============================] - 0s 1ms/step - loss: 1.1669e-04\n","Epoch 434/500\n","1/1 [==============================] - 0s 3ms/step - loss: 1.1429e-04\n","Epoch 435/500\n","1/1 [==============================] - 0s 2ms/step - loss: 1.1194e-04\n","Epoch 436/500\n","1/1 [==============================] - 0s 935us/step - loss: 1.0964e-04\n","Epoch 437/500\n","1/1 [==============================] - 0s 888us/step - loss: 1.0739e-04\n","Epoch 438/500\n","1/1 [==============================] - 0s 2ms/step - loss: 1.0519e-04\n","Epoch 439/500\n","1/1 [==============================] - 0s 942us/step - loss: 1.0303e-04\n","Epoch 440/500\n","1/1 [==============================] - 0s 913us/step - loss: 1.0091e-04\n","Epoch 441/500\n","1/1 [==============================] - 0s 1ms/step - loss: 9.8835e-05\n","Epoch 442/500\n","1/1 [==============================] - 0s 906us/step - loss: 9.6805e-05\n","Epoch 443/500\n","1/1 [==============================] - 0s 2ms/step - loss: 9.4817e-05\n","Epoch 444/500\n","1/1 [==============================] - 0s 908us/step - loss: 9.2869e-05\n","Epoch 445/500\n","1/1 [==============================] - 0s 1ms/step - loss: 9.0962e-05\n","Epoch 446/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.9093e-05\n","Epoch 447/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.7263e-05\n","Epoch 448/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.5471e-05\n","Epoch 449/500\n","1/1 [==============================] - 0s 4ms/step - loss: 8.3715e-05\n","Epoch 450/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.1996e-05\n","Epoch 451/500\n","1/1 [==============================] - 0s 1ms/step - loss: 8.0312e-05\n","Epoch 452/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.8662e-05\n","Epoch 453/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.7046e-05\n","Epoch 454/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.5463e-05\n","Epoch 455/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.3913e-05\n","Epoch 456/500\n","1/1 [==============================] - 0s 1ms/step - loss: 7.2395e-05\n","Epoch 457/500\n","1/1 [==============================] - 0s 2ms/step - loss: 7.0907e-05\n","Epoch 458/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.9452e-05\n","Epoch 459/500\n","1/1 [==============================] - 0s 2ms/step - loss: 6.8025e-05\n","Epoch 460/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.6628e-05\n","Epoch 461/500\n","1/1 [==============================] - 0s 2ms/step - loss: 6.5260e-05\n","Epoch 462/500\n","1/1 [==============================] - 0s 2ms/step - loss: 6.3919e-05\n","Epoch 463/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.2607e-05\n","Epoch 464/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.1320e-05\n","Epoch 465/500\n","1/1 [==============================] - 0s 1ms/step - loss: 6.0060e-05\n","Epoch 466/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.8827e-05\n","Epoch 467/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.7618e-05\n","Epoch 468/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.6434e-05\n","Epoch 469/500\n","1/1 [==============================] - 0s 996us/step - loss: 5.5275e-05\n","Epoch 470/500\n","1/1 [==============================] - 0s 2ms/step - loss: 5.4139e-05\n","Epoch 471/500\n","1/1 [==============================] - 0s 2ms/step - loss: 5.3027e-05\n","Epoch 472/500\n","1/1 [==============================] - 0s 970us/step - loss: 5.1939e-05\n","Epoch 473/500\n","1/1 [==============================] - 0s 1ms/step - loss: 5.0871e-05\n","Epoch 474/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.9827e-05\n","Epoch 475/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.8802e-05\n","Epoch 476/500\n","1/1 [==============================] - 0s 2ms/step - loss: 4.7801e-05\n","Epoch 477/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.6819e-05\n","Epoch 478/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.5857e-05\n","Epoch 479/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.4915e-05\n","Epoch 480/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.3992e-05\n","Epoch 481/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.3088e-05\n","Epoch 482/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.2203e-05\n","Epoch 483/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.1337e-05\n","Epoch 484/500\n","1/1 [==============================] - 0s 1ms/step - loss: 4.0487e-05\n","Epoch 485/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.9656e-05\n","Epoch 486/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.8841e-05\n","Epoch 487/500\n","1/1 [==============================] - 0s 2ms/step - loss: 3.8043e-05\n","Epoch 488/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.7262e-05\n","Epoch 489/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.6496e-05\n","Epoch 490/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.5747e-05\n","Epoch 491/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.5012e-05\n","Epoch 492/500\n","1/1 [==============================] - 0s 7ms/step - loss: 3.4293e-05\n","Epoch 493/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.3588e-05\n","Epoch 494/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.2898e-05\n","Epoch 495/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.2223e-05\n","Epoch 496/500\n","1/1 [==============================] - 0s 2ms/step - loss: 3.1561e-05\n","Epoch 497/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.0912e-05\n","Epoch 498/500\n","1/1 [==============================] - 0s 1ms/step - loss: 3.0277e-05\n","Epoch 499/500\n","1/1 [==============================] - 0s 2ms/step - loss: 2.9656e-05\n","Epoch 500/500\n","1/1 [==============================] - 0s 1ms/step - loss: 2.9047e-05\n"]},{"data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7f3cc985a358>"]},"execution_count":4,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["import tensorflow as tf\n","\n","# store data for x and y\n","x = [-1,0,1,2,3,4]\n","y = [-3,-1,1,3,5,7]\n","\n","# create a simple keras model\n","model = tf.keras.models.Sequential(\n","    tf.keras.layers.Dense(units=1, input_shape=[1]))\n","model.compile(optimizer='sgd', loss='mean_squared_error')\n","model.fit(x, y, epochs=500)"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":68},"colab_type":"code","executionInfo":{"elapsed":3730,"status":"ok","timestamp":1589835830752,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"PIjRqdh9k_MJ","outputId":"959e9659-bc28-4300-ca0b-6e94ac2f2e41"},"outputs":[{"name":"stdout","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/saved_model/assets\n"]},{"name":"stderr","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/saved_model/assets\n"]},{"data":{"text/plain":["640"]},"execution_count":6,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["import pathlib\n","\n","# export the savedmodel\n","export_dir = '/content/saved_model'\n","tf.saved_model.save(model, export_dir)\n","\n","# convert the model\n","converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n","tflite_model = converter.convert()\n","\n","# save the model\n","tflite_model_file = pathlib.Path('/content/foo.tflite')\n","tflite_model_file.write_bytes(tflite_model)"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":88},"colab_type":"code","executionInfo":{"elapsed":17232,"status":"ok","timestamp":1589836238693,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"QkEGtfeBmVwt","outputId":"13201d1e-dce6-48ca-bc0e-1f91ebc5e979"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224.h5\n","14540800/14536120 [==============================] - 1s 0us/step\n"]},{"data":{"text/plain":["13971160"]},"execution_count":7,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["### Alternative - Directly convert keras model into tflite model ###\n","\n","import tensorflow as tf\n","import pathlib\n","\n","# load the MobileNet tf.keras.model\n","model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224,224,3))\n","\n","# convert the model\n","converter = tf.lite.TFLiteConverter.from_keras_model(model)\n","tflite_model = converter.convert()\n","\n","# save the model\n","tflite_model_file = pathlib.Path('/content/foo2.tflite')\n","tflite_model_file.write_bytes(tflite_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"HS5Q2w6cn4tj"},"outputs":[],"source":["# quantize only the weights\n","converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n","tflite_quant_model  = converter.convert()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"56BaHnkktOMK"},"source":["## Official"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"fVD6_lJntPoV"},"outputs":[],"source":["import tensorflow as tf\n","\n","import pathlib\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Input"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"gGMMEO4YtVzv"},"source":["### Create a basic model of the form y = mx + c"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"colab_type":"code","executionInfo":{"elapsed":6117,"status":"ok","timestamp":1589838043981,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"nhVjv6yOtSgk","outputId":"8e0d099d-feac-41d5-a37e-19343cf207a6"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/200\n","1/1 [==============================] - 0s 1ms/step - loss: 2.4523\n","Epoch 2/200\n","1/1 [==============================] - 0s 1ms/step - loss: 2.0789\n","Epoch 3/200\n","1/1 [==============================] - 0s 1ms/step - loss: 1.7821\n","Epoch 4/200\n","1/1 [==============================] - 0s 867us/step - loss: 1.5456\n","Epoch 5/200\n","1/1 [==============================] - 0s 881us/step - loss: 1.3566\n","Epoch 6/200\n","1/1 [==============================] - 0s 1ms/step - loss: 1.2050\n","Epoch 7/200\n","1/1 [==============================] - 0s 1ms/step - loss: 1.0829\n","Epoch 8/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.9841\n","Epoch 9/200\n","1/1 [==============================] - 0s 954us/step - loss: 0.9036\n","Epoch 10/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.8376\n","Epoch 11/200\n","1/1 [==============================] - 0s 957us/step - loss: 0.7831\n","Epoch 12/200\n","1/1 [==============================] - 0s 938us/step - loss: 0.7377\n","Epoch 13/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.6995\n","Epoch 14/200\n","1/1 [==============================] - 0s 969us/step - loss: 0.6669\n","Epoch 15/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.6389\n","Epoch 16/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.6146\n","Epoch 17/200\n","1/1 [==============================] - 0s 939us/step - loss: 0.5931\n","Epoch 18/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.5740\n","Epoch 19/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.5567\n","Epoch 20/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.5409\n","Epoch 21/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.5264\n","Epoch 22/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.5130\n","Epoch 23/200\n","1/1 [==============================] - 0s 879us/step - loss: 0.5003\n","Epoch 24/200\n","1/1 [==============================] - 0s 916us/step - loss: 0.4884\n","Epoch 25/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.4771\n","Epoch 26/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.4663\n","Epoch 27/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.4559\n","Epoch 28/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.4459\n","Epoch 29/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.4362\n","Epoch 30/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.4269\n","Epoch 31/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.4178\n","Epoch 32/200\n","1/1 [==============================] - 0s 959us/step - loss: 0.4090\n","Epoch 33/200\n","1/1 [==============================] - 0s 939us/step - loss: 0.4004\n","Epoch 34/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.3920\n","Epoch 35/200\n","1/1 [==============================] - 0s 837us/step - loss: 0.3838\n","Epoch 36/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.3759\n","Epoch 37/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.3681\n","Epoch 38/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.3604\n","Epoch 39/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.3530\n","Epoch 40/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.3457\n","Epoch 41/200\n","1/1 [==============================] - 0s 881us/step - loss: 0.3386\n","Epoch 42/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.3316\n","Epoch 43/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.3248\n","Epoch 44/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.3181\n","Epoch 45/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.3115\n","Epoch 46/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.3051\n","Epoch 47/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2989\n","Epoch 48/200\n","1/1 [==============================] - 0s 941us/step - loss: 0.2927\n","Epoch 49/200\n","1/1 [==============================] - 0s 910us/step - loss: 0.2867\n","Epoch 50/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2808\n","Epoch 51/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2750\n","Epoch 52/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2694\n","Epoch 53/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2639\n","Epoch 54/200\n","1/1 [==============================] - 0s 3ms/step - loss: 0.2584\n","Epoch 55/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2531\n","Epoch 56/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2479\n","Epoch 57/200\n","1/1 [==============================] - 0s 905us/step - loss: 0.2428\n","Epoch 58/200\n","1/1 [==============================] - 0s 3ms/step - loss: 0.2378\n","Epoch 59/200\n","1/1 [==============================] - 0s 892us/step - loss: 0.2330\n","Epoch 60/200\n","1/1 [==============================] - 0s 921us/step - loss: 0.2282\n","Epoch 61/200\n","1/1 [==============================] - 0s 922us/step - loss: 0.2235\n","Epoch 62/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2189\n","Epoch 63/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2144\n","Epoch 64/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2100\n","Epoch 65/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.2057\n","Epoch 66/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.2015\n","Epoch 67/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.1973\n","Epoch 