diff --git a/clean_notebooks/CNN_injection_superposition_4Chann.ipynb b/clean_notebooks/CNN_injection_superposition_4Chann.ipynb
index 2e8fb570f0009c389f5ca286e99b904d1b808783..01154689ba049aa6e6a7a8b0593459bd3feb6fd9 100644
--- a/clean_notebooks/CNN_injection_superposition_4Chann.ipynb
+++ b/clean_notebooks/CNN_injection_superposition_4Chann.ipynb
@@ -18,7 +18,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -38,7 +38,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -144,7 +144,7 @@
"4 ID00007637202177411956430 11 2069 52.063412 79 Male Ex-smoker"
]
},
- "execution_count": 6,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -166,7 +166,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -177,7 +177,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -194,7 +194,7 @@
"text": [
"Array shape: (176, 240, 240, 4)\n",
"min value: -0.1251496147096971\n",
- "max value: 0.16921848376183674\n"
+ "max value: 0.16921848376184256\n"
]
}
],
@@ -228,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -240,7 +240,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -251,7 +251,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -279,7 +279,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -323,7 +323,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -339,7 +339,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -357,7 +357,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -455,7 +455,7 @@
"4 1 "
]
},
- "execution_count": 15,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -473,7 +473,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -487,79 +487,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"model_1\"\n",
- "__________________________________________________________________________________________________\n",
- "Layer (type) Output Shape Param # Connected to \n",
- "==================================================================================================\n",
- "input_1 (InputLayer) [(None, 240, 240, 4) 0 \n",
- "__________________________________________________________________________________________________\n",
- "conv2d (Conv2D) (None, 240, 240, 32) 1184 input_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "activation (Activation) (None, 240, 240, 32) 0 conv2d[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "batch_normalization (BatchNorma (None, 240, 240, 32) 128 activation[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "max_pooling2d (MaxPooling2D) (None, 120, 120, 32) 0 batch_normalization[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "conv2d_1 (Conv2D) (None, 120, 120, 64) 18496 max_pooling2d[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "activation_1 (Activation) (None, 120, 120, 64) 0 conv2d_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "batch_normalization_1 (BatchNor (None, 120, 120, 64) 256 activation_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "max_pooling2d_1 (MaxPooling2D) (None, 60, 60, 64) 0 batch_normalization_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "conv2d_2 (Conv2D) (None, 60, 60, 128) 73856 max_pooling2d_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "activation_2 (Activation) (None, 60, 60, 128) 0 conv2d_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "batch_normalization_2 (BatchNor (None, 60, 60, 128) 512 activation_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "max_pooling2d_2 (MaxPooling2D) (None, 30, 30, 128) 0 batch_normalization_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "flatten (Flatten) (None, 115200) 0 max_pooling2d_2[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_3 (Dense) (None, 16) 1843216 flatten[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "activation_3 (Activation) (None, 16) 0 dense_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_input (InputLayer) [(None, 6)] 0 \n",
- "__________________________________________________________________________________________________\n",
- "batch_normalization_3 (BatchNor (None, 16) 64 activation_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense (Dense) (None, 8) 56 dense_input[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dropout (Dropout) (None, 16) 0 batch_normalization_3[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_1 (Dense) (None, 4) 36 dense[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_4 (Dense) (None, 4) 68 dropout[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_2 (Dense) (None, 1) 5 dense_1[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "activation_4 (Activation) (None, 4) 0 dense_4[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "concatenate (Concatenate) (None, 5) 0 dense_2[0][0] \n",
- " activation_4[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_5 (Dense) (None, 4) 24 concatenate[0][0] \n",
- "__________________________________________________________________________________________________\n",
- "dense_6 (Dense) (None, 1) 5 dense_5[0][0] \n",
- "==================================================================================================\n",
- "Total params: 1,937,906\n",
- "Trainable params: 1,937,426\n",
- "Non-trainable params: 480\n",
- "__________________________________________________________________________________________________\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"model.summary()"
]
@@ -580,7 +510,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -591,12 +521,12 @@
"es = EarlyStopping(monitor='val_loss', patience=pat, verbose=1)\n",
"\n",
"#define the model checkpoint callback -> this will keep on saving the model as a physical file\n",
- "cp = ModelCheckpoint('superposition_injection.h5', verbose=1, save_best_only=True)"
+ "cp = ModelCheckpoint('clean_notebooks/superposition_injection.h5', verbose=1, save_best_only=True)"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -613,19 +543,183 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from time import time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Training on Fold: 1\n"
+ "Training on Fold: 1\n",
+ "Epoch 1/30\n",
+ "111/111 [==============================] - ETA: 3:58 - loss: 488.372 - ETA: 2:04 - loss: 451.508 - ETA: 1:59 - loss: 458.970 - ETA: 1:51 - loss: 465.577 - ETA: 1:46 - loss: 492.173 - ETA: 1:43 - loss: 497.866 - ETA: 1:41 - loss: 494.030 - ETA: 1:40 - loss: 486.396 - ETA: 1:39 - loss: 478.084 - ETA: 1:37 - loss: 469.345 - ETA: 1:36 - loss: 461.107 - ETA: 1:35 - loss: 452.684 - ETA: 1:34 - loss: 444.001 - ETA: 1:32 - loss: 436.931 - ETA: 1:31 - loss: 430.059 - ETA: 1:30 - loss: 425.118 - ETA: 1:29 - loss: 420.196 - ETA: 1:28 - loss: 415.275 - ETA: 1:27 - loss: 411.393 - ETA: 1:27 - loss: 424.982 - ETA: 1:27 - loss: 436.103 - ETA: 1:26 - loss: 447.503 - ETA: 1:25 - loss: 457.195 - ETA: 1:25 - loss: 467.897 - ETA: 1:24 - loss: 476.861 - ETA: 1:26 - loss: 484.268 - ETA: 1:28 - loss: 493.840 - ETA: 1:29 - loss: 502.453 - ETA: 1:29 - loss: 509.875 - ETA: 1:28 - loss: 534.254 - ETA: 1:27 - loss: 555.882 - ETA: 1:27 - loss: 575.071 - ETA: 1:27 - loss: 592.185 - ETA: 1:28 - loss: 607.925 - ETA: 1:28 - loss: 622.005 - ETA: 1:27 - loss: 634.723 - ETA: 1:27 - loss: 646.328 - ETA: 1:26 - loss: 656.662 - ETA: 1:26 - loss: 666.777 - ETA: 1:25 - loss: 676.581 - ETA: 1:25 - loss: 685.529 - ETA: 1:24 - loss: 693.531 - ETA: 1:23 - loss: 700.694 - ETA: 1:23 - loss: 707.155 - ETA: 1:22 - loss: 712.925 - ETA: 1:21 - loss: 718.092 - ETA: 1:20 - loss: 722.658 - ETA: 1:19 - loss: 726.710 - ETA: 1:18 - loss: 730.980 - ETA: 1:17 - loss: 734.799 - ETA: 1:16 - loss: 738.461 - ETA: 1:15 - loss: 741.723 - ETA: 1:14 - loss: 744.757 - ETA: 1:12 - loss: 747.454 - ETA: 1:11 - loss: 750.206 - ETA: 1:10 - loss: 752.708 - ETA: 1:09 - loss: 754.953 - ETA: 1:08 - loss: 757.004 - ETA: 1:07 - loss: 758.790 - ETA: 1:05 - loss: 760.345 - ETA: 1:04 - loss: 761.644 - ETA: 1:03 - loss: 762.822 - ETA: 1:02 - loss: 763.780 - ETA: 1:01 - loss: 764.576 - ETA: 59s - loss: 765.198 - ETA: 58s - loss: 765.68 - ETA: 57s - loss: 766.01 - ETA: 56s - loss: 766.19 - ETA: 54s - loss: 766.26 - ETA: 53s - loss: 766.21 - ETA: 52s - loss: 766.02 - ETA: 51s - loss: 765.72 - ETA: 49s - loss: 765.32 - ETA: 48s - loss: 764.84 - ETA: 47s - loss: 764.36 - ETA: 46s - loss: 763.80 - ETA: 44s - loss: 763.18 - ETA: 43s - loss: 762.49 - ETA: 42s - loss: 761.73 - ETA: 40s - loss: 760.90 - ETA: 39s - loss: 760.01 - ETA: 38s - loss: 759.11 - ETA: 37s - loss: 758.15 - ETA: 35s - loss: 757.74 - ETA: 34s - loss: 757.26 - ETA: 33s - loss: 756.78 - ETA: 31s - loss: 756.27 - ETA: 30s - loss: 755.73 - ETA: 29s - loss: 755.13 - ETA: 27s - loss: 754.46 - ETA: 26s - loss: 753.76 - ETA: 25s - loss: 753.03 - ETA: 23s - loss: 752.34 - ETA: 22s - loss: 751.61 - ETA: 21s - loss: 750.87 - ETA: 20s - loss: 750.08 - ETA: 18s - loss: 749.25 - ETA: 17s - loss: 748.59 - ETA: 16s - loss: 747.90 - ETA: 14s - loss: 747.17 - ETA: 13s - loss: 746.41 - ETA: 12s - loss: 745.62 - ETA: 10s - loss: 744.84 - ETA: 9s - loss: 744.1952 - ETA: 8s - loss: 743.536 - ETA: 6s - loss: 742.852 - ETA: 5s - loss: 742.134 - ETA: 4s - loss: 741.385 - ETA: 2s - loss: 740.606 - ETA: 1s - loss: 739.796 - ETA: 0s - loss: 738.975 - 153s 1s/step - loss: 738.1684 - val_loss: 453.5122\n",
+ "\n",
+ "Epoch 00001: val_loss improved from inf to 453.51224, saving model to superposition_injection.h5\n",
+ "Epoch 2/30\n",
+ "111/111 [==============================] - ETA: 2:35 - loss: 81.53 - ETA: 2:37 - loss: 112.508 - ETA: 2:34 - loss: 143.875 - ETA: 2:32 - loss: 161.969 - ETA: 2:30 - loss: 175.921 - ETA: 2:29 - loss: 181.946 - ETA: 2:27 - loss: 183.681 - ETA: 2:25 - loss: 184.398 - ETA: 2:23 - loss: 183.853 - ETA: 2:22 - loss: 182.511 - ETA: 2:21 - loss: 181.367 - ETA: 2:19 - loss: 180.895 - ETA: 2:18 - loss: 180.204 - ETA: 2:17 - loss: 186.595 - ETA: 2:15 - loss: 191.423 - ETA: 2:14 - loss: 195.077 - ETA: 2:12 - loss: 204.370 - ETA: 2:11 - loss: 212.234 - ETA: 2:09 - loss: 218.818 - ETA: 2:08 - loss: 224.321 - ETA: 2:07 - loss: 228.859 - ETA: 2:05 - loss: 232.718 - ETA: 2:04 - loss: 237.039 - ETA: 2:02 - loss: 240.739 - ETA: 2:01 - loss: 243.769 - ETA: 1:59 - loss: 246.329 - ETA: 1:58 - loss: 248.845 - ETA: 1:56 - loss: 250.985 - ETA: 1:55 - loss: 252.851 - ETA: 1:53 - loss: 254.436 - ETA: 1:52 - loss: 256.006 - ETA: 1:51 - loss: 257.266 - ETA: 1:49 - loss: 258.314 - ETA: 1:48 - loss: 259.727 - ETA: 1:47 - loss: 261.448 - ETA: 1:45 - loss: 262.880 - ETA: 1:44 - loss: 264.220 - ETA: 1:42 - loss: 265.448 - ETA: 1:41 - loss: 266.594 - ETA: 1:39 - loss: 267.581 - ETA: 1:38 - loss: 268.414 - ETA: 1:37 - loss: 269.203 - ETA: 1:35 - loss: 269.828 - ETA: 1:34 - loss: 270.367 - ETA: 1:32 - loss: 270.797 - ETA: 1:31 - loss: 271.223 - ETA: 1:30 - loss: 271.594 - ETA: 1:28 - loss: 273.771 - ETA: 1:27 - loss: 275.754 - ETA: 1:25 - loss: 277.748 - ETA: 1:24 - loss: 279.573 - ETA: 1:22 - loss: 281.337 - ETA: 1:21 - loss: 283.079 - ETA: 1:20 - loss: 284.696 - ETA: 1:18 - loss: 286.420 - ETA: 1:17 - loss: 288.032 - ETA: 1:15 - loss: 289.615 - ETA: 1:14 - loss: 291.096 - ETA: 1:13 - loss: 292.483 - ETA: 1:11 - loss: 293.761 - ETA: 1:10 - loss: 295.841 - ETA: 1:09 - loss: 297.775 - ETA: 1:07 - loss: 299.595 - ETA: 1:06 - loss: 301.275 - ETA: 1:04 - loss: 302.875 - ETA: 1:03 - loss: 304.370 - ETA: 1:01 - loss: 305.800 - ETA: 1:00 - loss: 307.129 - ETA: 59s - loss: 308.494 - ETA: 57s - loss: 309.77 - ETA: 56s - loss: 310.97 - ETA: 55s - loss: 312.08 - ETA: 53s - loss: 313.13 - ETA: 52s - loss: 314.09 - ETA: 50s - loss: 315.00 - ETA: 49s - loss: 315.83 - ETA: 47s - loss: 316.60 - ETA: 46s - loss: 317.36 - ETA: 45s - loss: 318.08 - ETA: 43s - loss: 318.76 - ETA: 42s - loss: 319.39 - ETA: 40s - loss: 320.13 - ETA: 39s - loss: 320.81 - ETA: 38s - loss: 321.45 - ETA: 36s - loss: 322.04 - ETA: 35s - loss: 322.61 - ETA: 33s - loss: 323.13 - ETA: 32s - loss: 324.27 - ETA: 31s - loss: 325.35 - ETA: 29s - loss: 326.36 - ETA: 28s - loss: 327.34 - ETA: 26s - loss: 328.28 - ETA: 25s - loss: 329.15 - ETA: 23s - loss: 329.99 - ETA: 22s - loss: 330.80 - ETA: 21s - loss: 331.56 - ETA: 19s - loss: 332.30 - ETA: 18s - loss: 332.99 - ETA: 16s - loss: 333.64 - ETA: 15s - loss: 334.25 - ETA: 14s - loss: 334.83 - ETA: 12s - loss: 335.38 - ETA: 11s - loss: 335.96 - ETA: 9s - loss: 336.5203 - ETA: 8s - loss: 337.050 - ETA: 7s - loss: 337.722 - ETA: 5s - loss: 338.365 - ETA: 4s - loss: 338.982 - ETA: 2s - loss: 339.580 - ETA: 1s - loss: 340.192 - ETA: 0s - loss: 340.779 - 159s 1s/step - loss: 341.3569 - val_loss: 228.6045\n",
