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279 | 279 | "inputLayer = Input(shape=(X_train.shape[1],))\n",
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280 | 280 | "x = BatchNormalization()(inputLayer)\n",
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281 | 281 | "#\n",
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282 |
| - "x = Dense(100, kernel_initializer='lecun_uniform', name='dense_relu1')(x)\n", |
| 282 | + "x = Dense(20, kernel_initializer='lecun_uniform', name='dense_relu1')(x)\n", |
283 | 283 | "x = BatchNormalization()(x)\n",
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284 | 284 | "x = Activation(\"relu\")(x)\n",
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285 | 285 | "#\n",
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286 |
| - "x = Dense(50, kernel_initializer='lecun_uniform', name='dense_relu2')(x)\n", |
| 286 | + "x = Dense(10, kernel_initializer='lecun_uniform', name='dense_relu2')(x)\n", |
287 | 287 | "x = BatchNormalization()(x)\n",
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288 | 288 | "x = Activation(\"relu\")(x)#\n",
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289 | 289 | "x = Dense(30, kernel_initializer='lecun_uniform', name='dense_relu3')(x)\n",
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290 | 290 | "#\n",
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291 |
| - "x = Dense(10, kernel_initializer='lecun_uniform', name='dense_relu4')(x)\n", |
| 291 | + "x = Dense(5, kernel_initializer='lecun_uniform', name='dense_relu4')(x)\n", |
292 | 292 | "x = BatchNormalization()(x)\n",
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293 | 293 | "x = Activation(\"relu\")(x)\n",
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294 | 294 | "#\n",
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|
359 | 359 | "outputs": [],
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360 | 360 | "source": [
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361 | 361 | "# true distribution\n",
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362 |
| - "plt.plot(TimeY_test,Y_test, label = \"True\")\n", |
| 362 | + "plt.scatter(TimeY_test,Y_test, label = \"True\")\n", |
363 | 363 | "plt.ylabel('Transparency')\n",
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364 | 364 | "plt.xlabel('Time')\n",
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365 | 365 | "plt.show()\n",
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373 | 373 | "plt.ylim((0.5,1))\n",
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374 | 374 | "plt.show()\n",
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375 | 375 | "\n",
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| 376 | + "# true distribution\n", |
| 377 | + "plt.scatter(TimeY_test,Y_test, label = \"True\", alpha=0.5)\n", |
| 378 | + "plt.scatter(TimeY_test,Y_hat, label = \"Predicted\", alpha=0.5)\n", |
| 379 | + "plt.ylabel('Transparency')\n", |
| 380 | + "plt.xlabel('Time')\n", |
| 381 | + "plt.legend()\n", |
| 382 | + "plt.ylim((0.5,1))\n", |
| 383 | + "plt.show()\n", |
| 384 | + "\n", |
376 | 385 | "\n",
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377 | 386 | "plt.plot(TimeY_test,Y_test-Y_hat, label = \"Residual\")\n",
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378 | 387 | "plt.ylabel('Transparency Residual')\n",
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