68/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1933\n","Epoch 69/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.1893\n","Epoch 70/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1854\n","Epoch 71/200\n","1/1 [==============================] - 0s 842us/step - loss: 0.1816\n","Epoch 72/200\n","1/1 [==============================] - 0s 876us/step - loss: 0.1779\n","Epoch 73/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.1742\n","Epoch 74/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1706\n","Epoch 75/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1671\n","Epoch 76/200\n","1/1 [==============================] - 0s 920us/step - loss: 0.1637\n","Epoch 77/200\n","1/1 [==============================] - 0s 885us/step - loss: 0.1603\n","Epoch 78/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1570\n","Epoch 79/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1538\n","Epoch 80/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1507\n","Epoch 81/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1476\n","Epoch 82/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1445\n","Epoch 83/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.1416\n","Epoch 84/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1387\n","Epoch 85/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1358\n","Epoch 86/200\n","1/1 [==============================] - 0s 872us/step - loss: 0.1330\n","Epoch 87/200\n","1/1 [==============================] - 0s 4ms/step - loss: 0.1303\n","Epoch 88/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.1276\n","Epoch 89/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1250\n","Epoch 90/200\n","1/1 [==============================] - 0s 946us/step - loss: 0.1224\n","Epoch 91/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1199\n","Epoch 92/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1174\n","Epoch 93/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1150\n","Epoch 94/200\n","1/1 [==============================] - 0s 967us/step - loss: 0.1127\n","Epoch 95/200\n","1/1 [==============================] - 0s 852us/step - loss: 0.1104\n","Epoch 96/200\n","1/1 [==============================] - 0s 997us/step - loss: 0.1081\n","Epoch 97/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1059\n","Epoch 98/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.1037\n","Epoch 99/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.1016\n","Epoch 100/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0995\n","Epoch 101/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0974\n","Epoch 102/200\n","1/1 [==============================] - 0s 954us/step - loss: 0.0954\n","Epoch 103/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0935\n","Epoch 104/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0916\n","Epoch 105/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0897\n","Epoch 106/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0878\n","Epoch 107/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0860\n","Epoch 108/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0843\n","Epoch 109/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0825\n","Epoch 110/200\n","1/1 [==============================] - 0s 854us/step - loss: 0.0808\n","Epoch 111/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0792\n","Epoch 112/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0775\n","Epoch 113/200\n","1/1 [==============================] - 0s 856us/step - loss: 0.0760\n","Epoch 114/200\n","1/1 [==============================] - 0s 923us/step - loss: 0.0744\n","Epoch 115/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0729\n","Epoch 116/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0714\n","Epoch 117/200\n","1/1 [==============================] - 0s 4ms/step - loss: 0.0699\n","Epoch 118/200\n","1/1 [==============================] - 0s 871us/step - loss: 0.0685\n","Epoch 119/200\n","1/1 [==============================] - 0s 881us/step - loss: 0.0671\n","Epoch 120/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0657\n","Epoch 121/200\n","1/1 [==============================] - 0s 972us/step - loss: 0.0643\n","Epoch 122/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0630\n","Epoch 123/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0617\n","Epoch 124/200\n","1/1 [==============================] - 0s 878us/step - loss: 0.0604\n","Epoch 125/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0592\n","Epoch 126/200\n","1/1 [==============================] - 0s 876us/step - loss: 0.0580\n","Epoch 127/200\n","1/1 [==============================] - 0s 880us/step - loss: 0.0568\n","Epoch 128/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0556\n","Epoch 129/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0545\n","Epoch 130/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0534\n","Epoch 131/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0523\n","Epoch 132/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0512\n","Epoch 133/200\n","1/1 [==============================] - 0s 3ms/step - loss: 0.0501\n","Epoch 134/200\n","1/1 [==============================] - 0s 7ms/step - loss: 0.0491\n","Epoch 135/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0481\n","Epoch 136/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0471\n","Epoch 137/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0462\n","Epoch 138/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0452\n","Epoch 139/200\n","1/1 [==============================] - 0s 973us/step - loss: 0.0443\n","Epoch 140/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0434\n","Epoch 141/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0425\n","Epoch 142/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0416\n","Epoch 143/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0408\n","Epoch 144/200\n","1/1 [==============================] - 0s 3ms/step - loss: 0.0399\n","Epoch 145/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0391\n","Epoch 146/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0383\n","Epoch 147/200\n","1/1 [==============================] - 0s 978us/step - loss: 0.0375\n","Epoch 148/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0367\n","Epoch 149/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0360\n","Epoch 150/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0352\n","Epoch 151/200\n","1/1 [==============================] - 0s 856us/step - loss: 0.0345\n","Epoch 152/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0338\n","Epoch 153/200\n","1/1 [==============================] - 0s 927us/step - loss: 0.0331\n","Epoch 154/200\n","1/1 [==============================] - 0s 844us/step - loss: 0.0324\n","Epoch 155/200\n","1/1 [==============================] - 0s 919us/step - loss: 0.0318\n","Epoch 156/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0311\n","Epoch 157/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0305\n","Epoch 158/200\n","1/1 [==============================] - 0s 914us/step - loss: 0.0298\n","Epoch 159/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0292\n","Epoch 160/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0286\n","Epoch 