+ "\n",
+ "Epoch 00002: val_loss improved from 453.51224 to 228.60451, saving model to superposition_injection.h5\n",
+ "Epoch 3/30\n",
+ "111/111 [==============================] - ETA: 2:38 - loss: 61.59 - ETA: 2:34 - loss: 71.50 - ETA: 2:30 - loss: 278.229 - ETA: 2:30 - loss: 351.889 - ETA: 2:28 - loss: 389.134 - ETA: 2:27 - loss: 413.400 - ETA: 2:25 - loss: 423.218 - ETA: 2:23 - loss: 425.324 - ETA: 2:22 - loss: 422.282 - ETA: 2:20 - loss: 416.481 - ETA: 2:19 - loss: 410.005 - ETA: 2:18 - loss: 404.266 - ETA: 2:16 - loss: 413.106 - ETA: 2:15 - loss: 419.251 - ETA: 2:14 - loss: 423.040 - ETA: 2:12 - loss: 424.967 - ETA: 2:11 - loss: 425.956 - ETA: 2:10 - loss: 425.680 - ETA: 2:09 - loss: 424.745 - ETA: 2:07 - loss: 423.285 - ETA: 2:06 - loss: 422.025 - ETA: 2:05 - loss: 420.553 - ETA: 2:05 - loss: 419.161 - ETA: 2:03 - loss: 417.479 - ETA: 2:02 - loss: 415.711 - ETA: 2:00 - loss: 413.950 - ETA: 1:59 - loss: 412.041 - ETA: 1:58 - loss: 409.990 - ETA: 1:57 - loss: 407.761 - ETA: 1:55 - loss: 405.442 - ETA: 1:54 - loss: 403.025 - ETA: 1:52 - loss: 400.574 - ETA: 1:50 - loss: 398.085 - ETA: 1:49 - loss: 395.993 - ETA: 1:47 - loss: 394.487 - ETA: 1:46 - loss: 392.879 - ETA: 1:44 - loss: 391.375 - ETA: 1:43 - loss: 389.808 - ETA: 1:42 - loss: 391.800 - ETA: 1:41 - loss: 393.571 - ETA: 1:39 - loss: 395.119 - ETA: 1:38 - loss: 396.390 - ETA: 1:36 - loss: 397.416 - ETA: 1:35 - loss: 398.225 - ETA: 1:33 - loss: 398.826 - ETA: 1:32 - loss: 399.497 - ETA: 1:30 - loss: 399.993 - ETA: 1:29 - loss: 400.340 - ETA: 1:27 - loss: 400.554 - ETA: 1:26 - loss: 400.716 - ETA: 1:24 - loss: 400.791 - ETA: 1:23 - loss: 400.762 - ETA: 1:22 - loss: 400.739 - ETA: 1:20 - loss: 400.610 - ETA: 1:19 - loss: 400.390 - ETA: 1:17 - loss: 400.099 - ETA: 1:16 - loss: 399.749 - ETA: 1:14 - loss: 399.638 - ETA: 1:13 - loss: 399.443 - ETA: 1:12 - loss: 399.331 - ETA: 1:10 - loss: 399.164 - ETA: 1:09 - loss: 398.933 - ETA: 1:07 - loss: 398.692 - ETA: 1:06 - loss: 398.507 - ETA: 1:04 - loss: 400.054 - ETA: 1:03 - loss: 401.476 - ETA: 1:02 - loss: 402.841 - ETA: 1:00 - loss: 404.101 - ETA: 59s - loss: 405.244 - ETA: 57s - loss: 406.29 - ETA: 56s - loss: 407.30 - ETA: 55s - loss: 408.23 - ETA: 53s - loss: 409.08 - ETA: 52s - loss: 409.87 - ETA: 50s - loss: 410.60 - ETA: 49s - loss: 411.32 - ETA: 48s - loss: 411.96 - ETA: 46s - loss: 412.55 - ETA: 45s - loss: 413.10 - ETA: 43s - loss: 413.62 - ETA: 42s - loss: 414.36 - ETA: 41s - loss: 415.05 - ETA: 39s - loss: 415.66 - ETA: 38s - loss: 416.21 - ETA: 36s - loss: 416.71 - ETA: 35s - loss: 417.15 - ETA: 34s - loss: 417.54 - ETA: 32s - loss: 417.90 - ETA: 31s - loss: 418.51 - ETA: 29s - loss: 419.12 - ETA: 28s - loss: 419.73 - ETA: 27s - loss: 420.30 - ETA: 25s - loss: 420.84 - ETA: 24s - loss: 421.77 - ETA: 22s - loss: 422.64 - ETA: 21s - loss: 423.47 - ETA: 19s - loss: 424.24 - ETA: 18s - loss: 424.96 - ETA: 17s - loss: 425.68 - ETA: 15s - loss: 426.35 - ETA: 14s - loss: 426.97 - ETA: 12s - loss: 427.54 - ETA: 11s - loss: 428.09 - ETA: 10s - loss: 428.59 - ETA: 8s - loss: 429.0536 - ETA: 7s - loss: 429.470 - ETA: 5s - loss: 429.846 - ETA: 4s - loss: 430.196 - ETA: 2s - loss: 430.510 - ETA: 1s - loss: 430.793 - ETA: 0s - loss: 431.053 - 161s 1s/step - loss: 431.3088 - val_loss: 313.4254\n",
+ "\n",
+ "Epoch 00003: val_loss did not improve from 228.60451\n",
+ "Epoch 4/30\n",
+ "111/111 [==============================] - ETA: 2:30 - loss: 126.091 - ETA: 2:31 - loss: 1703.20 - ETA: 2:32 - loss: 1899.84 - ETA: 2:33 - loss: 1863.88 - ETA: 2:31 - loss: 1776.18 - ETA: 2:29 - loss: 1683.58 - ETA: 2:28 - loss: 1623.77 - ETA: 2:27 - loss: 1562.77 - ETA: 2:26 - loss: 1502.24 - ETA: 2:24 - loss: 1444.74 - ETA: 2:23 - loss: 1391.11 - ETA: 2:21 - loss: 1345.32 - ETA: 2:20 - loss: 1302.72 - ETA: 2:18 - loss: 1266.32 - ETA: 2:16 - loss: 1232.48 - ETA: 2:15 - loss: 1200.28 - ETA: 2:13 - loss: 1169.74 - ETA: 2:12 - loss: 1141.15 - ETA: 2:09 - loss: 1119.54 - ETA: 2:07 - loss: 1098.71 - ETA: 2:06 - loss: 1078.45 - ETA: 2:05 - loss: 1060.07 - ETA: 2:03 - loss: 1042.21 - ETA: 2:02 - loss: 1026.52 - ETA: 2:01 - loss: 1011.16 - ETA: 1:59 - loss: 996.8151 - ETA: 1:58 - loss: 982.866 - ETA: 1:57 - loss: 969.380 - ETA: 1:55 - loss: 956.380 - ETA: 1:54 - loss: 943.771 - ETA: 1:52 - loss: 931.715 - ETA: 1:51 - loss: 919.935 - ETA: 1:49 - loss: 908.511 - ETA: 1:48 - loss: 897.483 - ETA: 1:46 - loss: 886.819 - ETA: 1:45 - loss: 876.484 - ETA: 1:43 - loss: 866.566 - ETA: 1:42 - loss: 856.935 - ETA: 1:40 - loss: 848.200 - ETA: 1:39 - loss: 840.033 - ETA: 1:38 - loss: 832.058 - ETA: 1:36 - loss: 824.955 - ETA: 1:35 - loss: 818.083 - ETA: 1:33 - loss: 811.334 - ETA: 1:32 - loss: 804.673 - ETA: 1:31 - loss: 801.513 - ETA: 1:29 - loss: 798.250 - ETA: 1:28 - loss: 795.016 - ETA: 1:26 - loss: 791.729 - ETA: 1:25 - loss: 788.413 - ETA: 1:23 - loss: 785.024 - ETA: 1:22 - loss: 781.580 - ETA: 1:21 - loss: 778.309 - ETA: 1:19 - loss: 775.061 - ETA: 1:18 - loss: 771.777 - ETA: 1:16 - loss: 768.511 - ETA: 1:15 - loss: 765.201 - ETA: 1:14 - loss: 761.860 - ETA: 1:12 - loss: 758.484 - ETA: 1:11 - loss: 755.133 - ETA: 1:09 - loss: 751.783 - ETA: 1:08 - loss: 748.466 - ETA: 1:07 - loss: 745.278 - ETA: 1:05 - loss: 742.115 - ETA: 1:04 - loss: 738.962 - ETA: 1:02 - loss: 735.861 - ETA: 1:01 - loss: 732.892 - ETA: 1:00 - loss: 729.947 - ETA: 58s - loss: 726.995 - ETA: 57s - loss: 724.15 - ETA: 55s - loss: 721.31 - ETA: 54s - loss: 718.47 - ETA: 52s - loss: 715.66 - ETA: 51s - loss: 712.85 - ETA: 50s - loss: 710.05 - ETA: 48s - loss: 707.27 - ETA: 47s - loss: 704.49 - ETA: 46s - loss: 701.71 - ETA: 44s - loss: 699.10 - ETA: 43s - loss: 696.49 - ETA: 41s - loss: 693.89 - ETA: 40s - loss: 691.37 - ETA: 39s - loss: 688.88 - ETA: 37s - loss: 686.39 - ETA: 36s - loss: 683.92 - ETA: 34s - loss: 681.45 - ETA: 33s - loss: 679.02 - ETA: 32s - loss: 676.61 - ETA: 30s - loss: 674.22 - ETA: 29s - loss: 671.85 - ETA: 27s - loss: 669.50 - ETA: 26s - loss: 667.15 - ETA: 25s - loss: 664.83 - ETA: 23s - loss: 662.52 - ETA: 22s - loss: 660.22 - ETA: 20s - loss: 657.93 - ETA: 19s - loss: 655.67 - ETA: 18s - loss: 653.44 - ETA: 16s - loss: 651.23 - ETA: 15s - loss: 649.04 - ETA: 13s - loss: 646.93 - ETA: 12s - loss: 644.84 - ETA: 11s - loss: 642.76 - ETA: 9s - loss: 640.7000 - ETA: 8s - loss: 638.657 - ETA: 6s - loss: 636.628 - ETA: 5s - loss: 634.620 - ETA: 4s - loss: 632.645 - ETA: 2s - loss: 630.684 - ETA: 1s - loss: 628.737 - ETA: 0s - loss: 626.816 - 157s 1s/step - loss: 624.9297 - val_loss: 220.8974\n",
+ "\n",
+ "Epoch 00004: val_loss improved from 228.60451 to 220.89743, saving model to superposition_injection.h5\n",
+ "Epoch 5/30\n",
+ "111/111 [==============================] - ETA: 2:21 - loss: 405.547 - ETA: 2:19 - loss: 340.306 - ETA: 2:20 - loss: 303.761 - ETA: 2:22 - loss: 277.256 - ETA: 2:23 - loss: 255.967 - ETA: 2:22 - loss: 241.384 - ETA: 2:21 - loss: 230.971 - ETA: 2:21 - loss: 223.253 - ETA: 2:22 - loss: 220.451 - ETA: 2:21 - loss: 217.389 - ETA: 2:22 - loss: 214.978 - ETA: 2:20 - loss: 212.513 - ETA: 2:18 - loss: 213.561 - ETA: 2:17 - loss: 214.294 - ETA: 2:15 - loss: 215.493 - ETA: 2:14 - loss: 237.111 - ETA: 2:12 - loss: 254.784 - ETA: 2:11 - loss: 271.920 - ETA: 2:09 - loss: 286.293 - ETA: 2:07 - loss: 303.234 - ETA: 2:06 - loss: 320.066 - ETA: 2:04 - loss: 334.232 - ETA: 2:03 - loss: 346.095 - ETA: 2:01 - loss: 356.083 - ETA: 2:00 - loss: 364.650 - ETA: 1:58 - loss: 372.198 - ETA: 1:57 - loss: 378.786 - ETA: 1:55 - loss: 384.371 - ETA: 1:54 - loss: 389.093 - ETA: 1:52 - loss: 393.068 - ETA: 1:51 - loss: 396.499 - ETA: 1:50 - loss: 399.334 - ETA: 1:48 - loss: 401.831 - ETA: 1:47 - loss: 403.919 - ETA: 1:45 - loss: 405.631 - ETA: 1:44 - loss: 407.667 - ETA: 1:42 - loss: 409.366 - ETA: 1:41 - loss: 410.802 - ETA: 1:40 - loss: 412.209 - ETA: 1:38 - loss: 413.355 - ETA: 1:37 - loss: 414.854 - ETA: 1:35 - loss: 416.300 - ETA: 1:34 - loss: 417.469 - ETA: 1:33 - loss: 418.464 - ETA: 1:31 - loss: 419.293 - ETA: 1:30 - loss: 419.998 - ETA: 1:28 - loss: 420.519 - ETA: 1:27 - loss: 420.857 - ETA: 1:26 - loss: 421.056 - ETA: 1:24 - loss: 421.104 - ETA: 1:23 - loss: 421.066 - ETA: 1:21 - loss: 420.931 - ETA: 1:20 - loss: 420.686 - ETA: 1:19 - loss: 420.389 - ETA: 1:17 - loss: 420.019 - ETA: 1:16 - loss: 419.579 - ETA: 1:14 - loss: 419.072 - ETA: 1:13 - loss: 418.509 - ETA: 1:11 - loss: 417.878 - ETA: 1:10 - loss: 417.212 - ETA: 1:09 - loss: 416.519 - ETA: 1:07 - loss: 415.784 - ETA: 1:06 - loss: 415.003 - ETA: 1:05 - loss: 414.882 - ETA: 1:03 - loss: 414.804 - ETA: 1:02 - loss: 414.655 - ETA: 1:00 - loss: 414.465 - ETA: 59s - loss: 414.207 - ETA: 58s - loss: 413.91 - ETA: 56s - loss: 413.56 - ETA: 55s - loss: 413.17 - ETA: 53s - loss: 412.74 - ETA: 52s - loss: 412.27 - ETA: 51s - loss: 411.77 - ETA: 49s - loss: 411.30 - ETA: 48s - loss: 410.80 - ETA: 47s - loss: 410.28 - ETA: 45s - loss: 409.73 - ETA: 44s - loss: 409.16 - ETA: 42s - loss: 408.67 - ETA: 41s - loss: 408.16 - ETA: 39s - loss: 407.64 - ETA: 38s - loss: 407.09 - ETA: 37s - loss: 406.53 - ETA: 35s - loss: 405.96 - ETA: 34s - loss: 405.37 - ETA: 33s - loss: 404.75 - ETA: 31s - loss: 404.12 - ETA: 30s - loss: 403.47 - ETA: 28s - loss: 402.80 - ETA: 27s - loss: 402.13 - ETA: 26s - loss: 401.44 - ETA: 24s - loss: 400.75 - ETA: 23s - loss: 400.05 - ETA: 22s - loss: 399.33 - ETA: 20s - loss: 398.61 - ETA: 19s - loss: 397.91 - ETA: 17s - loss: 397.19 - ETA: 16s - loss: 396.51 - ETA: 15s - loss: 395.83 - ETA: 13s - loss: 395.31 - ETA: 12s - loss: 394.78 - ETA: 11s - loss: 394.25 - ETA: 9s - loss: 393.7189 - ETA: 8s - loss: 393.171 - ETA: 6s - loss: 392.620 - ETA: 5s - loss: 392.064 - ETA: 4s - loss: 391.496 - ETA: 2s - loss: 390.925 - ETA: 1s - loss: 390.444 - ETA: 0s - loss: 389.973 - 155s 1s/step - loss: 389.5100 - val_loss: 207.0528\n",
+ "\n",
+ "Epoch 00005: val_loss improved from 220.89743 to 207.05280, saving model to superposition_injection.h5\n",
+ "Epoch 6/30\n",
