|
279 | 279 | "inputLayer = Input(shape=(X_train.shape[1],))\n", |
280 | 280 | "x = BatchNormalization()(inputLayer)\n", |
281 | 281 | "#\n", |
282 | | - "x = Dense(20, kernel_initializer='lecun_uniform', name='dense_relu1')(x)\n", |
283 | | - "x = BatchNormalization()(x)\n", |
284 | | - "x = Activation(\"relu\")(x)\n", |
285 | | - "#\n", |
286 | 282 | "x = Dense(10, kernel_initializer='lecun_uniform', name='dense_relu2')(x)\n", |
287 | 283 | "x = BatchNormalization()(x)\n", |
288 | 284 | "x = Activation(\"relu\")(x)#\n", |
|
370 | 366 | "plt.ylabel('Transparency')\n", |
371 | 367 | "plt.xlabel('Time')\n", |
372 | 368 | "plt.legend()\n", |
373 | | - "plt.ylim((0.5,1))\n", |
| 369 | + "plt.ylim((0.86,0.94))\n", |
374 | 370 | "plt.show()\n", |
375 | 371 | "\n", |
376 | 372 | "# true distribution\n", |
|
379 | 375 | "plt.ylabel('Transparency')\n", |
380 | 376 | "plt.xlabel('Time')\n", |
381 | 377 | "plt.legend()\n", |
382 | | - "plt.ylim((0.5,1))\n", |
| 378 | + "plt.ylim((0.86,0.94))\n", |
383 | 379 | "plt.show()\n", |
384 | 380 | "\n", |
385 | 381 | "\n", |
386 | 382 | "plt.plot(TimeY_test,Y_test-Y_hat, label = \"Residual\")\n", |
387 | 383 | "plt.ylabel('Transparency Residual')\n", |
388 | 384 | "plt.xlabel('Time')\n", |
| 385 | + "plt.show()\n", |
| 386 | + "\n", |
| 387 | + "plt.plot(TimeY_test,(Y_test-Y_hat)/Y_test*100, label = \"Residual\")\n", |
| 388 | + "plt.ylabel('Transparency % Error')\n", |
| 389 | + "plt.xlabel('Time')\n", |
389 | 390 | "plt.show()" |
390 | 391 | ] |
391 | 392 | }, |
|
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