161/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0280\n","Epoch 162/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0275\n","Epoch 163/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0269\n","Epoch 164/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0264\n","Epoch 165/200\n","1/1 [==============================] - 0s 986us/step - loss: 0.0258\n","Epoch 166/200\n","1/1 [==============================] - 0s 3ms/step - loss: 0.0253\n","Epoch 167/200\n","1/1 [==============================] - 0s 887us/step - loss: 0.0248\n","Epoch 168/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0243\n","Epoch 169/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0238\n","Epoch 170/200\n","1/1 [==============================] - 0s 950us/step - loss: 0.0233\n","Epoch 171/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0228\n","Epoch 172/200\n","1/1 [==============================] - 0s 965us/step - loss: 0.0223\n","Epoch 173/200\n","1/1 [==============================] - 0s 904us/step - loss: 0.0219\n","Epoch 174/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0214\n","Epoch 175/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0210\n","Epoch 176/200\n","1/1 [==============================] - 0s 926us/step - loss: 0.0205\n","Epoch 177/200\n","1/1 [==============================] - 0s 873us/step - loss: 0.0201\n","Epoch 178/200\n","1/1 [==============================] - 0s 933us/step - loss: 0.0197\n","Epoch 179/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0193\n","Epoch 180/200\n","1/1 [==============================] - 0s 874us/step - loss: 0.0189\n","Epoch 181/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0185\n","Epoch 182/200\n","1/1 [==============================] - 0s 915us/step - loss: 0.0181\n","Epoch 183/200\n","1/1 [==============================] - 0s 910us/step - loss: 0.0178\n","Epoch 184/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0174\n","Epoch 185/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0170\n","Epoch 186/200\n","1/1 [==============================] - 0s 987us/step - loss: 0.0167\n","Epoch 187/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0164\n","Epoch 188/200\n","1/1 [==============================] - 0s 864us/step - loss: 0.0160\n","Epoch 189/200\n","1/1 [==============================] - 0s 880us/step - loss: 0.0157\n","Epoch 190/200\n","1/1 [==============================] - 0s 865us/step - loss: 0.0154\n","Epoch 191/200\n","1/1 [==============================] - 0s 919us/step - loss: 0.0150\n","Epoch 192/200\n","1/1 [==============================] - 0s 871us/step - loss: 0.0147\n","Epoch 193/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0144\n","Epoch 194/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0141\n","Epoch 195/200\n","1/1 [==============================] - 0s 1ms/step - loss: 0.0138\n","Epoch 196/200\n","1/1 [==============================] - 0s 892us/step - loss: 0.0136\n","Epoch 197/200\n","1/1 [==============================] - 0s 2ms/step - loss: 0.0133\n","Epoch 198/200\n","1/1 [==============================] - 0s 872us/step - loss: 0.0130\n","Epoch 199/200\n","1/1 [==============================] - 0s 3ms/step - loss: 0.0127\n","Epoch 200/200\n","1/1 [==============================] - 0s 940us/step - loss: 0.0125\n","INFO:tensorflow:Assets written to: saved_model/1/assets\n"]},{"name":"stderr","output_type":"stream","text":["INFO:tensorflow:Assets written to: saved_model/1/assets\n"]}],"source":["# Create a simple Keras model.\n","x = [-1, 0, 1, 2, 3, 4]\n","y = [-3, -1, 1, 3, 5, 7]\n","\n","model = tf.keras.models.Sequential([\n","    tf.keras.layers.Dense(units=1, input_shape=[1])\n","])\n","model.compile(optimizer='sgd', loss='mean_squared_error')\n","model.fit(x, y, epochs=200, verbose=1)\n","\n","# Generate a SavedModel\n","export_dir = 'saved_model/1'\n","tf.saved_model.save(model, export_dir)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"uGejKISctxBH"},"source":["### Convert the SavedModel to TFLite"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"colab_type":"code","executionInfo":{"elapsed":6012,"status":"ok","timestamp":1589838051724,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"3MVATADst1fB","outputId":"bef70551-9dba-47e2-b439-4b359cb277d2"},"outputs":[{"data":{"text/plain":["656"]},"execution_count":13,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n","tflite_model = converter.convert()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"7GLHVSkCvfsz"},"outputs":[],"source":["tflite_model_file = pathlib.Path('model.tflite')\n","tflite_model_file.write_bytes(tflite_model)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"ZhOlP6l5tsh4"},"source":["### Initialize the TFLite interpreter to try it out"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"kCkZm7Hmttms"},"outputs":[],"source":["# Load TFLite model and allocate tensors.\n","interpreter = tf.lite.Interpreter(model_content=tflite_model)\n","interpreter.allocate_tensors()\n","\n","# Get input and output tensors.\n","input_details = interpreter.get_input_details()\n","output_details = interpreter.get_output_details()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"bkBEPjrGugXM"},"outputs":[],"source":["# Test the TensorFlow Lite model on random input data.\n","input_shape = input_details[0]['shape']\n","inputs, outputs = [], []\n","for _ in range(100):\n","  input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\n","  interpreter.set_tensor(input_details[0]['index'], input_data)\n","\n","  interpreter.invoke()\n","  tflite_results = interpreter.get_tensor(output_details[0]['index'])\n","\n","  # Test the TensorFlow model on random input data.\n","  tf_results = model(tf.constant(input_data))\n","  output_data = np.array(tf_results)\n","  \n","  inputs.append(input_data[0][0])\n","  outputs.append(output_data[0][0])"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"2qTw0Kx7uifX"},"source":["### Visualize the model"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"colab_type":"code","executionInfo":{"elapsed":1436,"status":"ok","timestamp":1589838059871,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"bYLShtBBukgn","outputId":"d6e88c8d-6b46-455d-9620-b8aea37b8936"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["plt.plot(inputs, outputs, 'r')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"R9e-Q3Obv3iL"},"source":["### Download the TFLite model file"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"Uo-yOq1Qu5M6"},"outputs":[],"source":["try:\n","  from google.colab import files\n","  files.download(tflite_model_file)\n","except:\n","  pass"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"upgftvpJwn4F"},"source":["## Cat vs. Dog with Transfer Learning and TFLite"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"bL54LWCHt5q5"},"source":["## Setup "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"dlauq-4FWGZM"},"outputs":[],"source":["import os\n","\n","import matplotlib.pylab as plt\n","import numpy as np\n","\n","import tensorflow as tf\n","import tensorflow_hub as hub\n","\n","print(\"Version: \", tf.__version__)\n","print(\"Eager mode: \", tf.executing_eagerly())\n","print(\"Hub version: \", hub.