+ "111/111 [==============================] - ETA: 2:35 - loss: 129.196 - ETA: 2:29 - loss: 124.680 - ETA: 2:23 - loss: 132.275 - ETA: 2:24 - loss: 150.631 - ETA: 2:23 - loss: 159.869 - ETA: 2:21 - loss: 163.119 - ETA: 2:20 - loss: 169.813 - ETA: 2:19 - loss: 173.336 - ETA: 2:18 - loss: 174.700 - ETA: 2:17 - loss: 175.241 - ETA: 2:16 - loss: 176.062 - ETA: 2:15 - loss: 177.241 - ETA: 2:13 - loss: 177.992 - ETA: 2:12 - loss: 178.469 - ETA: 2:11 - loss: 178.651 - ETA: 2:10 - loss: 178.932 - ETA: 2:08 - loss: 180.463 - ETA: 2:07 - loss: 181.448 - ETA: 2:06 - loss: 182.053 - ETA: 2:04 - loss: 182.338 - ETA: 2:03 - loss: 182.815 - ETA: 2:01 - loss: 183.384 - ETA: 2:00 - loss: 183.815 - ETA: 1:59 - loss: 184.078 - ETA: 1:57 - loss: 184.157 - ETA: 1:56 - loss: 184.159 - ETA: 1:55 - loss: 184.013 - ETA: 1:53 - loss: 189.815 - ETA: 1:52 - loss: 194.944 - ETA: 1:50 - loss: 199.526 - ETA: 1:49 - loss: 203.578 - ETA: 1:48 - loss: 207.319 - ETA: 1:46 - loss: 210.918 - ETA: 1:45 - loss: 214.140 - ETA: 1:44 - loss: 216.981 - ETA: 1:42 - loss: 219.493 - ETA: 1:41 - loss: 221.722 - ETA: 1:40 - loss: 223.692 - ETA: 1:39 - loss: 225.458 - ETA: 1:38 - loss: 227.009 - ETA: 1:37 - loss: 228.668 - ETA: 1:36 - loss: 230.186 - ETA: 1:35 - loss: 231.529 - ETA: 1:33 - loss: 232.707 - ETA: 1:32 - loss: 233.759 - ETA: 1:31 - loss: 234.698 - ETA: 1:29 - loss: 235.542 - ETA: 1:28 - loss: 236.261 - ETA: 1:27 - loss: 236.891 - ETA: 1:25 - loss: 237.424 - ETA: 1:24 - loss: 238.640 - ETA: 1:22 - loss: 239.730 - ETA: 1:21 - loss: 240.710 - ETA: 1:20 - loss: 241.587 - ETA: 1:18 - loss: 242.384 - ETA: 1:17 - loss: 243.179 - ETA: 1:16 - loss: 243.914 - ETA: 1:14 - loss: 244.571 - ETA: 1:13 - loss: 245.290 - ETA: 1:12 - loss: 245.987 - ETA: 1:10 - loss: 246.605 - ETA: 1:09 - loss: 247.175 - ETA: 1:08 - loss: 247.684 - ETA: 1:06 - loss: 248.133 - ETA: 1:05 - loss: 248.527 - ETA: 1:03 - loss: 248.897 - ETA: 1:02 - loss: 249.229 - ETA: 1:00 - loss: 249.537 - ETA: 59s - loss: 249.820 - ETA: 57s - loss: 250.06 - ETA: 56s - loss: 250.42 - ETA: 55s - loss: 250.74 - ETA: 53s - loss: 251.39 - ETA: 52s - loss: 251.98 - ETA: 50s - loss: 252.56 - ETA: 49s - loss: 253.09 - ETA: 47s - loss: 253.57 - ETA: 46s - loss: 254.00 - ETA: 44s - loss: 254.43 - ETA: 43s - loss: 254.82 - ETA: 42s - loss: 255.18 - ETA: 40s - loss: 255.53 - ETA: 39s - loss: 255.84 - ETA: 37s - loss: 256.12 - ETA: 36s - loss: 256.39 - ETA: 34s - loss: 256.62 - ETA: 33s - loss: 256.84 - ETA: 32s - loss: 257.02 - ETA: 30s - loss: 257.19 - ETA: 29s - loss: 257.82 - ETA: 27s - loss: 258.43 - ETA: 26s - loss: 259.00 - ETA: 25s - loss: 259.54 - ETA: 23s - loss: 260.38 - ETA: 22s - loss: 261.18 - ETA: 20s - loss: 261.96 - ETA: 19s - loss: 262.70 - ETA: 18s - loss: 263.41 - ETA: 16s - loss: 264.11 - ETA: 15s - loss: 264.76 - ETA: 13s - loss: 265.42 - ETA: 12s - loss: 266.04 - ETA: 11s - loss: 266.63 - ETA: 9s - loss: 267.2553 - ETA: 8s - loss: 267.839 - ETA: 6s - loss: 268.394 - ETA: 5s - loss: 268.922 - ETA: 4s - loss: 269.422 - ETA: 2s - loss: 269.900 - ETA: 1s - loss: 270.351 - ETA: 0s - loss: 270.783 - 155s 1s/step - loss: 271.2083 - val_loss: 378.5795\n",
+ "\n",
+ "Epoch 00006: val_loss did not improve from 207.05280\n",
+ "Epoch 7/30\n",
+ "111/111 [==============================] - ETA: 2:20 - loss: 160.455 - ETA: 2:23 - loss: 173.297 - ETA: 2:23 - loss: 164.888 - ETA: 2:20 - loss: 159.418 - ETA: 2:19 - loss: 154.859 - ETA: 2:17 - loss: 150.596 - ETA: 2:15 - loss: 147.673 - ETA: 2:14 - loss: 146.422 - ETA: 2:13 - loss: 144.903 - ETA: 2:12 - loss: 147.270 - ETA: 2:11 - loss: 148.681 - ETA: 2:09 - loss: 149.491 - ETA: 2:08 - loss: 150.567 - ETA: 2:07 - loss: 151.209 - ETA: 2:06 - loss: 151.714 - ETA: 2:04 - loss: 151.791 - ETA: 2:03 - loss: 151.680 - ETA: 2:02 - loss: 151.437 - ETA: 2:00 - loss: 151.636 - ETA: 1:59 - loss: 152.141 - ETA: 1:58 - loss: 152.385 - ETA: 1:57 - loss: 152.537 - ETA: 1:55 - loss: 167.272 - ETA: 1:54 - loss: 180.339 - ETA: 1:53 - loss: 191.783 - ETA: 1:51 - loss: 201.967 - ETA: 1:50 - loss: 210.962 - ETA: 1:49 - loss: 218.932 - ETA: 1:47 - loss: 227.425 - ETA: 1:46 - loss: 235.064 - ETA: 1:45 - loss: 241.837 - ETA: 1:43 - loss: 247.996 - ETA: 1:42 - loss: 253.616 - ETA: 1:40 - loss: 258.619 - ETA: 1:39 - loss: 263.074 - ETA: 1:38 - loss: 267.038 - ETA: 1:37 - loss: 270.700 - ETA: 1:36 - loss: 276.882 - ETA: 1:34 - loss: 282.497 - ETA: 1:33 - loss: 287.584 - ETA: 1:32 - loss: 292.193 - ETA: 1:31 - loss: 296.644 - ETA: 1:29 - loss: 300.684 - ETA: 1:28 - loss: 304.424 - ETA: 1:27 - loss: 307.793 - ETA: 1:25 - loss: 310.853 - ETA: 1:24 - loss: 313.726 - ETA: 1:23 - loss: 316.336 - ETA: 1:21 - loss: 318.711 - ETA: 1:20 - loss: 320.923 - ETA: 1:19 - loss: 322.940 - ETA: 1:17 - loss: 324.803 - ETA: 1:16 - loss: 326.534 - ETA: 1:15 - loss: 328.099 - ETA: 1:13 - loss: 329.507 - ETA: 1:12 - loss: 330.769 - ETA: 1:11 - loss: 331.995 - ETA: 1:09 - loss: 333.135 - ETA: 1:08 - loss: 334.142 - ETA: 1:07 - loss: 335.038 - ETA: 1:05 - loss: 335.831 - ETA: 1:04 - loss: 336.562 - ETA: 1:03 - loss: 337.207 - ETA: 1:02 - loss: 337.825 - ETA: 1:00 - loss: 338.480 - ETA: 59s - loss: 339.058 - ETA: 58s - loss: 339.55 - ETA: 56s - loss: 339.96 - ETA: 55s - loss: 340.33 - ETA: 54s - loss: 340.76 - ETA: 52s - loss: 341.19 - ETA: 51s - loss: 341.65 - ETA: 50s - loss: 342.07 - ETA: 48s - loss: 342.43 - ETA: 47s - loss: 342.72 - ETA: 46s - loss: 342.97 - ETA: 44s - loss: 343.17 - ETA: 43s - loss: 343.32 - ETA: 42s - loss: 343.43 - ETA: 40s - loss: 343.50 - ETA: 39s - loss: 343.54 - ETA: 38s - loss: 343.53 - ETA: 36s - loss: 343.51 - ETA: 35s - loss: 343.44 - ETA: 34s - loss: 343.35 - ETA: 32s - loss: 343.23 - ETA: 31s - loss: 343.08 - ETA: 30s - loss: 342.93 - ETA: 28s - loss: 342.78 - ETA: 27s - loss: 342.61 - ETA: 26s - loss: 342.41 - ETA: 25s - loss: 342.24 - ETA: 23s - loss: 342.07 - ETA: 22s - loss: 341.88 - ETA: 21s - loss: 341.70 - ETA: 19s - loss: 341.50 - ETA: 18s - loss: 341.29 - ETA: 17s - loss: 341.05 - ETA: 15s - loss: 340.81 - ETA: 14s - loss: 340.54 - ETA: 13s - loss: 340.28 - ETA: 11s - loss: 340.00 - ETA: 10s - loss: 339.72 - ETA: 9s - loss: 339.4390 - ETA: 7s - loss: 339.139 - ETA: 6s - loss: 338.825 - ETA: 5s - loss: 338.497 - ETA: 3s - loss: 338.160 - ETA: 2s - loss: 337.828 - ETA: 1s - loss: 337.486 - ETA: 0s - loss: 337.149 - 148s 1s/step - loss: 336.8187 - val_loss: 210.4037\n",
+ "\n",
+ "Epoch 00007: val_loss did not improve from 207.05280\n",
+ "Epoch 8/30\n",
+ "111/111 [==============================] - ETA: 2:30 - loss: 95.91 - ETA: 2:19 - loss: 694.465 - ETA: 2:20 - loss: 758.766 - ETA: 2:20 - loss: 752.669 - ETA: 2:19 - loss: 724.052 - ETA: 2:17 - loss: 691.307 - ETA: 2:16 - loss: 660.177 - ETA: 2:15 - loss: 630.623 - ETA: 2:14 - loss: 603.660 - ETA: 2:13 - loss: 580.440 - ETA: 2:11 - loss: 559.353 - ETA: 2:10 - loss: 540.628 - ETA: 2:08 - loss: 523.301 - ETA: 2:06 - loss: 507.368 - ETA: 2:05 - loss: 492.912 - ETA: 2:04 - loss: 479.558 - ETA: 2:03 - loss: 467.070 - ETA: 2:03 - loss: 457.211 - ETA: 2:02 - loss: 447.824 - ETA: 2:01 - loss: 439.494 - ETA: 1:59 - loss: 432.700 - ETA: 1:58 - loss: 429.164 - ETA: 1:56 - loss: 425.509 - ETA: 1:55 - loss: 423.523 - ETA: 1:54 - loss: 421.267 - ETA: 1:53 - loss: 418.785 - ETA: 1:51 - loss: 416.118 - ETA: 1:50 - loss: 413.303 - ETA: 1:49 - loss: 410.374 - ETA: 1:47 - loss: 407.393 - ETA: 1:46 - loss: 404.378 - ETA: 1:45 - loss: 401.360 - ETA: 1:43 - loss: 398.355 - ETA: 1:42 - loss: 395.363 - ETA: 1:40 - loss: 392.506 - ETA: 1:39 - loss: 389.648 - ETA: 1:38 - loss: 387.254 - ETA: 1:36 - loss: 384.882 - ETA: 1:35 - loss: 382.563 - ETA: 1:34 - loss: 380.246 - ETA: 1:32 - loss: 377.923 - ETA: 1:31 - loss: 375.614 - ETA: 1:30 - loss: 373.318 - ETA: 1:28 - loss: 371.036 - ETA: 1:27 - loss: 368.773 - ETA: 1:26 - loss: 366.552 - ETA: 1:24 - loss: 364.340 - ETA: 1:23 - loss: 362.175 - ETA: 1:22 - loss: 360.059 - ETA: 1:20 - loss: 357.964 - ETA: 1:19 - loss: 355.931 - ETA: 1:18 - loss: 354.061 - ETA: 1:16 - loss: 352.212 - ETA: 1:15 - loss: 350.390 - ETA: 1:14 - loss: 348.634 - ETA: 1:12 - loss: 346.897 - ETA: 1:11 - loss: 345.163 - ETA: 1:10 - loss: 343.442 - ETA: 1:08 - loss: 341.733 - ETA: 1:07 - loss: 340.654 - ETA: 1:06 - loss: 339.593 - ETA: 1:04 - loss: 338.526 - ETA: 1:03 - loss: 337.441 - ETA: 1:02 - loss: 336.347 - ETA: 1:00 - loss: 335.253 - ETA: 59s - loss: 334.156 - ETA: 58s - loss: 333.06 - ETA: 56s - loss: 331.97 - ETA: 55s - loss: 330.88 - ETA: 54s - loss: 329.78 - ETA: 52s - loss: 328.71 - ETA: 51s - loss: 327.65 - ETA: 50s - loss: 326.60 - ETA: 48s - loss: 325.60 - ETA: 47s - loss: 324.61 - ETA: 46s - loss: 323.70 - ETA: 44s - loss: 322.79 - ETA: 43s - loss: 321.88 - ETA: 42s - loss: 321.03 - ETA: 40s - loss: 320.17 - ETA: 39s - loss: 319.32 - ETA: 38s - loss: 318.47 - ETA: 37s - loss: 317.62 - ETA: 35s - loss: 316.79 - ETA: 34s - loss: 315.99 - ETA: 33s - loss: 315.19 - ETA: 31s - loss: 314.39 - ETA: 30s - loss: 313.60 - ETA: 29s - loss: 312.80 - ETA: 27s - loss: 312.03 - ETA: 26s - loss: 311.25 - ETA: 25s - loss: 310.47 - ETA: 23s - loss: 309.69 - ETA: 22s - loss: 308.91 - ETA: 21s - loss: 308.13 - ETA: 19s - loss: 307.36 - ETA: 18s - loss: 306.59 - ETA: 17s - loss: 305.82 - ETA: 15s - loss: 305.05 - ETA: 14s - loss: 304.28 - ETA: 13s - loss: 303.52 - ETA: 11s - loss: 302.81 - ETA: 10s - loss: 302.10 - ETA: 9s - loss: 301.4327 - ETA: 7s - loss: 300.783 - ETA: 6s - loss: 300.133 - ETA: 5s - loss: 299.482 - ETA: 3s - loss: 298.836 - ETA: 2s - loss: 298.191 - ETA: 1s - loss: 297.546 - ETA: 0s - loss: 296.906 - 148s 1s/step - loss: 296.2779 - val_loss: 171.5556\n",
+ "\n",
+ "Epoch 00008: val_loss improved from 207.05280 to 171.55556, saving model to superposition_injection.h5\n",
+ "Epoch 9/30\n",
+ "111/111 [==============================] - ETA: 2:30 - loss: 357.283 - ETA: 2:22 - loss: 299.494 - ETA: 2:21 - loss: 271.296 - ETA: 2:18 - loss: 248.538 - ETA: 2:18 - loss: 242.732 - ETA: 2:16 - loss: 239.271 - ETA: 2:16 - loss: 238.697 - ETA: 2:15 - loss: 237.341 - ETA: 2:13 - loss: 236.070 - ETA: 2:12 - loss: 233.510 - ETA: 2:11 - loss: 232.861 - ETA: 2:10 - loss: 231.318 - ETA: 2:09 - loss: 229.249 - ETA: 2:08 - loss: 231.585 - ETA: 2:06 - loss: 233.171 - ETA: 2:05 - loss: 233.994 - ETA: 2:04 - loss: 234.545 - ETA: 2:02 - loss: 234.662 - ETA: 2:01 - loss: 234.402 - ETA: 2:00 - loss: 234.236 - ETA: 1:59 - loss: 233.810 - ETA: 1:57 - loss: 233.339 - ETA: 1:56 - loss: 232.792 - ETA: 1:55 - loss: 232.035 - ETA: 1:53 - loss: 231.141 - ETA: 1:52 - loss: 230.111 - ETA: 1:50 - loss: 229.002 - ETA: 1:49 - loss: 228.154 - ETA: 1:48 - loss: 227.247 - ETA: 1:46 - loss: 226.311 - ETA: 1:45 - loss: 225.305 - ETA: 1:44 - loss: 224.234 - ETA: 1:42 - loss: 223.167 - ETA: 1:41 - loss: 222.075 - ETA: 1:40 - loss: 221.909 - ETA: 1:39 - loss: 221.677 - ETA: 1:37 - loss: 221.366 - ETA: 1:36 - loss: 221.002 - ETA: 1:35 - loss: 220.696 - ETA: 1:33 - loss: 220.326 - ETA: 1:32 - loss: 219.950 - ETA: 1:31 - loss: 219.525 - ETA: 1:29 - loss: 219.104 - ETA: 1:28 - loss: 218.655 - ETA: 1:27 - loss: 218.199 - ETA: 1:25 - loss: 217.703 - ETA: 1:24 - loss: 217.179 - ETA: 1:23 - loss: 216.636 - ETA: 1:21 - loss: 216.069 - ETA: 1:20 - loss: 215.490 - ETA: 1:19 - loss: 214.893 - ETA: 1:17 - loss: 214.278 - ETA: 1:16 - loss: 213.698 - ETA: 1:15 - loss: 213.114 - ETA: 1:13 - loss: 212.583 - ETA: 1:12 - loss: 212.075 - ETA: 1:11 - loss: 211.603 - ETA: 1:09 - loss: 211.120 - ETA: 1:08 - loss: 210.631 - ETA: 1:07 - loss: 210.136 - ETA: 1:06 - loss: 209.698 - ETA: 1:04 - loss: 209.248 - ETA: 1:03 - loss: 208.799 - ETA: 1:01 - loss: 208.521 - ETA: 1:00 - loss: 208.237 - ETA: 59s - loss: 207.953 - ETA: 58s - loss: 207.65 - ETA: 56s - loss: 207.34 - ETA: 55s - loss: 207.02 - ETA: 54s - loss: 206.72 - ETA: 52s - loss: 206.45 - ETA: 51s - loss: 206.18 - ETA: 50s - loss: 205.89 - ETA: 48s - loss: 205.61 - ETA: 47s - loss: 205.32 - ETA: 46s - loss: 205.03 - ETA: 44s - loss: 204.74 - ETA: 43s - loss: 204.46 - ETA: 42s - loss: 204.16 - ETA: 40s - loss: 203.88 - ETA: 39s - loss: 204.35 - ETA: 38s - loss: 204.80 - ETA: 36s - loss: 205.23 - ETA: 35s - loss: 205.63 - ETA: 34s - loss: 206.00 - ETA: 32s - loss: 206.34 - ETA: 31s - loss: 206.66 - ETA: 30s - loss: 206.96 - ETA: 28s - loss: 207.23 - ETA: 27s - loss: 207.47 - ETA: 26s - loss: 207.73 - ETA: 25s - loss: 207.97 - ETA: 23s - loss: 208.19 - ETA: 22s - loss: 208.39 - ETA: 21s - loss: 208.58 - ETA: 19s - loss: 208.74 - ETA: 18s - loss: 208.92 - ETA: 17s - loss: 209.07 - ETA: 15s - loss: 209.22 - ETA: 14s - loss: 209.36 - ETA: 13s - loss: 209.47 - ETA: 11s - loss: 209.58 - ETA: 10s - loss: 209.68 - ETA: 9s - loss: 209.7967 - ETA: 7s - loss: 209.912 - ETA: 6s - loss: 210.014 - ETA: 5s - loss: 210.101 - ETA: 3s - loss: 210.175 - ETA: 2s - loss: 210.236 - ETA: 1s - loss: 210.287 - ETA: 0s - loss: 210.331 - 148s 1s/step - loss: 210.3748 - val_loss: 129.8188\n",
+ "\n",
+ "Epoch 00009: val_loss improved from 171.55556 to 129.81879, saving model to superposition_injection.h5\n",
+ "Epoch 10/30\n",
+ "111/111 [==============================] - ETA: 2:28 - loss: 141.975 - ETA: 2:21 - loss: 125.725 - ETA: 2:19 - loss: 117.682 - ETA: 2:19 - loss: 121.769 - ETA: 2:18 - loss: 124.988 - ETA: 2:17 - loss: 130.553 - ETA: 2:16 - loss: 132.449 - ETA: 2:15 - loss: 133.660 - ETA: 2:14 - loss: 134.251 - ETA: 2:13 - loss: 134.197 - ETA: 2:12 - loss: 133.794 - ETA: 2:10 - loss: 133.509 - ETA: 2:09 - loss: 133.313 - ETA: 2:07 - loss: 133.108 - ETA: 2:06 - loss: 133.488 - ETA: 2:04 - loss: 134.195 - ETA: 2:03 - loss: 134.639 - ETA: 2:02 - loss: 136.029 - ETA: 2:00 - loss: 137.014 - ETA: 1:59 - loss: 138.252 - ETA: 1:58 - loss: 139.474 - ETA: 1:57 - loss: 140.474 - ETA: 1:55 - loss: 141.296 - ETA: 1:54 - loss: 141.886 - ETA: 1:53 - loss: 142.365 - ETA: 1:52 - loss: 142.683 - ETA: 1:50 - loss: 142.978 - ETA: 1:49 - loss: 143.229 - ETA: 1:48 - loss: 143.400 - ETA: 1:47 - loss: 143.467 - ETA: 1:45 - loss: 143.660 - ETA: 1:44 - loss: 143.768 - ETA: 1:43 - loss: 143.884 - ETA: 1:41 - loss: 143.924 - ETA: 1:40 - loss: 143.905 - ETA: 1:38 - loss: 143.855 - ETA: 1:37 - loss: 143.759 - ETA: 1:36 - loss: 143.681 - ETA: 1:34 - loss: 143.572 - ETA: 1:33 - loss: 143.541 - ETA: 1:32 - loss: 143.870 - ETA: 1:31 - loss: 144.152 - ETA: 1:29 - loss: 144.422 - ETA: 1:28 - loss: 144.661 - ETA: 1:27 - loss: 144.897 - ETA: 1:25 - loss: 145.098 - ETA: 1:24 - loss: 145.273 - ETA: 1:23 - loss: 145.423 - ETA: 1:21 - loss: 145.609 - ETA: 1:20 - loss: 145.760 - ETA: 1:19 - loss: 145.874 - ETA: 1:18 - loss: 145.978 - ETA: 1:16 - loss: 146.298 - ETA: 1:15 - loss: 146.571 - ETA: 1:14 - loss: 146.819 - ETA: 1:12 - loss: 147.049 - ETA: 1:11 - loss: 147.273 - ETA: 1:10 - loss: 147.556 - ETA: 1:08 - loss: 147.812 - ETA: 1:07 - loss: 148.032 - ETA: 1:06 - loss: 148.222 - ETA: 1:04 - loss: 148.392 - ETA: 1:03 - loss: 148.555 - ETA: 1:02 - loss: 148.697 - ETA: 1:00 - loss: 148.816 - ETA: 59s - loss: 148.924 - ETA: 58s - loss: 149.01 - ETA: 56s - loss: 149.09 - ETA: 55s - loss: 149.16 - ETA: 54s - loss: 149.20 - ETA: 53s - loss: 149.24 - ETA: 51s - loss: 149.26 - ETA: 50s - loss: 149.27 - ETA: 49s - loss: 149.27 - ETA: 47s - loss: 149.26 - ETA: 46s - loss: 149.31 - ETA: 45s - loss: 149.36 - ETA: 43s - loss: 149.41 - ETA: 42s - loss: 149.44 - ETA: 41s - loss: 149.46 - ETA: 39s - loss: 149.51 - ETA: 38s - loss: 149.83 - ETA: 37s - loss: 150.13 - ETA: 35s - loss: 150.41 - ETA: 34s - loss: 150.66 - ETA: 33s - loss: 150.91 - ETA: 31s - loss: 151.13 - ETA: 30s - loss: 151.34 - ETA: 29s - loss: 151.57 - ETA: 27s - loss: 151.79 - ETA: 26s - loss: 151.99 - ETA: 25s - loss: 152.85 - ETA: 23s - loss: 153.67 - ETA: 22s - loss: 154.47 - ETA: 21s - loss: 155.23 - ETA: 19s - loss: 155.96 - ETA: 18s - loss: 156.67 - ETA: 17s - loss: 157.35 - ETA: 15s - loss: 158.01 - ETA: 14s - loss: 158.63 - ETA: 13s - loss: 159.23 - ETA: 11s - loss: 159.81 - ETA: 10s - loss: 160.36 - ETA: 9s - loss: 160.9005 - ETA: 7s - loss: 161.414 - ETA: 6s - loss: 161.906 - ETA: 5s - loss: 162.380 - ETA: 3s - loss: 162.840 - ETA: 2s - loss: 163.285 - ETA: 1s - loss: 163.712 - ETA: 0s - loss: 164.143 - 149s 1s/step - loss: 164.5654 - val_loss: 173.1316\n",
+ "\n",
+ "Epoch 00010: val_loss did not improve from 129.81879\n",
+ "Epoch 11/30\n",
+ "111/111 [==============================] - ETA: 2:21 - loss: 95.89 - ETA: 2:23 - loss: 90.98 - ETA: 2:21 - loss: 314.093 - ETA: 2:21 - loss: 385.153 - ETA: 2:19 - loss: 445.533 - ETA: 2:18 - loss: 468.475 - ETA: 2:17 - loss: 494.188 - ETA: 2:15 - loss: 505.339 - ETA: 2:14 - loss: 507.661 - ETA: 2:13 - loss: 505.250 - ETA: 2:11 - loss: 500.355 - ETA: 2:10 - loss: 493.550 - ETA: 2:09 - loss: 485.799 - ETA: 2:07 - loss: 477.546 - ETA: 2:06 - loss: 469.244 - ETA: 2:05 - loss: 461.651 - ETA: 2:03 - loss: 454.053 - ETA: 2:02 - loss: 446.502 - ETA: 2:00 - loss: 439.149 - ETA: 1:59 - loss: 432.034 - ETA: 1:58 - loss: 425.133 - ETA: 1:56 - loss: 418.615 - ETA: 1:55 - loss: 412.324 - ETA: 1:54 - loss: 406.251 - ETA: 1:52 - loss: 400.721 - ETA: 1:51 - loss: 395.792 - ETA: 1:50 - loss: 390.995 - ETA: 1:49 - loss: 386.349 - ETA: 1:48 - loss: 381.859 - ETA: 1:46 - loss: 377.516 - ETA: 1:45 - loss: 373.388 - ETA: 1:44 - loss: 369.349 - ETA: 1:42 - loss: 365.411 - ETA: 1:41 - loss: 361.658 - ETA: 1:39 - loss: 358.073 - ETA: 1:38 - loss: 354.613 - ETA: 1:37 - loss: 351.264 - ETA: 1:35 - loss: 348.012 - ETA: 1:34 - loss: 344.852 - ETA: 1:33 - loss: 341.772 - ETA: 1:31 - loss: 338.785 - ETA: 1:30 - loss: 335.865 - ETA: 1:29 - loss: 333.057 - ETA: 1:27 - loss: 330.304 - ETA: 1:26 - loss: 327.608 - ETA: 1:25 - loss: 324.984 - ETA: 1:24 - loss: 322.420 - ETA: 1:22 - loss: 320.126 - ETA: 1:21 - loss: 317.869 - ETA: 1:20 - loss: 315.673 - ETA: 1:18 - loss: 313.513 - ETA: 1:17 - loss: 311.411 - ETA: 1:16 - loss: 309.344 - ETA: 1:15 - loss: 307.348 - ETA: 1:13 - loss: 305.394 - ETA: 1:12 - loss: 303.473 - ETA: 1:11 - loss: 301.582 - ETA: 1:09 - loss: 299.723 - ETA: 1:08 - loss: 297.938 - ETA: 1:07 - loss: 296.189 - ETA: 1:05 - loss: 294.473 - ETA: 1:04 - loss: 292.820 - ETA: 1:03 - loss: 291.188 - ETA: 1:01 - loss: 289.602 - ETA: 1:00 - loss: 288.272 - ETA: 59s - loss: 286.960 - ETA: 57s - loss: 285.66 - ETA: 56s - loss: 284.38 - ETA: 55s - loss: 283.10 - ETA: 53s - loss: 281.84 - ETA: 52s - loss: 280.60 - ETA: 51s - loss: 279.38 - ETA: 49s - loss: 278.18 - ETA: 48s - loss: 276.99 - ETA: 47s - loss: 275.81 - ETA: 45s - loss: 274.65 - ETA: 44s - loss: 273.51 - ETA: 43s - loss: 272.38 - ETA: 42s - loss: 271.28 - ETA: 40s - loss: 270.19 - ETA: 39s - loss: 269.12 - ETA: 38s - loss: 268.07 - ETA: 36s - loss: 267.05 - ETA: 35s - loss: 266.06 - ETA: 34s - loss: 265.07 - ETA: 32s - loss: 264.09 - ETA: 31s - loss: 263.12 - ETA: 30s - loss: 262.17 - ETA: 28s - loss: 261.24 - ETA: 27s - loss: 260.31 - ETA: 26s - loss: 259.41 - ETA: 24s - loss: 258.51 - ETA: 23s - loss: 257.61 - ETA: 22s - loss: 256.73 - ETA: 21s - loss: 255.85 - ETA: 19s - loss: 254.98 - ETA: 18s - loss: 254.12 - ETA: 17s - loss: 253.28 - ETA: 15s - loss: 252.47 - ETA: 14s - loss: 251.66 - ETA: 13s - loss: 250.87 - ETA: 11s - loss: 250.08 - ETA: 10s - loss: 249.30 - ETA: 9s - loss: 248.5397 - ETA: 7s - loss: 247.782 - ETA: 6s - loss: 247.053 - ETA: 5s - loss: 246.334 - ETA: 3s - loss: 245.631 - ETA: 2s - loss: 244.961 - ETA: 1s - loss: 244.302 - ETA: 0s - loss: 243.653 - 148s 1s/step - loss: 243.0163 - val_loss: 106.1862\n",
+ "\n",
+ "Epoch 00011: val_loss improved from 129.81879 to 106.18616, saving model to superposition_injection.h5\n",
+ "Epoch 12/30\n",
+ "111/111 [==============================] - ETA: 2:30 - loss: 136.452 - ETA: 2:22 - loss: 130.338 - ETA: 2:22 - loss: 119.887 - ETA: 2:20 - loss: 114.706 - ETA: 2:19 - loss: 118.341 - ETA: 2:18 - loss: 118.389 - ETA: 2:16 - loss: 117.884 - ETA: 2:15 - loss: 118.829 - ETA: 2:14 - loss: 118.964 - ETA: 2:12 - loss: 119.036 - ETA: 2:11 - loss: 119.037 - ETA: 2:10 - loss: 118.915 - ETA: 2:08 - loss: 118.520 - ETA: 2:07 - loss: 117.999 - ETA: 2:06 - loss: 117.817 - ETA: 2:04 - loss: 117.876 - ETA: 2:03 - loss: 117.796 - ETA: 2:02 - loss: 117.609 - ETA: 2:00 - loss: 117.666 - ETA: 1:58 - loss: 117.662 - ETA: 1:57 - loss: 117.701 - ETA: 1:56 - loss: 117.734 - ETA: 1:55 - loss: 117.706 - ETA: 1:54 - loss: 117.617 - ETA: 1:53 - loss: 117.572 - ETA: 1:52 - loss: 117.567 - ETA: 1:51 - loss: 117.562 - ETA: 1:49 - loss: 119.337 - ETA: 1:48 - loss: 120.887 - ETA: 1:47 - loss: 122.221 - ETA: 1:45 - loss: 123.379 - ETA: 1:44 - loss: 124.393 - ETA: 1:43 - loss: 125.304 - ETA: 1:41 - loss: 126.133 - ETA: 1:40 - loss: 126.857 - ETA: 1:39 - loss: 127.493 - ETA: 1:37 - loss: 128.052 - ETA: 1:36 - loss: 128.548 - ETA: 1:35 - loss: 128.971 - ETA: 1:33 - loss: 129.368 - ETA: 1:32 - loss: 129.709 - ETA: 1:31 - loss: 130.026 - ETA: 1:29 - loss: 130.285 - ETA: 1:28 - loss: 130.574 - ETA: 1:27 - loss: 130.844 - ETA: 1:25 - loss: 131.187 - ETA: 1:24 - loss: 131.485 - ETA: 1:23 - loss: 131.741 - ETA: 1:21 - loss: 131.974 - ETA: 1:20 - loss: 132.188 - ETA: 1:19 - loss: 132.372 - ETA: 1:18 - loss: 132.543 - ETA: 1:16 - loss: 132.689 - ETA: 1:15 - loss: 132.937 - ETA: 1:14 - loss: 133.154 - ETA: 1:12 - loss: 133.346 - ETA: 1:11 - loss: 133.518 - ETA: 1:10 - loss: 133.663 - ETA: 1:08 - loss: 133.802 - ETA: 1:07 - loss: 133.914 - ETA: 1:06 - loss: 134.112 - ETA: 1:04 - loss: 134.289 - ETA: 1:03 - loss: 134.454 - ETA: 1:02 - loss: 134.602 - ETA: 1:00 - loss: 134.736 - ETA: 59s - loss: 134.886 - ETA: 58s - loss: 135.02 - ETA: 56s - loss: 135.15 - ETA: 55s - loss: 135.27 - ETA: 54s - loss: 135.39 - ETA: 52s - loss: 135.51 - ETA: 51s - loss: 135.62 - ETA: 50s - loss: 135.71 - ETA: 48s - loss: 135.80 - ETA: 47s - loss: 135.88 - ETA: 46s - loss: 135.94 - ETA: 44s - loss: 136.00 - ETA: 43s - loss: 136.04 - ETA: 42s - loss: 136.08 - ETA: 40s - loss: 136.52 - ETA: 39s - loss: 136.95 - ETA: 38s - loss: 137.35 - ETA: 36s - loss: 137.74 - ETA: 35s - loss: 138.10 - ETA: 34s - loss: 138.47 - ETA: 33s - loss: 138.83 - ETA: 31s - loss: 139.17 - ETA: 30s - loss: 139.49 - ETA: 29s - loss: 139.83 - ETA: 27s - loss: 140.16 - ETA: 26s - loss: 140.48 - ETA: 25s - loss: 140.81 - ETA: 23s - loss: 141.13 - ETA: 22s - loss: 141.43 - ETA: 21s - loss: 141.72 - ETA: 19s - loss: 142.02 - ETA: 18s - loss: 142.30 - ETA: 17s - loss: 142.58 - ETA: 15s - loss: 142.85 - ETA: 14s - loss: 143.11 - ETA: 13s - loss: 143.36 - ETA: 11s - loss: 143.61 - ETA: 10s - loss: 143.85 - ETA: 9s - loss: 144.0785 - ETA: 7s - loss: 144.294 - ETA: 6s - loss: 144.499 - ETA: 5s - loss: 144.712 - ETA: 3s - loss: 144.914 - ETA: 2s - loss: 145.105 - ETA: 1s - loss: 145.287 - ETA: 0s - loss: 145.463 - 148s 1s/step - loss: 145.6353 - val_loss: 144.8436\n",
+ "\n",
+ "Epoch 00012: val_loss did not improve from 106.18616\n",
+ "Epoch 13/30\n",
+ "111/111 [==============================] - ETA: 2:14 - loss: 133.209 - ETA: 2:30 - loss: 117.192 - ETA: 2:27 - loss: 116.248 - ETA: 2:25 - loss: 117.665 - ETA: 2:24 - loss: 153.404 - ETA: 2:23 - loss: 172.455 - ETA: 2:20 - loss: 183.022 - ETA: 2:18 - loss: 188.490 - ETA: 2:16 - loss: 191.335 - ETA: 2:14 - loss: 194.143 - ETA: 2:13 - loss: 195.379 - ETA: 2:12 - loss: 195.650 - ETA: 2:10 - loss: 197.084 - ETA: 2:08 - loss: 197.733 - ETA: 2:07 - loss: 198.124 - ETA: 2:06 - loss: 197.978 - ETA: 2:04 - loss: 197.560 - ETA: 2:03 - loss: 196.875 - ETA: 2:02 - loss: 195.975 - ETA: 2:00 - loss: 195.065 - ETA: 1:59 - loss: 194.083 - ETA: 1:58 - loss: 194.424 - ETA: 1:56 - loss: 194.500 - ETA: 1:55 - loss: 194.417 - ETA: 1:54 - loss: 194.291 - ETA: 1:52 - loss: 194.035 - ETA: 1:51 - loss: 193.677 - ETA: 1:50 - loss: 193.222 - ETA: 1:48 - loss: 192.683 - ETA: 1:47 - loss: 192.056 - ETA: 1:45 - loss: 192.358 - ETA: 1:44 - loss: 192.631 - ETA: 1:43 - loss: 193.232 - ETA: 1:41 - loss: 193.806 - ETA: 1:40 - loss: 194.241 - ETA: 1:39 - loss: 194.811 - ETA: 1:38 - loss: 195.261 - ETA: 1:36 - loss: 195.637 - ETA: 1:35 - loss: 195.945 - ETA: 1:34 - loss: 196.367 - ETA: 1:32 - loss: 196.717 - ETA: 1:31 - loss: 197.080 - ETA: 1:29 - loss: 197.419 - ETA: 1:28 - loss: 197.687 - ETA: 1:27 - loss: 197.881 - ETA: 1:25 - loss: 198.027 - ETA: 1:24 - loss: 198.130 - ETA: 1:23 - loss: 198.179 - ETA: 1:21 - loss: 198.183 - ETA: 1:20 - loss: 198.138 - ETA: 1:19 - loss: 198.049 - ETA: 1:18 - loss: 197.925 - ETA: 1:16 - loss: 197.761 - ETA: 1:15 - loss: 197.563 - ETA: 1:13 - loss: 197.332 - ETA: 1:12 - loss: 197.109 - ETA: 1:11 - loss: 196.868 - ETA: 1:10 - loss: 196.615 - ETA: 1:08 - loss: 196.343 - ETA: 1:07 - loss: 196.068 - ETA: 1:06 - loss: 195.790 - ETA: 1:04 - loss: 195.489 - ETA: 1:03 - loss: 195.187 - ETA: 1:02 - loss: 194.875 - ETA: 1:00 - loss: 194.553 - ETA: 59s - loss: 194.214 - ETA: 58s - loss: 193.90 - ETA: 56s - loss: 193.60 - ETA: 55s - loss: 193.30 - ETA: 54s - loss: 192.99 - ETA: 52s - loss: 192.68 - ETA: 51s - loss: 192.37 - ETA: 50s - loss: 192.05 - ETA: 48s - loss: 191.75 - ETA: 47s - loss: 191.44 - ETA: 46s - loss: 191.13 - ETA: 44s - loss: 190.84 - ETA: 43s - loss: 190.55 - ETA: 42s - loss: 190.26 - ETA: 40s - loss: 189.96 - ETA: 39s - loss: 189.65 - ETA: 38s - loss: 189.34 - ETA: 36s - loss: 189.04 - ETA: 35s - loss: 188.75 - ETA: 34s - loss: 188.48 - ETA: 32s - loss: 188.22 - ETA: 31s - loss: 187.95 - ETA: 30s - loss: 187.68 - ETA: 28s - loss: 187.46 - ETA: 27s - loss: 187.30 - ETA: 26s - loss: 187.13 - ETA: 24s - loss: 186.95 - ETA: 23s - loss: 186.77 - ETA: 22s - loss: 186.58 - ETA: 21s - loss: 186.39 - ETA: 19s - loss: 186.19 - ETA: 18s - loss: 185.99 - ETA: 17s - loss: 185.79 - ETA: 15s - loss: 185.58 - ETA: 14s - loss: 185.37 - ETA: 13s - loss: 185.16 - ETA: 11s - loss: 184.94 - ETA: 10s - loss: 184.72 - ETA: 9s - loss: 184.5116 - ETA: 7s - loss: 184.292 - ETA: 6s - loss: 184.075 - ETA: 5s - loss: 183.856 - ETA: 3s - loss: 183.633 - ETA: 2s - loss: 183.417 - ETA: 1s - loss: 183.200 - ETA: 0s - loss: 182.985 - 150s 1s/step - loss: 182.7751 - val_loss: 108.4188\n",