__version__)\n","print(\"GPU is\", \"available\" if tf.test.is_gpu_available() else \"NOT AVAILABLE\")"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"mmaHHH7Pvmth"},"source":["## Select the Hub/TF2 module to use\n","\n","Hub modules for TF 1.x won't work here, please use one of the selections provided."]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"both","colab":{},"colab_type":"code","id":"FlsEcKVeuCnf"},"outputs":[],"source":["module_selection = (\"mobilenet_v2\", 224, 1280) #@param [\"(\\\"mobilenet_v2\\\", 224, 1280)\", \"(\\\"inception_v3\\\", 299, 2048)\"] {type:\"raw\", allow-input: true}\n","handle_base, pixels, FV_SIZE = module_selection\n","MODULE_HANDLE =\"https://tfhub.dev/google/tf2-preview/{}/feature_vector/4\".format(handle_base)\n","IMAGE_SIZE = (pixels, pixels)\n","print(\"Using {} with input size {} and output dimension {}\".format(\n","  MODULE_HANDLE, IMAGE_SIZE, FV_SIZE))"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"sYUsgwCBv87A"},"source":["## Data preprocessing"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"8nqVX3KYwGPh"},"source":["Use [TensorFlow Datasets](http://tensorflow.org/datasets) to load the cats and dogs dataset.\n","\n","This `tfds` package is the easiest way to load pre-defined data. If you have your own data, and are interested in importing using it with TensorFlow see [loading image data](../load_data/images.ipynb)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"jGvpkDj4wBup"},"outputs":[],"source":["import tensorflow_datasets as tfds\n","tfds.disable_progress_bar()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"YkF4Boe5wN7N"},"source":["The `tfds.load` method downloads and caches the data, and returns a `tf.data.Dataset` object. These objects provide powerful, efficient methods for manipulating data and piping it into your model.\n","\n","Since `\"cats_vs_dog\"` doesn't define standard splits, use the subsplit feature to divide it into (train, validation, test) with 80%, 10%, 10% of the data respectively."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"SQ9xK9F2wGD8"},"outputs":[],"source":["(train_examples, validation_examples, test_examples), info = tfds.load(\n","    'cats_vs_dogs',\n","    split=['train[80%:]', 'train[80%:90%]', 'train[90%:]'],\n","    with_info=True, \n","    as_supervised=True, \n",")\n","\n","num_examples = info.splits['train'].num_examples\n","num_classes = info.features['label'].num_classes"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"pmXQYXNWwf19"},"source":["### Format the Data\n","\n","Use the `tf.image` module to format the images for the task.\n","\n","Resize the images to a fixes input size, and rescale the input channels"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"y7UyXblSwkUS"},"outputs":[],"source":["def format_image(image, label):\n","  image = tf.image.resize(image, IMAGE_SIZE) / 255.0\n","  return  image, label"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"1nrDR8CnwrVk"},"source":["Now shuffle and batch the data\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"both","colab":{},"colab_type":"code","id":"zAEUG7vawxLm"},"outputs":[],"source":["BATCH_SIZE = 32 #@param {type:\"integer\"}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"fHEC9mbswxvM"},"outputs":[],"source":["train_batches = train_examples.shuffle(num_examples // 4).map(format_image).batch(BATCH_SIZE).prefetch(1)\n","validation_batches = validation_examples.map(format_image).batch(BATCH_SIZE).prefetch(1)\n","test_batches = test_examples.map(format_image).batch(1)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"ghQhZjgEw1cK"},"source":["Inspect a batch"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"gz0xsMCjwx54"},"outputs":[],"source":["for image_batch, label_batch in train_batches.take(1):\n","  pass\n","\n","image_batch.shape"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"FS_gVStowW3G"},"source":["## Defining the model\n","\n","All it takes is to put a linear classifier on top of the `feature_extractor_layer` with the Hub module.\n","\n","For speed, we start out with a non-trainable `feature_extractor_layer`, but you can also enable fine-tuning for greater accuracy."]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{},"colab_type":"code","id":"RaJW3XrPyFiF"},"outputs":[],"source":["do_fine_tuning = True #@param {type:\"boolean\"}"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"wd0KfstqaUmE"},"source":["Load TFHub Module"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"svvDrt3WUrrm"},"outputs":[],"source":["feature_extractor = hub.KerasLayer(MODULE_HANDLE,\n","                                   input_shape=IMAGE_SIZE + (3,), \n","                                   output_shape=[FV_SIZE],\n","                                   trainable=do_fine_tuning)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"50FYNIb1dmJH"},"outputs":[],"source":["print(\"Building model with\", MODULE_HANDLE)\n","model = tf.keras.Sequential([\n","    feature_extractor,\n","    tf.keras.layers.Dense(num_classes)\n","])\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{},"colab_type":"code","id":"1PzLoQK0Zadv"},"outputs":[],"source":["#@title (Optional) Unfreeze some layers\n","NUM_LAYERS = 7 #@param {type:\"slider\", min:1, max:50, step:1}\n","      \n","if do_fine_tuning:\n","  feature_extractor.trainable = True\n","  \n","  for layer in model.layers[-NUM_LAYERS:]:\n","    layer.trainable = True\n","\n","else:\n","  feature_extractor.trainable = False"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"u2e5WupIw2N2"},"source":["## Training the model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"9f3yBUvkd_VJ"},"outputs":[],"source":["if do_fine_tuning:\n","  model.compile(\n","    optimizer=tf.keras.optimizers.SGD(lr=0.002, momentum=0.9), \n","    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n","    metrics=['accuracy'])\n","else:\n","  model.compile(\n","    optimizer='adam', \n","    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n","    metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"w_YKX2Qnfg6x"},"outputs":[],"source":["EPOCHS = 5\n","hist = model.fit(train_batches,\n","                    epochs=EPOCHS,\n","                    validation_data=validation_batches)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"u_psFoTeLpHU"},"source":["## Export the model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"XaSb5nVzHcVv"},"outputs":[],"source":["CATS_VS_DOGS_SAVED_MODEL = \"exp_saved_model\""]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"fZqRAg1uz1Nu"},"source":["Export the SavedModel"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"yJMue5YgnwtN"},"outputs":[],"source":["tf.saved_model.save(model, CATS_VS_DOGS_SAVED_MODEL)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"SOQF4cOan0SY"},"outputs":[],"source":["%%bash -s $CATS_VS_DOGS_SAVED_MODEL\n","saved_model_cli show --dir $1 --tag_set serve --signature_def serving_default"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"FY7QGBgBytwX"},"outputs":[],"source":["loaded = tf.saved_model.load(CATS_VS_DOGS_SAVED_MODEL)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"tIhPyMISz952"},"outputs":[],"source":["print(list(loaded.signatures.keys()))\n","infer = loaded.signatures[\"serving_default\"]\n","print(infer.structured_input_signature)\n","print(infer.structured_outputs)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"XxLiLC8n0H16"},"source":["## Convert using TFLite's Converter"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"1aUYvCpfWmrQ"},"source":["Load the TFLiteConverter with the SavedModel"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"dqJRyIg8Wl1n"},"outputs":[],"source":["converter = tf.lite.TFLiteConverter.from_saved_model(CATS_VS_DOGS_SAVED_MODEL)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"AudcNjT0UtfF"},"source":["### Post-training quantization\n","The simplest form of post-training quantization quantizes weights from floating point to 8-bits of precision. This technique is enabled as an option in the TensorFlow Lite converter. At inference, weights are converted from 8-bits of precision to floating point and computed using floating-point kernels. This conversion is done once and cached to reduce latency.