+ "\n",
+ "Epoch 00013: val_loss did not improve from 106.18616\n",
+ "Epoch 14/30\n",
+ "111/111 [==============================] - ETA: 2:25 - loss: 146.722 - ETA: 2:23 - loss: 332.896 - ETA: 2:23 - loss: 348.952 - ETA: 2:20 - loss: 345.968 - ETA: 2:19 - loss: 334.516 - ETA: 2:17 - loss: 321.618 - ETA: 2:16 - loss: 309.573 - ETA: 2:14 - loss: 298.440 - ETA: 2:15 - loss: 288.663 - ETA: 2:15 - loss: 279.566 - ETA: 2:14 - loss: 271.555 - ETA: 2:14 - loss: 278.917 - ETA: 2:13 - loss: 284.436 - ETA: 2:12 - loss: 287.830 - ETA: 2:14 - loss: 290.386 - ETA: 2:13 - loss: 291.696 - ETA: 2:13 - loss: 292.098 - ETA: 2:12 - loss: 291.795 - ETA: 2:11 - loss: 291.860 - ETA: 2:10 - loss: 291.399 - ETA: 2:08 - loss: 290.547 - ETA: 2:06 - loss: 289.389 - ETA: 2:04 - loss: 288.713 - ETA: 2:02 - loss: 287.760 - ETA: 2:01 - loss: 286.612 - ETA: 1:59 - loss: 285.404 - ETA: 1:57 - loss: 284.169 - ETA: 1:56 - loss: 283.211 - ETA: 1:54 - loss: 282.102 - ETA: 1:52 - loss: 280.905 - ETA: 1:51 - loss: 279.589 - ETA: 1:50 - loss: 278.192 - ETA: 1:48 - loss: 276.734 - ETA: 1:46 - loss: 275.242 - ETA: 1:45 - loss: 273.726 - ETA: 1:43 - loss: 272.188 - ETA: 1:42 - loss: 270.674 - ETA: 1:40 - loss: 269.225 - ETA: 1:39 - loss: 267.776 - ETA: 1:38 - loss: 266.355 - ETA: 1:36 - loss: 264.923 - ETA: 1:35 - loss: 263.478 - ETA: 1:33 - loss: 262.031 - ETA: 1:32 - loss: 260.597 - ETA: 1:30 - loss: 259.199 - ETA: 1:29 - loss: 257.803 - ETA: 1:28 - loss: 256.412 - ETA: 1:26 - loss: 255.044 - ETA: 1:25 - loss: 253.700 - ETA: 1:23 - loss: 252.365 - ETA: 1:22 - loss: 251.057 - ETA: 1:21 - loss: 249.770 - ETA: 1:19 - loss: 248.503 - ETA: 1:18 - loss: 247.247 - ETA: 1:16 - loss: 246.021 - ETA: 1:15 - loss: 244.811 - ETA: 1:14 - loss: 243.623 - ETA: 1:12 - loss: 242.454 - ETA: 1:11 - loss: 241.449 - ETA: 1:09 - loss: 240.445 - ETA: 1:08 - loss: 239.444 - ETA: 1:07 - loss: 238.451 - ETA: 1:05 - loss: 237.465 - ETA: 1:04 - loss: 236.546 - ETA: 1:02 - loss: 235.631 - ETA: 1:01 - loss: 234.721 - ETA: 1:00 - loss: 233.857 - ETA: 58s - loss: 233.002 - ETA: 57s - loss: 232.15 - ETA: 55s - loss: 231.31 - ETA: 54s - loss: 230.48 - ETA: 53s - loss: 229.65 - ETA: 51s - loss: 228.83 - ETA: 50s - loss: 228.02 - ETA: 49s - loss: 227.22 - ETA: 47s - loss: 226.43 - ETA: 46s - loss: 225.65 - ETA: 45s - loss: 224.94 - ETA: 43s - loss: 224.23 - ETA: 42s - loss: 223.52 - ETA: 40s - loss: 222.82 - ETA: 39s - loss: 222.12 - ETA: 38s - loss: 221.44 - ETA: 36s - loss: 220.76 - ETA: 35s - loss: 220.09 - ETA: 34s - loss: 219.44 - ETA: 32s - loss: 218.80 - ETA: 31s - loss: 218.16 - ETA: 29s - loss: 217.52 - ETA: 28s - loss: 216.89 - ETA: 27s - loss: 216.27 - ETA: 25s - loss: 215.66 - ETA: 24s - loss: 215.05 - ETA: 23s - loss: 214.44 - ETA: 21s - loss: 213.83 - ETA: 20s - loss: 213.23 - ETA: 19s - loss: 212.63 - ETA: 17s - loss: 212.05 - ETA: 16s - loss: 211.47 - ETA: 14s - loss: 210.90 - ETA: 13s - loss: 210.33 - ETA: 12s - loss: 209.77 - ETA: 10s - loss: 209.22 - ETA: 9s - loss: 208.7734 - ETA: 8s - loss: 208.321 - ETA: 6s - loss: 207.872 - ETA: 5s - loss: 207.424 - ETA: 4s - loss: 206.977 - ETA: 2s - loss: 206.540 - ETA: 1s - loss: 206.104 - ETA: 0s - loss: 205.672 - 153s 1s/step - loss: 205.2487 - val_loss: 99.0927\n",
+ "\n",
+ "Epoch 00014: val_loss improved from 106.18616 to 99.09274, saving model to superposition_injection.h5\n",
+ "Epoch 15/30\n",
+ "111/111 [==============================] - ETA: 2:34 - loss: 147.712 - ETA: 2:26 - loss: 135.654 - ETA: 2:26 - loss: 127.396 - ETA: 2:25 - loss: 126.024 - ETA: 2:23 - loss: 124.530 - ETA: 2:22 - loss: 123.111 - ETA: 2:20 - loss: 133.443 - ETA: 2:18 - loss: 139.747 - ETA: 2:17 - loss: 143.798 - ETA: 2:16 - loss: 146.032 - ETA: 2:14 - loss: 147.624 - ETA: 2:13 - loss: 148.494 - ETA: 2:12 - loss: 149.620 - ETA: 2:10 - loss: 150.126 - ETA: 2:09 - loss: 151.565 - ETA: 2:07 - loss: 152.623 - ETA: 2:06 - loss: 155.498 - ETA: 2:04 - loss: 157.693 - ETA: 2:03 - loss: 159.594 - ETA: 2:02 - loss: 161.206 - ETA: 2:00 - loss: 162.472 - ETA: 1:59 - loss: 163.431 - ETA: 1:58 - loss: 164.118 - ETA: 1:56 - loss: 164.597 - ETA: 1:55 - loss: 165.132 - ETA: 1:54 - loss: 165.465 - ETA: 1:52 - loss: 165.717 - ETA: 1:51 - loss: 165.831 - ETA: 1:50 - loss: 165.938 - ETA: 1:48 - loss: 165.941 - ETA: 1:47 - loss: 165.861 - ETA: 1:46 - loss: 165.710 - ETA: 1:44 - loss: 165.487 - ETA: 1:43 - loss: 165.285 - ETA: 1:42 - loss: 165.076 - ETA: 1:40 - loss: 164.811 - ETA: 1:39 - loss: 164.507 - ETA: 1:38 - loss: 164.173 - ETA: 1:36 - loss: 163.802 - ETA: 1:35 - loss: 163.406 - ETA: 1:34 - loss: 162.994 - ETA: 1:32 - loss: 162.582 - ETA: 1:31 - loss: 162.147 - ETA: 1:29 - loss: 161.702 - ETA: 1:28 - loss: 161.239 - ETA: 1:27 - loss: 160.760 - ETA: 1:25 - loss: 160.326 - ETA: 1:24 - loss: 160.932 - ETA: 1:23 - loss: 161.524 - ETA: 1:22 - loss: 162.054 - ETA: 1:20 - loss: 162.551 - ETA: 1:19 - loss: 163.000 - ETA: 1:18 - loss: 163.402 - ETA: 1:16 - loss: 163.768 - ETA: 1:15 - loss: 164.089 - ETA: 1:13 - loss: 164.426 - ETA: 1:12 - loss: 164.719 - ETA: 1:11 - loss: 164.987 - ETA: 1:09 - loss: 165.217 - ETA: 1:08 - loss: 165.423 - ETA: 1:07 - loss: 165.598 - ETA: 1:05 - loss: 165.749 - ETA: 1:04 - loss: 165.898 - ETA: 1:03 - loss: 166.025 - ETA: 1:01 - loss: 166.126 - ETA: 1:00 - loss: 166.196 - ETA: 59s - loss: 166.264 - ETA: 57s - loss: 166.32 - ETA: 56s - loss: 166.35 - ETA: 55s - loss: 166.37 - ETA: 53s - loss: 166.37 - ETA: 52s - loss: 166.36 - ETA: 51s - loss: 166.34 - ETA: 49s - loss: 166.31 - ETA: 48s - loss: 166.26 - ETA: 47s - loss: 166.20 - ETA: 45s - loss: 166.13 - ETA: 44s - loss: 166.09 - ETA: 42s - loss: 166.05 - ETA: 41s - loss: 165.99 - ETA: 40s - loss: 165.93 - ETA: 38s - loss: 165.87 - ETA: 37s - loss: 166.03 - ETA: 36s - loss: 166.17 - ETA: 34s - loss: 166.30 - ETA: 33s - loss: 166.44 - ETA: 32s - loss: 166.56 - ETA: 30s - loss: 166.67 - ETA: 29s - loss: 166.76 - ETA: 28s - loss: 166.87 - ETA: 26s - loss: 166.96 - ETA: 25s - loss: 167.04 - ETA: 24s - loss: 167.10 - ETA: 22s - loss: 167.17 - ETA: 21s - loss: 167.28 - ETA: 20s - loss: 167.36 - ETA: 18s - loss: 167.44 - ETA: 17s - loss: 167.52 - ETA: 16s - loss: 167.58 - ETA: 14s - loss: 167.63 - ETA: 13s - loss: 167.68 - ETA: 12s - loss: 167.71 - ETA: 10s - loss: 167.73 - ETA: 9s - loss: 167.7745 - ETA: 8s - loss: 167.807 - ETA: 6s - loss: 167.837 - ETA: 5s - loss: 167.860 - ETA: 4s - loss: 167.877 - ETA: 2s - loss: 167.885 - ETA: 1s - loss: 167.888 - ETA: 0s - loss: 167.893 - 151s 1s/step - loss: 167.8981 - val_loss: 107.8244\n",
+ "\n",
+ "Epoch 00015: val_loss did not improve from 99.09274\n",
+ "Epoch 16/30\n",
+ "111/111 [==============================] - ETA: 2:29 - loss: 202.463 - ETA: 2:25 - loss: 172.750 - ETA: 2:26 - loss: 154.390 - ETA: 2:23 - loss: 157.067 - ETA: 2:21 - loss: 155.083 - ETA: 2:20 - loss: 152.122 - ETA: 2:19 - loss: 148.787 - ETA: 2:17 - loss: 145.856 - ETA: 2:16 - loss: 142.838 - ETA: 2:15 - loss: 140.062 - ETA: 2:13 - loss: 137.469 - ETA: 2:12 - loss: 135.141 - ETA: 2:11 - loss: 133.068 - ETA: 2:09 - loss: 131.137 - ETA: 2:09 - loss: 129.382 - ETA: 2:07 - loss: 127.771 - ETA: 2:06 - loss: 126.198 - ETA: 2:06 - loss: 124.744 - ETA: 2:06 - loss: 123.451 - ETA: 2:04 - loss: 122.273 - ETA: 2:02 - loss: 121.343 - ETA: 2:00 - loss: 120.871 - ETA: 1:59 - loss: 120.388 - ETA: 1:58 - loss: 119.913 - ETA: 1:57 - loss: 119.706 - ETA: 1:55 - loss: 119.483 - ETA: 1:54 - loss: 119.245 - ETA: 1:53 - loss: 119.090 - ETA: 1:51 - loss: 119.495 - ETA: 1:50 - loss: 119.824 - ETA: 1:48 - loss: 120.098 - ETA: 1:47 - loss: 120.341 - ETA: 1:45 - loss: 120.659 - ETA: 1:44 - loss: 120.909 - ETA: 1:43 - loss: 121.114 - ETA: 1:41 - loss: 121.428 - ETA: 1:40 - loss: 121.697 - ETA: 1:38 - loss: 121.913 - ETA: 1:37 - loss: 122.115 - ETA: 1:36 - loss: 122.279 - ETA: 1:34 - loss: 122.428 - ETA: 1:33 - loss: 122.555 - ETA: 1:31 - loss: 122.643 - ETA: 1:30 - loss: 122.705 - ETA: 1:29 - loss: 122.731 - ETA: 1:27 - loss: 122.750 - ETA: 1:26 - loss: 122.758 - ETA: 1:25 - loss: 122.752 - ETA: 1:23 - loss: 122.733 - ETA: 1:22 - loss: 122.693 - ETA: 1:21 - loss: 122.644 - ETA: 1:19 - loss: 122.593 - ETA: 1:18 - loss: 122.543 - ETA: 1:17 - loss: 122.480 - ETA: 1:15 - loss: 122.418 - ETA: 1:14 - loss: 122.360 - ETA: 1:12 - loss: 122.298 - ETA: 1:11 - loss: 122.284 - ETA: 1:10 - loss: 122.256 - ETA: 1:08 - loss: 122.215 - ETA: 1:07 - loss: 122.167 - ETA: 1:06 - loss: 122.110 - ETA: 1:04 - loss: 122.133 - ETA: 1:03 - loss: 122.153 - ETA: 1:01 - loss: 122.162 - ETA: 1:00 - loss: 122.161 - ETA: 59s - loss: 122.158 - ETA: 57s - loss: 122.14 - ETA: 56s - loss: 122.12 - ETA: 55s - loss: 122.11 - ETA: 53s - loss: 122.09 - ETA: 52s - loss: 122.08 - ETA: 51s - loss: 122.05 - ETA: 49s - loss: 122.01 - ETA: 48s - loss: 121.97 - ETA: 47s - loss: 121.94 - ETA: 45s - loss: 121.90 - ETA: 44s - loss: 121.86 - ETA: 43s - loss: 121.84 - ETA: 41s - loss: 121.81 - ETA: 40s - loss: 121.82 - ETA: 39s - loss: 121.82 - ETA: 37s - loss: 121.82 - ETA: 36s - loss: 121.81 - ETA: 35s - loss: 121.80 - ETA: 33s - loss: 121.79 - ETA: 32s - loss: 121.79 - ETA: 30s - loss: 121.79 - ETA: 29s - loss: 121.83 - ETA: 28s - loss: 121.86 - ETA: 26s - loss: 121.89 - ETA: 25s - loss: 121.92 - ETA: 24s - loss: 121.95 - ETA: 22s - loss: 122.00 - ETA: 21s - loss: 122.05 - ETA: 20s - loss: 122.09 - ETA: 18s - loss: 122.13 - ETA: 17s - loss: 122.17 - ETA: 16s - loss: 122.20 - ETA: 14s - loss: 122.25 - ETA: 13s - loss: 122.29 - ETA: 12s - loss: 122.33 - ETA: 10s - loss: 122.37 - ETA: 9s - loss: 122.4096 - ETA: 8s - loss: 122.440 - ETA: 6s - loss: 122.470 - ETA: 5s - loss: 122.494 - ETA: 4s - loss: 122.520 - ETA: 2s - loss: 122.543 - ETA: 1s - loss: 122.584 - ETA: 0s - loss: 122.632 - 151s 1s/step - loss: 122.6794 - val_loss: 117.5344\n",
+ "\n",
+ "Epoch 00016: val_loss did not improve from 99.09274\n",
+ "Epoch 17/30\n",