\n","\n","To further improve latency, hybrid operators dynamically quantize activations to 8-bits and perform computations with 8-bit weights and activations. This optimization provides latencies close to fully fixed-point inference. However, the outputs are still stored using floating point, so that the speedup with hybrid ops is less than a full fixed-point computation."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"WmSr2-yZoUhz"},"outputs":[],"source":["converter.optimizations = [tf.lite.Optimize.DEFAULT]"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"YpCijI08UxP0"},"source":["### Post-training integer quantization\n","We can get further latency improvements, reductions in peak memory usage, and access to integer only hardware accelerators by making sure all model math is quantized. To do this, we need to measure the dynamic range of activations and inputs with a representative data set. You can simply create an input data generator and provide it to our converter."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"clM_dTIkWdIa"},"outputs":[],"source":["def representative_data_gen():\n","  for input_value, _ in test_batches.take(100):\n","    yield [input_value]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"0oPkAxDvUias"},"outputs":[],"source":["converter.representative_dataset = representative_data_gen"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"IGUAVTqXVfnu"},"source":["The resulting model will be fully quantized but still take float input and output for convenience.\n","\n","Ops that do not have quantized implementations will automatically be left in floating point. This allows conversion to occur smoothly but may restrict deployment to accelerators that support float. "]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"cPVdjaEJVkHy"},"source":["### Full integer quantization\n","\n","To require the converter to only output integer operations, one can specify:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"eQi1aO2cVhoL"},"outputs":[],"source":["converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"snwssESbVtFw"},"source":["### Finally convert the model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"tUEgr46WVsqd"},"outputs":[],"source":["tflite_model = converter.convert()\n","tflite_model_file = 'converted_model.tflite'\n","\n","with open(tflite_model_file, \"wb\") as f:\n","  f.write(tflite_model)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"BbTF6nd1KG2o"},"source":["##Test the TFLite model using the Python Interpreter"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"dg2NkVTmLUdJ"},"outputs":[],"source":["# Load TFLite model and allocate tensors.\n","  \n","interpreter = tf.lite.Interpreter(model_path=tflite_model_file)\n","interpreter.allocate_tensors()\n","\n","input_index = interpreter.get_input_details()[0][\"index\"]\n","output_index = interpreter.get_output_details()[0][\"index\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"snJQVs9JNglv"},"outputs":[],"source":["from tqdm import tqdm\n","\n","# Gather results for the randomly sampled test images\n","predictions = []\n","\n","test_labels, test_imgs = [], []\n","for img, label in tqdm(test_batches.take(10)):\n","  interpreter.set_tensor(input_index, img)\n","  interpreter.invoke()\n","  predictions.append(interpreter.get_tensor(output_index))\n","  \n","  test_labels.append(label.numpy()[0])\n","  test_imgs.append(img)"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{},"colab_type":"code","id":"YMTWNqPpNiAI"},"outputs":[],"source":["#@title Utility functions for plotting\n","# Utilities for plotting\n","\n","class_names = ['cat', 'dog']\n","\n","def plot_image(i, predictions_array, true_label, img):\n","  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n","  plt.grid(False)\n","  plt.xticks([])\n","  plt.yticks([])\n","    \n","  img = np.squeeze(img)\n","\n","  plt.imshow(img, cmap=plt.cm.binary)\n","\n","  predicted_label = np.argmax(predictions_array)\n","  if predicted_label == true_label:\n","    color = 'green'\n","  else:\n","    color = 'red'\n","  \n","  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n","                                100*np.max(predictions_array),\n","                                class_names[true_label]),\n","                                color=color)\n"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"fK_CTyL3XQt1"},"source":["NOTE: Colab runs on server CPUs. At the time of writing this, TensorFlow Lite doesn't have super optimized server CPU kernels. For this reason post-training full-integer quantized models  may be slower here than the other kinds of optimized models. But for mobile CPUs, considerable speedup can be observed."]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{},"colab_type":"code","id":"1-lbnicPNkZs"},"outputs":[],"source":["#@title Visualize the outputs { run: \"auto\" }\n","index = 0 #@param {type:\"slider\", min:0, max:9, step:1}\n","plt.figure(figsize=(6,3))\n","plt.subplot(1,2,1)\n","plot_image(index, predictions, test_labels, test_imgs)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"PmZRieHmKLY5"},"source":["Download the model.