+ "111/111 [==============================] - ETA: 2:28 - loss: 88.85 - ETA: 2:30 - loss: 90.48 - ETA: 2:28 - loss: 101.760 - ETA: 2:27 - loss: 106.762 - ETA: 2:25 - loss: 125.302 - ETA: 2:22 - loss: 139.005 - ETA: 2:21 - loss: 145.949 - ETA: 2:19 - loss: 149.842 - ETA: 2:17 - loss: 152.482 - ETA: 2:16 - loss: 154.351 - ETA: 2:14 - loss: 155.135 - ETA: 2:13 - loss: 155.382 - ETA: 2:11 - loss: 155.163 - ETA: 2:10 - loss: 155.206 - ETA: 2:08 - loss: 155.095 - ETA: 2:07 - loss: 154.706 - ETA: 2:06 - loss: 154.107 - ETA: 2:04 - loss: 153.419 - ETA: 2:03 - loss: 152.652 - ETA: 2:01 - loss: 152.058 - ETA: 2:00 - loss: 151.414 - ETA: 1:59 - loss: 150.720 - ETA: 1:57 - loss: 150.028 - ETA: 1:56 - loss: 149.580 - ETA: 1:55 - loss: 149.072 - ETA: 1:53 - loss: 148.507 - ETA: 1:52 - loss: 147.958 - ETA: 1:50 - loss: 147.402 - ETA: 1:49 - loss: 146.807 - ETA: 1:48 - loss: 146.235 - ETA: 1:46 - loss: 145.830 - ETA: 1:45 - loss: 145.393 - ETA: 1:44 - loss: 144.946 - ETA: 1:42 - loss: 144.510 - ETA: 1:41 - loss: 144.070 - ETA: 1:40 - loss: 143.620 - ETA: 1:38 - loss: 143.212 - ETA: 1:37 - loss: 142.791 - ETA: 1:36 - loss: 142.364 - ETA: 1:34 - loss: 141.926 - ETA: 1:33 - loss: 141.483 - ETA: 1:32 - loss: 141.027 - ETA: 1:30 - loss: 140.578 - ETA: 1:29 - loss: 140.353 - ETA: 1:28 - loss: 140.126 - ETA: 1:26 - loss: 139.889 - ETA: 1:25 - loss: 139.632 - ETA: 1:24 - loss: 139.418 - ETA: 1:22 - loss: 139.207 - ETA: 1:21 - loss: 139.003 - ETA: 1:20 - loss: 138.792 - ETA: 1:18 - loss: 138.626 - ETA: 1:17 - loss: 138.466 - ETA: 1:16 - loss: 138.322 - ETA: 1:14 - loss: 138.168 - ETA: 1:13 - loss: 138.008 - ETA: 1:12 - loss: 137.838 - ETA: 1:10 - loss: 137.922 - ETA: 1:09 - loss: 137.994 - ETA: 1:07 - loss: 138.048 - ETA: 1:06 - loss: 138.087 - ETA: 1:05 - loss: 138.118 - ETA: 1:03 - loss: 138.134 - ETA: 1:02 - loss: 138.135 - ETA: 1:01 - loss: 138.144 - ETA: 1:00 - loss: 138.149 - ETA: 58s - loss: 138.220 - ETA: 57s - loss: 138.27 - ETA: 55s - loss: 138.39 - ETA: 54s - loss: 138.90 - ETA: 53s - loss: 139.37 - ETA: 51s - loss: 139.83 - ETA: 50s - loss: 140.26 - ETA: 49s - loss: 140.66 - ETA: 47s - loss: 141.04 - ETA: 46s - loss: 141.42 - ETA: 45s - loss: 141.77 - ETA: 43s - loss: 142.10 - ETA: 42s - loss: 142.41 - ETA: 41s - loss: 142.71 - ETA: 39s - loss: 142.99 - ETA: 38s - loss: 143.26 - ETA: 37s - loss: 143.61 - ETA: 35s - loss: 143.95 - ETA: 34s - loss: 144.28 - ETA: 33s - loss: 144.58 - ETA: 31s - loss: 144.88 - ETA: 30s - loss: 145.15 - ETA: 29s - loss: 145.41 - ETA: 27s - loss: 145.68 - ETA: 26s - loss: 145.94 - ETA: 25s - loss: 146.19 - ETA: 24s - loss: 146.42 - ETA: 22s - loss: 146.63 - ETA: 21s - loss: 146.84 - ETA: 20s - loss: 147.04 - ETA: 18s - loss: 147.23 - ETA: 17s - loss: 147.41 - ETA: 16s - loss: 147.58 - ETA: 14s - loss: 147.74 - ETA: 13s - loss: 147.88 - ETA: 12s - loss: 148.02 - ETA: 10s - loss: 148.15 - ETA: 9s - loss: 148.2798 - ETA: 8s - loss: 148.391 - ETA: 6s - loss: 148.494 - ETA: 5s - loss: 148.589 - ETA: 4s - loss: 148.727 - ETA: 2s - loss: 148.856 - ETA: 1s - loss: 148.977 - ETA: 0s - loss: 149.096 - 150s 1s/step - loss: 149.2119 - val_loss: 92.6297\n",
+ "\n",
+ "Epoch 00017: val_loss improved from 99.09274 to 92.62974, saving model to superposition_injection.h5\n",
+ "Epoch 18/30\n",
+ "111/111 [==============================] - ETA: 2:32 - loss: 91.35 - ETA: 2:28 - loss: 88.06 - ETA: 2:24 - loss: 86.84 - ETA: 2:21 - loss: 86.44 - ETA: 2:21 - loss: 86.15 - ETA: 2:19 - loss: 86.75 - ETA: 2:18 - loss: 87.13 - ETA: 2:16 - loss: 87.70 - ETA: 2:15 - loss: 87.99 - ETA: 2:13 - loss: 89.37 - ETA: 2:12 - loss: 90.35 - ETA: 2:11 - loss: 91.74 - ETA: 2:09 - loss: 92.77 - ETA: 2:09 - loss: 93.62 - ETA: 2:07 - loss: 94.29 - ETA: 2:05 - loss: 94.77 - ETA: 2:04 - loss: 95.17 - ETA: 2:03 - loss: 95.56 - ETA: 2:01 - loss: 95.87 - ETA: 2:00 - loss: 96.18 - ETA: 1:59 - loss: 96.43 - ETA: 1:58 - loss: 97.05 - ETA: 1:56 - loss: 97.56 - ETA: 1:55 - loss: 98.02 - ETA: 1:53 - loss: 98.43 - ETA: 1:52 - loss: 98.78 - ETA: 1:51 - loss: 99.03 - ETA: 1:49 - loss: 99.23 - ETA: 1:48 - loss: 99.40 - ETA: 1:47 - loss: 99.54 - ETA: 1:46 - loss: 99.66 - ETA: 1:44 - loss: 99.75 - ETA: 1:43 - loss: 99.80 - ETA: 1:42 - loss: 99.85 - ETA: 1:40 - loss: 99.97 - ETA: 1:39 - loss: 100.083 - ETA: 1:38 - loss: 100.173 - ETA: 1:36 - loss: 100.270 - ETA: 1:35 - loss: 100.361 - ETA: 1:34 - loss: 100.439 - ETA: 1:32 - loss: 100.515 - ETA: 1:31 - loss: 100.606 - ETA: 1:30 - loss: 100.683 - ETA: 1:28 - loss: 100.746 - ETA: 1:27 - loss: 100.794 - ETA: 1:26 - loss: 100.845 - ETA: 1:25 - loss: 100.889 - ETA: 1:23 - loss: 100.925 - ETA: 1:22 - loss: 101.116 - ETA: 1:21 - loss: 101.297 - ETA: 1:19 - loss: 101.457 - ETA: 1:18 - loss: 101.602 - ETA: 1:17 - loss: 101.729 - ETA: 1:15 - loss: 101.854 - ETA: 1:14 - loss: 101.981 - ETA: 1:13 - loss: 102.190 - ETA: 1:11 - loss: 102.408 - ETA: 1:10 - loss: 102.680 - ETA: 1:09 - loss: 102.927 - ETA: 1:07 - loss: 103.159 - ETA: 1:06 - loss: 103.373 - ETA: 1:05 - loss: 103.580 - ETA: 1:03 - loss: 103.771 - ETA: 1:02 - loss: 103.952 - ETA: 1:01 - loss: 104.140 - ETA: 59s - loss: 104.313 - ETA: 58s - loss: 104.54 - ETA: 57s - loss: 104.78 - ETA: 55s - loss: 105.00 - ETA: 54s - loss: 105.24 - ETA: 53s - loss: 105.46 - ETA: 51s - loss: 105.67 - ETA: 50s - loss: 106.05 - ETA: 49s - loss: 106.41 - ETA: 47s - loss: 106.76 - ETA: 46s - loss: 107.09 - ETA: 45s - loss: 107.40 - ETA: 43s - loss: 107.70 - ETA: 42s - loss: 107.99 - ETA: 41s - loss: 108.27 - ETA: 39s - loss: 108.54 - ETA: 38s - loss: 108.79 - ETA: 37s - loss: 109.03 - ETA: 35s - loss: 109.26 - ETA: 34s - loss: 109.48 - ETA: 33s - loss: 109.70 - ETA: 31s - loss: 109.90 - ETA: 30s - loss: 110.09 - ETA: 29s - loss: 110.43 - ETA: 27s - loss: 110.75 - ETA: 26s - loss: 111.06 - ETA: 25s - loss: 111.36 - ETA: 23s - loss: 111.65 - ETA: 22s - loss: 111.93 - ETA: 21s - loss: 112.20 - ETA: 19s - loss: 112.46 - ETA: 18s - loss: 112.71 - ETA: 17s - loss: 112.94 - ETA: 15s - loss: 113.17 - ETA: 14s - loss: 113.40 - ETA: 13s - loss: 113.61 - ETA: 11s - loss: 113.85 - ETA: 10s - loss: 114.08 - ETA: 9s - loss: 114.3024 - ETA: 7s - loss: 114.515 - ETA: 6s - loss: 114.719 - ETA: 5s - loss: 114.916 - ETA: 3s - loss: 115.104 - ETA: 2s - loss: 115.286 - ETA: 1s - loss: 115.548 - ETA: 0s - loss: 115.802 - 150s 1s/step - loss: 116.0522 - val_loss: 117.4844\n",
+ "\n",
+ "Epoch 00018: val_loss did not improve from 92.62974\n",
+ "Epoch 19/30\n",
+ "111/111 [==============================] - ETA: 2:24 - loss: 97.81 - ETA: 2:32 - loss: 91.71 - ETA: 2:26 - loss: 119.416 - ETA: 2:23 - loss: 127.457 - ETA: 2:22 - loss: 129.186 - ETA: 2:21 - loss: 129.065 - ETA: 2:19 - loss: 128.200 - ETA: 2:18 - loss: 126.814 - ETA: 2:16 - loss: 125.214 - ETA: 2:14 - loss: 123.554 - ETA: 2:14 - loss: 122.407 - ETA: 2:12 - loss: 121.299 - ETA: 2:10 - loss: 120.445 - ETA: 2:09 - loss: 119.641 - ETA: 2:08 - loss: 118.786 - ETA: 2:06 - loss: 117.926 - ETA: 2:05 - loss: 117.144 - ETA: 2:04 - loss: 116.431 - ETA: 2:02 - loss: 115.677 - ETA: 2:01 - loss: 114.960 - ETA: 2:00 - loss: 114.286 - ETA: 1:58 - loss: 113.688 - ETA: 1:57 - loss: 113.115 - ETA: 1:55 - loss: 112.817 - ETA: 1:54 - loss: 112.504 - ETA: 1:53 - loss: 112.188 - ETA: 1:51 - loss: 112.084 - ETA: 1:50 - loss: 111.947 - ETA: 1:49 - loss: 111.826 - ETA: 1:47 - loss: 111.675 - ETA: 1:46 - loss: 111.518 - ETA: 1:45 - loss: 111.343 - ETA: 1:43 - loss: 111.165 - ETA: 1:42 - loss: 110.998 - ETA: 1:41 - loss: 110.846 - ETA: 1:39 - loss: 110.752 - ETA: 1:38 - loss: 110.651 - ETA: 1:37 - loss: 110.545 - ETA: 1:35 - loss: 110.443 - ETA: 1:34 - loss: 110.332 - ETA: 1:33 - loss: 110.225 - ETA: 1:31 - loss: 110.108 - ETA: 1:30 - loss: 109.993 - ETA: 1:28 - loss: 109.875 - ETA: 1:27 - loss: 109.783 - ETA: 1:26 - loss: 109.688 - ETA: 1:24 - loss: 109.629 - ETA: 1:23 - loss: 109.563 - ETA: 1:22 - loss: 109.488 - ETA: 1:20 - loss: 109.410 - ETA: 1:19 - loss: 109.337 - ETA: 1:18 - loss: 109.254 - ETA: 1:16 - loss: 109.275 - ETA: 1:15 - loss: 109.287 - ETA: 1:14 - loss: 109.323 - ETA: 1:13 - loss: 109.353 - ETA: 1:11 - loss: 109.374 - ETA: 1:10 - loss: 109.384 - ETA: 1:09 - loss: 109.388 - ETA: 1:07 - loss: 109.442 - ETA: 1:06 - loss: 109.577 - ETA: 1:05 - loss: 109.700 - ETA: 1:03 - loss: 109.810 - ETA: 1:02 - loss: 109.925 - ETA: 1:01 - loss: 110.026 - ETA: 59s - loss: 110.117 - ETA: 58s - loss: 110.19 - ETA: 57s - loss: 110.27 - ETA: 55s - loss: 110.34 - ETA: 54s - loss: 110.41 - ETA: 53s - loss: 110.48 - ETA: 51s - loss: 110.55 - ETA: 50s - loss: 110.62 - ETA: 49s - loss: 110.68 - ETA: 47s - loss: 110.73 - ETA: 46s - loss: 110.77 - ETA: 45s - loss: 110.84 - ETA: 43s - loss: 110.91 - ETA: 42s - loss: 110.97 - ETA: 41s - loss: 111.24 - ETA: 39s - loss: 111.49 - ETA: 38s - loss: 111.74 - ETA: 37s - loss: 111.97 - ETA: 35s - loss: 112.19 - ETA: 34s - loss: 112.40 - ETA: 33s - loss: 112.60 - ETA: 31s - loss: 112.79 - ETA: 30s - loss: 112.97 - ETA: 29s - loss: 113.14 - ETA: 27s - loss: 113.32 - ETA: 26s - loss: 113.48 - ETA: 25s - loss: 113.65 - ETA: 23s - loss: 113.81 - ETA: 22s - loss: 113.96 - ETA: 21s - loss: 114.11 - ETA: 19s - loss: 114.25 - ETA: 18s - loss: 114.38 - ETA: 17s - loss: 114.51 - ETA: 15s - loss: 114.64 - ETA: 14s - loss: 114.77 - ETA: 13s - loss: 114.90 - ETA: 11s - loss: 115.02 - ETA: 10s - loss: 115.14 - ETA: 9s - loss: 115.2606 - ETA: 7s - loss: 115.404 - ETA: 6s - loss: 115.545 - ETA: 5s - loss: 115.677 - ETA: 3s - loss: 115.803 - ETA: 2s - loss: 115.926 - ETA: 1s - loss: 116.042 - ETA: 0s - loss: 116.155 - 150s 1s/step - loss: 116.2664 - val_loss: 92.3930\n",
+ "\n",
+ "Epoch 00019: val_loss improved from 92.62974 to 92.39297, saving model to superposition_injection.h5\n",
+ "Epoch 20/30\n",
+ "111/111 [==============================] - ETA: 2:33 - loss: 104.208 - ETA: 2:21 - loss: 97.155 - ETA: 2:22 - loss: 96.53 - ETA: 2:20 - loss: 96.10 - ETA: 2:18 - loss: 95.17 - ETA: 2:17 - loss: 96.59 - ETA: 2:16 - loss: 97.77 - ETA: 2:15 - loss: 98.38 - ETA: 2:15 - loss: 99.50 - ETA: 2:13 - loss: 100.082 - ETA: 2:11 - loss: 100.721 - ETA: 2:10 - loss: 101.327 - ETA: 2:09 - loss: 102.881 - ETA: 2:07 - loss: 103.983 - ETA: 2:06 - loss: 104.855 - ETA: 2:05 - loss: 105.565 - ETA: 2:03 - loss: 106.516 - ETA: 2:02 - loss: 107.263 - ETA: 2:01 - loss: 107.886 - ETA: 2:00 - loss: 108.412 - ETA: 1:58 - loss: 108.803 - ETA: 1:57 - loss: 109.298 - ETA: 1:55 - loss: 109.708 - ETA: 1:54 - loss: 110.073 - ETA: 1:53 - loss: 110.413 - ETA: 1:52 - loss: 110.686 - ETA: 1:51 - loss: 113.261 - ETA: 1:50 - loss: 115.561 - ETA: 1:48 - loss: 117.609 - ETA: 1:47 - loss: 119.460 - ETA: 1:46 - loss: 121.138 - ETA: 1:44 - loss: 122.639 - ETA: 1:43 - loss: 123.986 - ETA: 1:42 - loss: 125.195 - ETA: 1:40 - loss: 126.261 - ETA: 1:39 - loss: 127.219 - ETA: 1:37 - loss: 128.066 - ETA: 1:36 - loss: 128.822 - ETA: 1:35 - loss: 129.507 - ETA: 1:33 - loss: 130.124 - ETA: 1:32 - loss: 130.668 - ETA: 1:31 - loss: 131.147 - ETA: 1:30 - loss: 131.585 - ETA: 1:28 - loss: 131.980 - ETA: 1:27 - loss: 132.319 - ETA: 1:26 - loss: 132.608 - ETA: 1:24 - loss: 132.863 - ETA: 1:23 - loss: 133.230 - ETA: 1:22 - loss: 133.556 - ETA: 1:21 - loss: 133.840 - ETA: 1:19 - loss: 134.140 - ETA: 1:18 - loss: 134.417 - ETA: 1:17 - loss: 134.691 - ETA: 1:15 - loss: 134.935 - ETA: 1:14 - loss: 135.144 - ETA: 1:13 - loss: 135.324 - ETA: 1:11 - loss: 135.476 - ETA: 1:10 - loss: 135.608 - ETA: 1:09 - loss: 135.723 - ETA: 1:07 - loss: 135.835 - ETA: 1:06 - loss: 135.967 - ETA: 1:05 - loss: 136.079 - ETA: 1:03 - loss: 136.175 - ETA: 1:02 - loss: 136.262 - ETA: 1:01 - loss: 136.339 - ETA: 59s - loss: 136.404 - ETA: 58s - loss: 136.45 - ETA: 57s - loss: 136.49 - ETA: 55s - loss: 136.55 - ETA: 54s - loss: 136.60 - ETA: 53s - loss: 136.64 - ETA: 51s - loss: 136.68 - ETA: 50s - loss: 136.71 - ETA: 49s - loss: 136.74 - ETA: 48s - loss: 136.76 - ETA: 46s - loss: 136.77 - ETA: 45s - loss: 136.79 - ETA: 43s - loss: 136.80 - ETA: 42s - loss: 136.81 - ETA: 41s - loss: 136.81 - ETA: 39s - loss: 136.81 - ETA: 38s - loss: 136.80 - ETA: 37s - loss: 136.85 - ETA: 35s - loss: 136.88 - ETA: 34s - loss: 136.91 - ETA: 33s - loss: 136.94 - ETA: 32s - loss: 136.95 - ETA: 30s - loss: 136.96 - ETA: 29s - loss: 136.96 - ETA: 27s - loss: 137.04 - ETA: 26s - loss: 137.11 - ETA: 25s - loss: 137.18 - ETA: 23s - loss: 137.24 - ETA: 22s - loss: 137.29 - ETA: 21s - loss: 137.33 - ETA: 19s - loss: 137.38 - ETA: 18s - loss: 137.42 - ETA: 17s - loss: 137.45 - ETA: 15s - loss: 137.48 - ETA: 14s - loss: 137.50 - ETA: 13s - loss: 137.52 - ETA: 11s - loss: 137.53 - ETA: 10s - loss: 137.54 - ETA: 9s - loss: 137.5629 - ETA: 7s - loss: 137.575 - ETA: 6s - loss: 137.583 - ETA: 5s - loss: 137.587 - ETA: 3s - loss: 137.586 - ETA: 2s - loss: 137.579 - ETA: 1s - loss: 137.570 - ETA: 0s - loss: 137.560 - 150s 1s/step - loss: 137.5507 - val_loss: 110.4724\n",
+ "\n",
+ "Epoch 00020: val_loss did not improve from 92.39297\n",
+ "Epoch 21/30\n",
+ "111/111 [==============================] - ETA: 2:28 - loss: 95.65 - ETA: 2:33 - loss: 93.54 - ETA: 2:29 - loss: 101.915 - ETA: 2:29 - loss: 103.657 - ETA: 2:28 - loss: 105.947 - ETA: 2:27 - loss: 106.409 - ETA: 2:24 - loss: 107.339 - ETA: 2:23 - loss: 107.401 - ETA: 2:20 - loss: 107.404 - ETA: 2:18 - loss: 107.216 - ETA: 2:17 - loss: 107.413 - ETA: 2:14 - loss: 108.261 - ETA: 2:12 - loss: 108.804 - ETA: 2:11 - loss: 109.067 - ETA: 2:09 - loss: 109.369 - ETA: 2:08 - loss: 109.515 - ETA: 2:06 - loss: 109.621 - ETA: 2:05 - loss: 109.791 - ETA: 2:03 - loss: 109.865 - ETA: 2:02 - loss: 109.883 - ETA: 2:01 - loss: 109.863 - ETA: 1:59 - loss: 109.769 - ETA: 1:58 - loss: 109.674 - ETA: 1:56 - loss: 109.562 - ETA: 1:55 - loss: 109.420 - ETA: 1:54 - loss: 109.252 - ETA: 1:52 - loss: 109.089 - ETA: 1:51 - loss: 109.364 - ETA: 1:49 - loss: 109.627 - ETA: 1:48 - loss: 109.853 - ETA: 1:46 - loss: 110.033 - ETA: 1:45 - loss: 110.190 - ETA: 1:44 - loss: 110.299 - ETA: 1:42 - loss: 110.824 - ETA: 1:41 - loss: 111.301 - ETA: 1:40 - loss: 111.715 - ETA: 1:38 - loss: 112.315 - ETA: 1:37 - loss: 112.853 - ETA: 1:36 - loss: 113.343 - ETA: 1:34 - loss: 113.784 - ETA: 1:33 - loss: 114.203 - ETA: 1:32 - loss: 114.584 - ETA: 1:30 - loss: 114.919 - ETA: 1:29 - loss: 115.221 - ETA: 1:28 - loss: 115.625 - ETA: 1:26 - loss: 115.999 - ETA: 1:25 - loss: 116.335 - ETA: 1:24 - loss: 116.648 - ETA: 1:22 - loss: 116.941 - ETA: 1:21 - loss: 117.434 - ETA: 1:20 - loss: 117.899 - ETA: 1:18 - loss: 118.331 - ETA: 1:17 - loss: 118.732 - ETA: 1:16 - loss: 119.145 - ETA: 1:14 - loss: 119.564 - ETA: 1:13 - loss: 119.953 - ETA: 1:12 - loss: 120.375 - ETA: 1:10 - loss: 120.771 - ETA: 1:09 - loss: 121.135 - ETA: 1:08 - loss: 121.475 - ETA: 1:06 - loss: 121.788 - ETA: 1:05 - loss: 122.073 - ETA: 1:04 - loss: 122.334 - ETA: 1:02 - loss: 122.600 - ETA: 1:01 - loss: 122.860 - ETA: 59s - loss: 123.105 - ETA: 58s - loss: 123.33 - ETA: 57s - loss: 124.10 - ETA: 55s - loss: 124.83 - ETA: 54s - loss: 125.52 - ETA: 53s - loss: 126.18 - ETA: 51s - loss: 126.80 - ETA: 50s - loss: 127.40 - ETA: 49s - loss: 127.96 - ETA: 47s - loss: 128.50 - ETA: 46s - loss: 129.01 - ETA: 45s - loss: 129.49 - ETA: 43s - loss: 129.99 - ETA: 42s - loss: 130.46 - ETA: 41s - loss: 130.92 - ETA: 39s - loss: 131.35 - ETA: 38s - loss: 131.76 - ETA: 37s - loss: 132.15 - ETA: 35s - loss: 132.55 - ETA: 34s - loss: 132.92 - ETA: 33s - loss: 133.29 - ETA: 31s - loss: 133.63 - ETA: 30s - loss: 133.96 - ETA: 29s - loss: 134.26 - ETA: 27s - loss: 134.56 - ETA: 26s - loss: 134.84 - ETA: 25s - loss: 135.10 - ETA: 23s - loss: 135.35 - ETA: 22s - loss: 135.60 - ETA: 21s - loss: 135.82 - ETA: 19s - loss: 136.04 - ETA: 18s - loss: 136.24 - ETA: 17s - loss: 136.43 - ETA: 15s - loss: 136.61 - ETA: 14s - loss: 136.79 - ETA: 13s - loss: 136.95 - ETA: 11s - loss: 137.12 - ETA: 10s - loss: 137.28 - ETA: 9s - loss: 137.4316 - ETA: 8s - loss: 137.577 - ETA: 6s - loss: 137.715 - ETA: 5s - loss: 137.846 - ETA: 4s - loss: 137.981 - ETA: 2s - loss: 138.110 - ETA: 1s - loss: 138.232 - ETA: 0s - loss: 138.355 - 151s 1s/step - loss: 138.4755 - val_loss: 110.9966\n",
+ "\n",
+ "Epoch 00021: val_loss did not improve from 92.39297\n",
+ "Epoch 22/30\n",
+ "111/111 [==============================] - ETA: 2:28 - loss: 278.561 - ETA: 2:29 - loss: 258.021 - ETA: 2:28 - loss: 240.086 - ETA: 2:27 - loss: 223.705 - ETA: 2:27 - loss: 211.013 - ETA: 2:29 - loss: 201.590 - ETA: 2:27 - loss: 193.260 - ETA: 2:25 - loss: 186.123 - ETA: 2:22 - loss: 180.075 - ETA: 2:20 - loss: 175.864 - ETA: 2:18 - loss: 171.934 - ETA: 2:16 - loss: 168.271 - ETA: 2:14 - loss: 164.966 - ETA: 2:13 - loss: 161.903 - ETA: 2:11 - loss: 159.122 - ETA: 2:09 - loss: 156.549 - ETA: 2:07 - loss: 154.121 - ETA: 2:05 - loss: 151.876 - ETA: 2:04 - loss: 149.790 - ETA: 2:02 - loss: 147.962 - ETA: 2:01 - loss: 146.338 - ETA: 2:00 - loss: 144.949 - ETA: 1:58 - loss: 143.671 - ETA: 1:57 - loss: 142.439 - ETA: 1:55 - loss: 141.269 - ETA: 1:54 - loss: 140.158 - ETA: 1:52 - loss: 139.073 - ETA: 1:51 - loss: 138.079 - ETA: 1:50 - loss: 137.151 - ETA: 1:48 - loss: 136.251 - ETA: 1:47 - loss: 135.396 - ETA: 1:45 - loss: 134.607 - ETA: 1:44 - loss: 133.875 - ETA: 1:42 - loss: 133.202 - ETA: 1:41 - loss: 132.573 - ETA: 1:40 - loss: 131.951 - ETA: 1:38 - loss: 131.340 - ETA: 1:37 - loss: 130.754 - ETA: 1:35 - loss: 130.205 - ETA: 1:34 - loss: 129.681 - ETA: 1:33 - loss: 129.172 - ETA: 1:31 - loss: 128.674 - ETA: 1:30 - loss: 128.189 - ETA: 1:29 - loss: 127.720 - ETA: 1:27 - loss: 127.273 - ETA: 1:26 - loss: 126.837 - ETA: 1:25 - loss: 126.412 - ETA: 1:23 - loss: 125.999 - ETA: 1:22 - loss: 125.704 - ETA: 1:21 - loss: 125.431 - ETA: 1:19 - loss: 125.167 - ETA: 1:18 - loss: 124.909 - ETA: 1:17 - loss: 124.654 - ETA: 1:15 - loss: 124.418 - ETA: 1:14 - loss: 124.184 - ETA: 1:13 - loss: 123.962 - ETA: 1:11 - loss: 123.748 - ETA: 1:10 - loss: 123.532 - ETA: 1:09 - loss: 123.312 - ETA: 1:07 - loss: 123.091 - ETA: 1:06 - loss: 122.872 - ETA: 1:05 - loss: 122.663 - ETA: 1:03 - loss: 122.451 - ETA: 1:02 - loss: 122.239 - ETA: 1:01 - loss: 122.029 - ETA: 59s - loss: 121.825 - ETA: 58s - loss: 121.62 - ETA: 57s - loss: 121.42 - ETA: 55s - loss: 121.22 - ETA: 54s - loss: 121.03 - ETA: 53s - loss: 120.83 - ETA: 51s - loss: 120.64 - ETA: 50s - loss: 120.45 - ETA: 49s - loss: 120.27 - ETA: 47s - loss: 120.09 - ETA: 46s - loss: 119.92 - ETA: 45s - loss: 119.74 - ETA: 43s - loss: 119.57 - ETA: 42s - loss: 119.41 - ETA: 41s - loss: 119.25 - ETA: 39s - loss: 119.09 - ETA: 38s - loss: 118.93 - ETA: 37s - loss: 118.77 - ETA: 35s - loss: 118.61 - ETA: 34s - loss: 118.46 - ETA: 33s - loss: 118.30 - ETA: 31s - loss: 118.15 - ETA: 30s - loss: 118.00 - ETA: 29s - loss: 117.85 - ETA: 27s - loss: 117.70 - ETA: 26s - loss: 117.55 - ETA: 25s - loss: 117.45 - ETA: 23s - loss: 117.35 - ETA: 22s - loss: 117.25 - ETA: 21s - loss: 117.18 - ETA: 19s - loss: 117.10 - ETA: 18s - loss: 117.03 - ETA: 17s - loss: 116.96 - ETA: 15s - loss: 116.89 - ETA: 14s - loss: 116.82 - ETA: 13s - loss: 116.80 - ETA: 11s - loss: 116.77 - ETA: 10s - loss: 116.74 - ETA: 9s - loss: 116.7234 - ETA: 7s - loss: 116.697 - ETA: 6s - loss: 116.670 - ETA: 5s - loss: 116.640 - ETA: 3s - loss: 116.759 - ETA: 2s - loss: 116.873 - ETA: 1s - loss: 116.982 - ETA: 0s - loss: 117.088 - 149s 1s/step - loss: 117.1932 - val_loss: 97.2366\n",
+ "\n",
+ "Epoch 00022: val_loss did not improve from 92.39297\n",
+ "Epoch 23/30\n",
+ "111/111 [==============================] - ETA: 2:24 - loss: 88.72 - ETA: 2:21 - loss: 84.85 - ETA: 2:20 - loss: 101.767 - ETA: 2:20 - loss: 107.813 - ETA: 2:18 - loss: 111.063 - ETA: 2:17 - loss: 113.210 - ETA: 2:16 - loss: 114.267 - ETA: 2:14 - loss: 114.742 - ETA: 2:13 - loss: 114.764 - ETA: 2:11 - loss: 114.672 - ETA: 2:10 - loss: 115.064 - ETA: 2:09 - loss: 115.157 - ETA: 2:08 - loss: 115.203 - ETA: 2:06 - loss: 115.053 - ETA: 2:05 - loss: 114.757 - ETA: 2:03 - loss: 114.401 - ETA: 2:02 - loss: 114.012 - ETA: 2:01 - loss: 113.728 - ETA: 2:00 - loss: 113.457 - ETA: 1:58 - loss: 113.163 - ETA: 1:57 - loss: 113.178 - ETA: 1:56 - loss: 113.152 - ETA: 1:55 - loss: 113.083 - ETA: 1:54 - loss: 112.996 - ETA: 1:52 - loss: 112.885 - ETA: 1:51 - loss: 112.726 - ETA: 1:50 - loss: 112.542 - ETA: 1:49 - loss: 112.320 - ETA: 1:47 - loss: 112.091 - ETA: 1:46 - loss: 111.859 - ETA: 1:45 - loss: 111.649 - ETA: 1:44 - loss: 111.445 - ETA: 1:42 - loss: 111.235 - ETA: 1:41 - loss: 111.039 - ETA: 1:39 - loss: 110.814 - ETA: 1:38 - loss: 110.591 - ETA: 1:37 - loss: 110.368 - ETA: 1:35 - loss: 110.148 - ETA: 1:34 - loss: 109.925 - ETA: 1:33 - loss: 109.699 - ETA: 1:32 - loss: 109.468 - ETA: 1:31 - loss: 109.241 - ETA: 1:29 - loss: 109.013 - ETA: 1:28 - loss: 108.787 - ETA: 1:27 - loss: 108.563 - ETA: 1:26 - loss: 108.346 - ETA: 1:24 - loss: 108.131 - ETA: 1:23 - loss: 107.937 - ETA: 1:21 - loss: 107.750 - ETA: 1:20 - loss: 107.586 - ETA: 1:19 - loss: 107.425 - ETA: 1:18 - loss: 107.267 - ETA: 1:16 - loss: 107.109 - ETA: 1:15 - loss: 107.030 - ETA: 1:14 - loss: 106.961 - ETA: 1:12 - loss: 106.889 - ETA: 1:11 - loss: 106.814 - ETA: 1:10 - loss: 106.739 - ETA: 1:09 - loss: 106.671 - ETA: 1:07 - loss: 106.801 - ETA: 1:06 - loss: 106.948 - ETA: 1:05 - loss: 107.082 - ETA: 1:03 - loss: 107.218 - ETA: 1:02 - loss: 107.340 - ETA: 1:01 - loss: 107.449 - ETA: 59s - loss: 107.550 - ETA: 58s - loss: 107.64 - ETA: 57s - loss: 107.72 - ETA: 55s - loss: 107.81 - ETA: 54s - loss: 107.90 - ETA: 53s - loss: 107.99 - ETA: 51s - loss: 108.08 - ETA: 50s - loss: 108.15 - ETA: 49s - loss: 108.23 - ETA: 47s - loss: 108.31 - ETA: 46s - loss: 108.38 - ETA: 45s - loss: 108.45 - ETA: 43s - loss: 108.51 - ETA: 42s - loss: 108.57 - ETA: 41s - loss: 108.63 - ETA: 39s - loss: 108.72 - ETA: 38s - loss: 108.79 - ETA: 37s - loss: 108.87 - ETA: 35s - loss: 108.93 - ETA: 34s - loss: 109.00 - ETA: 33s - loss: 109.06 - ETA: 31s - loss: 109.12 - ETA: 30s - loss: 109.18 - ETA: 29s - loss: 109.51 - ETA: 27s - loss: 109.82 - ETA: 26s - loss: 110.13 - ETA: 25s - loss: 110.43 - ETA: 23s - loss: 110.73 - ETA: 22s - loss: 111.09 - ETA: 21s - loss: 111.43 - ETA: 19s - loss: 111.76 - ETA: 18s - loss: 112.08 - ETA: 17s - loss: 112.39 - ETA: 15s - loss: 112.69 - ETA: 14s - loss: 112.97 - ETA: 13s - loss: 113.24 - ETA: 11s - loss: 113.50 - ETA: 10s - loss: 113.76 - ETA: 9s - loss: 114.0059 - ETA: 7s - loss: 114.241 - ETA: 6s - loss: 114.469 - ETA: 5s - loss: 114.689 - ETA: 3s - loss: 114.903 - ETA: 2s - loss: 115.107 - ETA: 1s - loss: 115.303 - ETA: 0s - loss: 115.494 - 150s 1s/step - loss: 115.6822 - val_loss: 144.8147\n",
+ "\n",
+ "Epoch 00023: val_loss did not improve from 92.39297\n",
+ "Epoch 24/30\n",