\n","\n","**NOTE: You might have to run to the cell below twice**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"0jJAxrQB2VFw"},"outputs":[],"source":["labels = ['cat', 'dog']\n","\n","with open('labels.txt', 'w') as f:\n","  f.write('\\n'.join(labels))\n","\n","try:\n","  from google.colab import files\n","  files.download('converted_model.tflite')\n","  files.download('labels.txt')\n","except:\n","  pass"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"BDlmpjC6VnFZ"},"source":["## Prepare the test images for download (Optional)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"_1ja_WA0WZOH"},"source":["This part involves downloading additional test images for the Mobile Apps only in case you need to try out more samples"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"fzLKEBrfTREA"},"outputs":[],"source":["!mkdir -p test_images"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"Qn7ukNQCSewb"},"outputs":[],"source":["from PIL import Image\n","\n","for index, (image, label) in enumerate(test_batches.take(50)):\n","  image = tf.cast(image * 255.0, tf.uint8)\n","  image = tf.squeeze(image).numpy()\n","  pil_image = Image.fromarray(image)\n","  pil_image.save('test_images/{}_{}.jpg'.format(class_names[label[0]], index))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"xVKKWUG8UMO5"},"outputs":[],"source":["!ls test_images"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"l_w_-UdlS9Vi"},"outputs":[],"source":["!zip -qq cats_vs_dogs_test_images.zip -r test_images/"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"Giva6EHwWm6Y"},"outputs":[],"source":["try:\n","  files.download('cats_vs_dogs_test_images.zip')\n","except:\n","  pass"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"vX6CANjFynQj"},"source":["## Exercise: Fashion MNIST"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"sUkrBEdUzJL2"},"source":["In this exercise you will train a simple convolutional neural network on the Fashion MNIST dataset. Once your model is trained, you will convert it to a TF Lite model and then test it using the TF Lite Interpreter."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"pXX-pi1r6NfG"},"source":["This notebook uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:\n","\n","<table>\n","  <tr><td>\n","    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n","         alt=\"Fashion MNIST sprite\"  width=\"600\">\n","  </td></tr>\n","  <tr><td align=\"center\">\n","    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n","  </td></tr>\n","</table>\n","\n","Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing we'll use here.\n","\n","This uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n","\n","We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow. Import and load the Fashion MNIST data directly from TensorFlow:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"pfyZKowNAQ4j"},"outputs":[],"source":["# TensorFlow and tf.keras\n","import tensorflow as tf\n","from tensorflow import keras\n","\n","import tensorflow_datasets as tfds\n","tfds.disable_progress_bar()\n","\n","# Helper libraries\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import pathlib\n","\n","print(tf.__version__)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"tadPBTEiAprt"},"source":["### Download Fashion MNIST Dataset\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"Ds9gfZKzAnkX"},"outputs":[],"source":["splits = tfds.Split.ALL.subsplit(weighted=(80, 10, 10))\n","\n","splits, info = tfds.load('fashion_mnist', with_info=True, as_supervised=True, split=splits)\n","\n","(train_examples, validation_examples, test_examples) = splits\n","\n","num_examples = info.splits['train'].num_examples\n","num_classes = info.features['label'].num_classes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"-eAv71FRm4JE"},"outputs":[],"source":["class_names = ['T-shirt_top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n","               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"hXe6jNokqX3_"},"outputs":[],"source":["with open('labels.txt', 'w') as f:\n","  f.write('\\n'.join(class_names))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"q0RxpwTmQN-y"},"outputs":[],"source":["IMG_SIZE = 28"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"ZAkuq0V0Aw2X"},"source":["### Preprocessing data"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"_5SIivkunKCC"},"source":["#### Preprocess"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"nQMIkJf9AvJ4"},"outputs":[],"source":["# Write a function to normalize and resize the images\n","\n","def format_example(image, label):\n","  # Cast image to float32\n","  image = # YOUR CODE HERE\n","  # Resize the image if necessary\n","  image = # YOUR CODE HERE\n","  # Normalize the image in the range [0, 1]\n","  image = # YOUR CODE HERE\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"oEQP743aMv4C"},"outputs":[],"source":["# Set the batch size to 32\n","\n","BATCH_SIZE = 32"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"JM4HfIJtnNEk"},"source":["### Create a Dataset from images and labels"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"zOL4gSUARFjM"},"outputs":[],"source":["# Prepare the examples by preprocessing the them and then batching them (and optionally prefetching them)\n","\n","# If you wish you can shuffle train set here\n","train_batches = # YOUR CODE HERE\n","\n","validation_batches = # YOUR CODE HERE\n","test_batches = # YOUR CODE HERE"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"M-topQaOm_LM"},"source":["## Building the model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"4gsYqdIlEFVg"},"outputs":[],"source":["\"\"\"\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 26, 26, 16)        160       \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 13, 13, 16)        0         \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 11, 11, 32)        4640      \n","_________________________________________________________________\n","flatten (Flatten)            (None, 3872)              0         \n","_________________________________________________________________\n","dense (Dense)                (None, 64)                247872    \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 10)                650       \n","=================================================================\n","Total params: 253,322\n","Trainable params: 253,322\n","Non-trainable params: 0\n","\"\"\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"kDqcwksFB1bh"},"outputs":[],"source":["# Build the model shown in the previous cell\n","\n","\n","model = tf.keras.Sequential([\n","  # Set the input shape to (28, 28, 1), kernel size=3, filters=16 and use ReLU activation,  \n","  tf.keras.layers.Conv2D(# YOUR CODE HERE),    \n","  tf.keras.layers.MaxPooling2D(),\n","  # Set the number of filters to 32, kernel size to 3 and use ReLU activation \n","  tf.keras.layers.Conv2D(# YOUR CODE HERE),\n","  # Flatten the output layer to 1 dimension\n","  tf.keras.layers.Flatten(),\n","  # Add a fully connected layer with 64 hidden units and ReLU activation\n","  tf.keras.layers.Dense(# YOUR CODE HERE),\n","  # Attach a final softmax classification head\n","  tf.keras.layers.Dense(# YOUR CODE HERE)])\n","\n","# Set the loss and accuracy metrics\n","model.compile(\n","    optimizer='adam', \n","    loss=# YOUR CODE HERE, \n","    metrics=# YOUR CODE HERE)\n","      "]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"zEMOz-LDnxgD"},"source":["### Train"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"JGlNoRtzCP4_"},"outputs":[],"source":["model.fit(train_batches, \n","          epochs=10,\n","          validation_data=validation_batches)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"TZT9-7w9n4YO"},"source":["## Exporting to TFLite"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"9dq78KBkCV2_"},"outputs":[],"source":["export_dir = 'saved_model/1'\n","\n","# Use the tf.saved_model API to export the SavedModel\n","\n","# Your Code Here"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{},"colab_type":"code","id":"EDGiYrBdE6fl"},"outputs":[],"source":["#@title Select mode of optimization\n","mode = \"Speed\" #@param [\"Default\", \"Storage\", \"Speed\"]\n","\n","if mode == 'Storage':\n","  optimization = tf.lite.Optimize.OPTIMIZE_FOR_SIZE\n","elif mode == 'Speed':\n","  optimization = tf.lite.Optimize.OPTIMIZE_FOR_LATENCY\n","else:\n","  optimization = tf.lite.Optimize.DEFAULT"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"SLskPWHsG4Nj"},"outputs":[],"source":["optimization"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"RbcS9C00CzGe"},"outputs":[],"source":["# Use the TFLiteConverter SavedModel API to initialize the converter\n","converter = # YOUR CODE HERE\n","\n","# Set the optimzations\n","converter.optimizations = # YOUR CODE HERE\n","\n","# Invoke the converter to finally generate the TFLite model\n","tflite_model = # YOUR CODE HERE"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"q5PWCDsTC3El"},"outputs":[],"source":["tflite_model_file = 'model.tflite'\n","\n","with open(tflite_model_file, \"wb\") as f:\n","  f.write(tflite_model)"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"SR6wFcQ1Fglm"},"source":["## Test if your model is working"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"O3IFOcUEIzQx"},"outputs":[],"source":["# Load TFLite model and allocate tensors.\n","interpreter = tf.lite.Interpreter(model_content=tflite_model)\n","interpreter.allocate_tensors()\n","\n","input_index = interpreter.get_input_details()[0][\"index\"]\n","output_index = interpreter.get_output_details()[0][\"index\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"rKcToCBEC-Bu"},"outputs":[],"source":["# Gather results for the randomly sampled test images\n","predictions = []\n","test_labels = []\n","test_images = []\n","\n","for img, label in test_batches.take(50):\n","  interpreter.set_tensor(input_index, img)\n","  interpreter.invoke()\n","  predictions.append(interpreter.get_tensor(output_index))\n","  test_labels.append(label[0])\n","  test_images.append(np.array(img))"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{},"colab_type":"code","id":"kSjTmi05Tyod"},"outputs":[],"source":["#@title Utility functions for plotting\n","# Utilities for plotting\n","\n","def plot_image(i, predictions_array, true_label, img):\n","  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n","  plt.grid(False)\n","  plt.xticks([])\n","  plt.yticks([])\n","  \n","  img = np.squeeze(img)\n","\n","  plt.imshow(img, cmap=plt.cm.binary)\n","\n","  predicted_label = np.argmax(predictions_array)\n","  if predicted_label == true_label.numpy():\n","    color = 'green'\n","  else:\n","    color = 'red'\n","    \n","  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n","                                100*np.max(predictions_array),\n","                                class_names[true_label]),\n","                                color=color)\n","\n","def plot_value_array(i, predictions_array, true_label):\n","  predictions_array, true_label = predictions_array[i], true_label[i]\n","  plt.grid(False)\n","  plt.xticks(list(range(10)), class_names, rotation='vertical')\n","  plt.yticks([])\n","  thisplot = plt.bar(range(10), predictions_array[0], color=\"#777777\")\n","  plt.ylim([0, 1])\n","  predicted_label = np.argmax(predictions_array[0])\n","\n","  thisplot[predicted_label].set_color('red')\n","  thisplot[true_label].set_color('green')"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{},"colab_type":"code","id":"ZZwg0wFaVXhZ"},"outputs":[],"source":["#@title Visualize the outputs { run: \"auto\" }\n","index = 49 #@param {type:\"slider\", min:1, max:50, step:1}\n","plt.figure(figsize=(6,3))\n","plt.subplot(1,2,1)\n","plot_image(index, predictions, test_labels, test_images)\n","plt.show()\n","plot_value_array(index, predictions, test_labels)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"076bo3FMpRDb"},"source":["## Download TFLite model and assets\n","\n","**NOTE: You might have to run to the cell below twice**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"XsPXqPlgZPjE"},"outputs":[],"source":["try:\n","  from google.colab import files\n","  files.download(tflite_model_file)\n","  files.download('labels.txt')\n","except:\n","  pass"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"VyBVNwAzH3Oe"},"source":["## Deploying TFLite model"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"pdfa5L6wH87u"},"source":["Now once you've the trained TFLite model downloaded, you can ahead and deploy this on an Android/iOS application by placing the model assets in the appropriate location."]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"iLY6X8P90L0P"},"source":["## Prepare the test images for download (Optional)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"G3bjzLj10OJv"},"outputs":[],"source":["!mkdir -p test_images"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"pVrBZv1-0Py-"},"outputs":[],"source":["from PIL import Image\n","\n","for index, (image, label) in enumerate(test_batches.take(50)):\n","  image = tf.cast(image * 255.0, tf.uint8)\n","  image = tf.squeeze(image).numpy()\n","  pil_image = Image.fromarray(image)\n","  pil_image.save('test_images/{}_{}.jpg'.format(class_names[label[0]].lower(), index))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"nX0N0M8u0R2s"},"outputs":[],"source":["!ls test_images"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"LvLht1QM0W8k"},"outputs":[],"source":["!zip -qq fmnist_test_images.zip -r test_images/"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"FdOq-4sT0X95"},"outputs":[],"source":["try:\n","  files.download('fmnist_test_images.zip')\n","except:\n","  pass"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"colab_type":"code","executionInfo":{"elapsed":5813,"status":"ok","timestamp":1589839716599,"user":{"displayName":"Sparsh Agarwal","photoUrl":"","userId":"13037694610922482904"},"user_tz":-330},"id":"2CCc8ozR1KsQ","outputId":"c055ad3a-d049-4b95-c1b9-7efcae5d1147"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2020-05-18 22:08:32--  https://video.udacity-data.com/topher/2019/September/5d8e8ca8_lesson-3-android-apps/lesson-3-android-apps.zip\n","Resolving video.udacity-data.com (video.udacity-data.com)... 104.31.15.178, 104.31.14.178, 2606:4700:3038::681f:eb2, ...\n","Connecting to video.udacity-data.com (video.udacity-data.com)|104.31.15.178|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 15535460 (15M) [application/zip]\n","Saving to: ‘lesson-3-android-apps.zip’\n","\n","lesson-3-android-ap 100%[===================>]  14.82M  12.4MB/s    in 1.2s    \n","\n","2020-05-18 22:08:33 (12.4 MB/s) - ‘lesson-3-android-apps.zip’ saved [15535460/15535460]\n","\n"]}],"source":["!wget https://video.udacity-data.com/topher/2019/September/5d8e8ca8_lesson-3-android-apps/lesson-3-android-apps.zip"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"W_FhH8Ma1Ybo"},"outputs":[],"source":["!mv '/content/android-apps.zip' '/content/drive/My Drive/TFLite'\n","!mv '/content/tflite-apps.zip' '/content/drive/My Drive/TFLite'"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{},"colab_type":"code","id":"J_ntptw21tiB"},"outputs":[],"source":[]}],"metadata":{"colab":{"authorship_tag":"ABX9TyOszoYbc/razEuFPg6JRJ+Y","mount_file_id":"1yfR6tdkdBFNgrnaaEVq9iNIvvb76Di0l","name":"dc6f004f-80b2-49c0-80d1-f9aeab9a9c18","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0}