+ "111/111 [==============================] - ETA: 2:24 - loss: 95.53 - ETA: 2:23 - loss: 94.54 - ETA: 2:24 - loss: 114.501 - ETA: 2:22 - loss: 121.552 - ETA: 2:20 - loss: 123.445 - ETA: 2:19 - loss: 124.240 - ETA: 2:18 - loss: 124.164 - ETA: 2:17 - loss: 123.527 - ETA: 2:16 - loss: 122.644 - ETA: 2:14 - loss: 121.709 - ETA: 2:13 - loss: 121.969 - ETA: 2:11 - loss: 122.006 - ETA: 2:10 - loss: 121.836 - ETA: 2:09 - loss: 121.451 - ETA: 2:07 - loss: 121.021 - ETA: 2:06 - loss: 120.531 - ETA: 2:05 - loss: 120.079 - ETA: 2:04 - loss: 119.600 - ETA: 2:02 - loss: 119.196 - ETA: 2:01 - loss: 118.767 - ETA: 2:00 - loss: 118.327 - ETA: 1:58 - loss: 117.914 - ETA: 1:57 - loss: 117.517 - ETA: 1:56 - loss: 117.215 - ETA: 1:54 - loss: 116.900 - ETA: 1:52 - loss: 116.685 - ETA: 1:51 - loss: 117.812 - ETA: 1:50 - loss: 118.806 - ETA: 1:48 - loss: 119.686 - ETA: 1:47 - loss: 120.497 - ETA: 1:46 - loss: 121.216 - ETA: 1:45 - loss: 121.851 - ETA: 1:44 - loss: 122.400 - ETA: 1:42 - loss: 122.868 - ETA: 1:41 - loss: 123.305 - ETA: 1:40 - loss: 123.692 - ETA: 1:38 - loss: 124.026 - ETA: 1:37 - loss: 124.302 - ETA: 1:36 - loss: 124.530 - ETA: 1:34 - loss: 124.735 - ETA: 1:33 - loss: 124.902 - ETA: 1:32 - loss: 125.086 - ETA: 1:30 - loss: 125.226 - ETA: 1:29 - loss: 125.341 - ETA: 1:28 - loss: 125.427 - ETA: 1:26 - loss: 125.574 - ETA: 1:25 - loss: 125.695 - ETA: 1:24 - loss: 125.792 - ETA: 1:22 - loss: 125.876 - ETA: 1:21 - loss: 125.937 - ETA: 1:20 - loss: 126.003 - ETA: 1:18 - loss: 126.046 - ETA: 1:17 - loss: 126.075 - ETA: 1:16 - loss: 126.089 - ETA: 1:14 - loss: 126.095 - ETA: 1:13 - loss: 126.094 - ETA: 1:12 - loss: 126.078 - ETA: 1:10 - loss: 126.050 - ETA: 1:09 - loss: 126.014 - ETA: 1:08 - loss: 125.972 - ETA: 1:06 - loss: 125.923 - ETA: 1:05 - loss: 125.865 - ETA: 1:04 - loss: 125.814 - ETA: 1:02 - loss: 125.754 - ETA: 1:01 - loss: 125.688 - ETA: 1:00 - loss: 125.622 - ETA: 58s - loss: 125.551 - ETA: 57s - loss: 125.47 - ETA: 56s - loss: 125.40 - ETA: 54s - loss: 125.33 - ETA: 53s - loss: 125.25 - ETA: 52s - loss: 125.18 - ETA: 50s - loss: 125.11 - ETA: 49s - loss: 125.03 - ETA: 48s - loss: 124.98 - ETA: 46s - loss: 124.92 - ETA: 45s - loss: 124.86 - ETA: 44s - loss: 124.80 - ETA: 42s - loss: 124.73 - ETA: 41s - loss: 124.67 - ETA: 40s - loss: 124.61 - ETA: 38s - loss: 124.54 - ETA: 37s - loss: 124.48 - ETA: 36s - loss: 124.48 - ETA: 34s - loss: 124.48 - ETA: 33s - loss: 124.48 - ETA: 32s - loss: 124.47 - ETA: 30s - loss: 124.46 - ETA: 29s - loss: 124.44 - ETA: 28s - loss: 124.44 - ETA: 26s - loss: 124.43 - ETA: 25s - loss: 124.41 - ETA: 24s - loss: 124.40 - ETA: 22s - loss: 124.38 - ETA: 21s - loss: 124.36 - ETA: 20s - loss: 124.34 - ETA: 18s - loss: 124.32 - ETA: 17s - loss: 124.29 - ETA: 16s - loss: 124.26 - ETA: 14s - loss: 124.23 - ETA: 13s - loss: 124.19 - ETA: 12s - loss: 124.15 - ETA: 10s - loss: 124.11 - ETA: 9s - loss: 124.0788 - ETA: 8s - loss: 124.036 - ETA: 6s - loss: 123.994 - ETA: 5s - loss: 123.968 - ETA: 4s - loss: 123.939 - ETA: 2s - loss: 123.909 - ETA: 1s - loss: 123.876 - ETA: 0s - loss: 123.842 - 151s 1s/step - loss: 123.8097 - val_loss: 103.3583\n",
+ "\n",
+ "Epoch 00024: val_loss did not improve from 92.39297\n",
+ "Epoch 00024: early stopping\n",
+ "4/4 [==============================] - ETA: 2s - loss: 88.43 - ETA: 1s - loss: 83.75 - ETA: 0s - loss: 88.43 - ETA: 0s - loss: 88.85 - 3s 643ms/step - loss: 88.8559\n",
+ "Val Score: 88.85594940185547\n",
+ "====================================================================================\n",
+ "\n",
+ "\n",
+ "Training on Fold: 2\n",
+ "Epoch 1/30\n",
+ "111/111 [==============================] - ETA: 10:49 - loss: 416.92 - ETA: 2:53 - loss: 348.9178 - ETA: 2:37 - loss: 309.969 - ETA: 2:30 - loss: 283.752 - ETA: 2:26 - loss: 266.171 - ETA: 2:24 - loss: 300.569 - ETA: 2:21 - loss: 323.628 - ETA: 2:19 - loss: 336.607 - ETA: 2:18 - loss: 343.229 - ETA: 2:17 - loss: 345.911 - ETA: 2:15 - loss: 346.360 - ETA: 2:13 - loss: 345.609 - ETA: 2:11 - loss: 347.210 - ETA: 2:10 - loss: 347.741 - ETA: 2:08 - loss: 346.972 - ETA: 2:06 - loss: 345.369 - ETA: 2:05 - loss: 343.286 - ETA: 2:03 - loss: 340.902 - ETA: 2:02 - loss: 338.298 - ETA: 2:01 - loss: 336.064 - ETA: 1:59 - loss: 334.981 - ETA: 1:58 - loss: 333.832 - ETA: 1:57 - loss: 332.399 - ETA: 1:56 - loss: 330.769 - ETA: 1:54 - loss: 328.998 - ETA: 1:53 - loss: 327.809 - ETA: 1:52 - loss: 326.800 - ETA: 1:50 - loss: 326.461 - ETA: 1:49 - loss: 325.991 - ETA: 1:48 - loss: 325.515 - ETA: 1:46 - loss: 324.871 - ETA: 1:45 - loss: 324.687 - ETA: 1:44 - loss: 324.343 - ETA: 1:42 - loss: 323.885 - ETA: 1:41 - loss: 323.295 - ETA: 1:40 - loss: 322.560 - ETA: 1:38 - loss: 321.766 - ETA: 1:37 - loss: 320.868 - ETA: 1:36 - loss: 320.039 - ETA: 1:34 - loss: 319.639 - ETA: 1:33 - loss: 319.132 - ETA: 1:31 - loss: 318.687 - ETA: 1:30 - loss: 318.181 - ETA: 1:29 - loss: 317.618 - ETA: 1:27 - loss: 317.128 - ETA: 1:26 - loss: 317.319 - ETA: 1:25 - loss: 317.412 - ETA: 1:23 - loss: 317.415 - ETA: 1:22 - loss: 317.408 - ETA: 1:21 - loss: 317.306 - ETA: 1:20 - loss: 317.126 - ETA: 1:19 - loss: 316.946 - ETA: 1:17 - loss: 316.722 - ETA: 1:16 - loss: 316.434 - ETA: 1:15 - loss: 316.088 - ETA: 1:13 - loss: 315.699 - ETA: 1:12 - loss: 315.270 - ETA: 1:10 - loss: 314.823 - ETA: 1:09 - loss: 314.374 - ETA: 1:08 - loss: 313.922 - ETA: 1:06 - loss: 313.434 - ETA: 1:05 - loss: 312.944 - ETA: 1:04 - loss: 312.467 - ETA: 1:02 - loss: 311.977 - ETA: 1:01 - loss: 311.468 - ETA: 59s - loss: 310.964 - ETA: 58s - loss: 310.44 - ETA: 57s - loss: 309.89 - ETA: 55s - loss: 309.34 - ETA: 54s - loss: 308.76 - ETA: 53s - loss: 308.29 - ETA: 51s - loss: 307.81 - ETA: 50s - loss: 307.32 - ETA: 49s - loss: 306.81 - ETA: 47s - loss: 306.29 - ETA: 46s - loss: 305.75 - ETA: 45s - loss: 305.20 - ETA: 43s - loss: 304.64 - ETA: 42s - loss: 304.06 - ETA: 41s - loss: 303.49 - ETA: 39s - loss: 302.91 - ETA: 38s - loss: 302.34 - ETA: 37s - loss: 301.75 - ETA: 35s - loss: 301.16 - ETA: 34s - loss: 300.57 - ETA: 33s - loss: 299.97 - ETA: 31s - loss: 299.39 - ETA: 30s - loss: 298.81 - ETA: 29s - loss: 298.24 - ETA: 27s - loss: 298.05 - ETA: 26s - loss: 297.83 - ETA: 25s - loss: 297.60 - ETA: 23s - loss: 297.35 - ETA: 22s - loss: 297.12 - ETA: 21s - loss: 296.88 - ETA: 19s - loss: 296.65 - ETA: 18s - loss: 296.43 - ETA: 17s - loss: 296.19 - ETA: 15s - loss: 295.97 - ETA: 14s - loss: 295.74 - ETA: 13s - loss: 295.51 - ETA: 11s - loss: 295.28 - ETA: 10s - loss: 295.07 - ETA: 9s - loss: 294.8431 - ETA: 7s - loss: 294.604 - ETA: 6s - loss: 294.354 - ETA: 5s - loss: 294.098 - ETA: 3s - loss: 293.840 - ETA: 2s - loss: 293.591 - ETA: 1s - loss: 293.332 - ETA: 0s - loss: 293.074 - 155s 1s/step - loss: 292.8209 - val_loss: 119.3787\n",
+ "\n",
+ "Epoch 00001: val_loss did not improve from 92.39297\n",
+ "Epoch 2/30\n",
+ "111/111 [==============================] - ETA: 2:28 - loss: 124.579 - ETA: 2:28 - loss: 137.837 - ETA: 2:19 - loss: 139.256 - ETA: 2:18 - loss: 137.497 - ETA: 2:18 - loss: 134.489 - ETA: 2:15 - loss: 130.886 - ETA: 2:14 - loss: 130.037 - ETA: 2:12 - loss: 129.469 - ETA: 2:11 - loss: 129.169 - ETA: 2:10 - loss: 129.065 - ETA: 2:09 - loss: 128.650 - ETA: 2:08 - loss: 128.342 - ETA: 2:06 - loss: 128.262 - ETA: 2:05 - loss: 128.132 - ETA: 2:03 - loss: 127.751 - ETA: 2:02 - loss: 127.377 - ETA: 2:01 - loss: 127.203 - ETA: 2:00 - loss: 128.011 - ETA: 1:59 - loss: 128.904 - ETA: 1:57 - loss: 129.771 - ETA: 1:56 - loss: 130.409 - ETA: 1:54 - loss: 130.934 - ETA: 1:53 - loss: 131.519 - ETA: 1:51 - loss: 132.050 - ETA: 1:50 - loss: 132.495 - ETA: 1:49 - loss: 132.842 - ETA: 1:48 - loss: 133.175 - ETA: 1:46 - loss: 133.998 - ETA: 1:45 - loss: 134.659 - ETA: 1:44 - loss: 135.185 - ETA: 1:42 - loss: 135.772 - ETA: 1:41 - loss: 136.269 - ETA: 1:40 - loss: 136.773 - ETA: 1:38 - loss: 137.194 - ETA: 1:37 - loss: 137.549 - ETA: 1:36 - loss: 137.967 - ETA: 1:35 - loss: 138.352 - ETA: 1:33 - loss: 138.708 - ETA: 1:32 - loss: 139.013 - ETA: 1:31 - loss: 139.281 - ETA: 1:30 - loss: 139.497 - ETA: 1:28 - loss: 139.656 - ETA: 1:27 - loss: 139.770 - ETA: 1:26 - loss: 139.879 - ETA: 1:24 - loss: 139.985 - ETA: 1:23 - loss: 140.047 - ETA: 1:22 - loss: 140.109 - ETA: 1:20 - loss: 140.162 - ETA: 1:19 - loss: 140.208 - ETA: 1:18 - loss: 140.228 - ETA: 1:17 - loss: 140.661 - ETA: 1:15 - loss: 141.060 - ETA: 1:14 - loss: 141.896 - ETA: 1:13 - loss: 142.667 - ETA: 1:11 - loss: 143.376 - ETA: 1:10 - loss: 144.031 - ETA: 1:09 - loss: 144.629 - ETA: 1:08 - loss: 145.190 - ETA: 1:06 - loss: 145.745 - ETA: 1:05 - loss: 146.250 - ETA: 1:04 - loss: 146.712 - ETA: 1:03 - loss: 147.144 - ETA: 1:01 - loss: 147.579 - ETA: 1:00 - loss: 147.979 - ETA: 59s - loss: 148.352 - ETA: 57s - loss: 148.70 - ETA: 56s - loss: 149.01 - ETA: 55s - loss: 149.30 - ETA: 54s - loss: 149.56 - ETA: 52s - loss: 149.82 - ETA: 51s - loss: 150.06 - ETA: 50s - loss: 150.28 - ETA: 48s - loss: 150.48 - ETA: 47s - loss: 150.68 - ETA: 46s - loss: 150.86 - ETA: 45s - loss: 151.09 - ETA: 43s - loss: 151.33 - ETA: 42s - loss: 151.57 - ETA: 41s - loss: 151.78 - ETA: 39s - loss: 151.99 - ETA: 38s - loss: 152.54 - ETA: 37s - loss: 153.07 - ETA: 36s - loss: 153.62 - ETA: 34s - loss: 154.37 - ETA: 33s - loss: 155.11 - ETA: 32s - loss: 155.80 - ETA: 30s - loss: 156.50 - ETA: 29s - loss: 157.16 - ETA: 28s - loss: 157.79 - ETA: 27s - loss: 158.40 - ETA: 25s - loss: 159.01 - ETA: 24s - loss: 159.58 - ETA: 23s - loss: 160.14 - ETA: 21s - loss: 160.67 - ETA: 20s - loss: 161.19 - ETA: 19s - loss: 161.69 - ETA: 18s - loss: 162.18 - ETA: 16s - loss: 162.64 - ETA: 15s - loss: 163.08 - ETA: 14s - loss: 163.52 - ETA: 12s - loss: 163.94 - ETA: 11s - loss: 164.35 - ETA: 10s - loss: 164.75 - ETA: 9s - loss: 165.3624 - ETA: 7s - loss: 165.946 - ETA: 6s - loss: 166.514 - ETA: 5s - loss: 167.059 - ETA: 3s - loss: 167.584 - ETA: 2s - loss: 168.090 - ETA: 1s - loss: 168.578 - ETA: 0s - loss: 169.071 - 145s 1s/step - loss: 169.5567 - val_loss: 163.7922\n",
+ "\n",
+ "Epoch 00002: val_loss did not improve from 92.39297\n",
+ "Epoch 3/30\n",
+ "111/111 [==============================] - ETA: 2:13 - loss: 89.14 - ETA: 2:21 - loss: 79.56 - ETA: 2:20 - loss: 78.91 - ETA: 2:18 - loss: 79.80 - ETA: 2:18 - loss: 82.82 - ETA: 2:17 - loss: 87.22 - ETA: 2:14 - loss: 91.40 - ETA: 2:13 - loss: 94.64 - ETA: 2:12 - loss: 96.86 - ETA: 2:10 - loss: 98.41 - ETA: 2:08 - loss: 100.615 - ETA: 2:08 - loss: 102.560 - ETA: 2:06 - loss: 112.159 - ETA: 2:05 - loss: 119.870 - ETA: 2:04 - loss: 126.337 - ETA: 2:02 - loss: 132.045 - ETA: 2:01 - loss: 136.794 - ETA: 1:59 - loss: 140.754 - ETA: 1:58 - loss: 144.024 - ETA: 1:57 - loss: 147.001 - ETA: 1:56 - loss: 149.558 - ETA: 1:54 - loss: 151.703 - ETA: 1:53 - loss: 153.687 - ETA: 1:52 - loss: 155.289 - ETA: 1:50 - loss: 156.622 - ETA: 1:49 - loss: 157.683 - ETA: 1:48 - loss: 158.536 - ETA: 1:47 - loss: 159.199 - ETA: 1:46 - loss: 159.729 - ETA: 1:45 - loss: 160.155 - ETA: 1:43 - loss: 163.391 - ETA: 1:42 - loss: 166.258 - ETA: 1:41 - loss: 168.786 - ETA: 1:39 - loss: 171.064 - ETA: 1:38 - loss: 173.144 - ETA: 1:37 - loss: 174.991 - ETA: 1:35 - loss: 176.614 - ETA: 1:34 - loss: 179.145 - ETA: 1:33 - loss: 181.524 - ETA: 1:32 - loss: 183.653 - ETA: 1:30 - loss: 185.684 - ETA: 1:29 - loss: 187.528 - ETA: 1:28 - loss: 189.195 - ETA: 1:26 - loss: 190.771 - ETA: 1:25 - loss: 192.220 - ETA: 1:24 - loss: 193.553 - ETA: 1:23 - loss: 194.755 - ETA: 1:21 - loss: 195.852 - ETA: 1:20 - loss: 196.842 - ETA: 1:19 - loss: 197.745 - ETA: 1:17 - loss: 198.558 - ETA: 1:16 - loss: 199.297 - ETA: 1:15 - loss: 199.982 - ETA: 1:13 - loss: 200.589 - ETA: 1:12 - loss: 201.122 - ETA: 1:11 - loss: 201.588 - ETA: 1:09 - loss: 201.994 - ETA: 1:08 - loss: 202.349 - ETA: 1:07 - loss: 202.652 - ETA: 1:06 - loss: 202.947 - ETA: 1:04 - loss: 203.186 - ETA: 1:03 - loss: 203.380 - ETA: 1:02 - loss: 203.538 - ETA: 1:00 - loss: 203.660 - ETA: 59s - loss: 203.795 - ETA: 58s - loss: 203.91 - ETA: 57s - loss: 204.01 - ETA: 55s - loss: 204.13 - ETA: 54s - loss: 204.25 - ETA: 53s - loss: 204.38 - ETA: 51s - loss: 204.50 - ETA: 50s - loss: 204.60 - ETA: 49s - loss: 204.67 - ETA: 47s - loss: 204.72 - ETA: 46s - loss: 204.77 - ETA: 45s - loss: 204.80 - ETA: 44s - loss: 204.81 - ETA: 42s - loss: 204.80 - ETA: 41s - loss: 204.77 - ETA: 40s - loss: 204.72 - ETA: 38s - loss: 204.67 - ETA: 37s - loss: 204.61 - ETA: 36s - loss: 204.55 - ETA: 34s - loss: 204.47 - ETA: 33s - loss: 204.37 - ETA: 32s - loss: 204.27 - ETA: 31s - loss: 204.16 - ETA: 29s - loss: 204.04 - ETA: 28s - loss: 203.94 - ETA: 27s - loss: 203.83 - ETA: 25s - loss: 203.92 - ETA: 24s - loss: 203.99 - ETA: 23s - loss: 204.05 - ETA: 21s - loss: 204.10 - ETA: 20s - loss: 204.12 - ETA: 19s - loss: 204.14 - ETA: 18s - loss: 204.14 - ETA: 16s - loss: 204.14 - ETA: 15s - loss: 204.14 - ETA: 14s - loss: 204.13 - ETA: 12s - loss: 204.13 - ETA: 11s - loss: 204.13 - ETA: 10s - loss: 204.11 - ETA: 9s - loss: 204.0946 - ETA: 7s - loss: 204.067 - ETA: 6s - loss: 204.031 - ETA: 5s - loss: 203.984 - ETA: 3s - loss: 203.929 - ETA: 2s - loss: 203.866 - ETA: 1s - loss: 203.796 - ETA: 0s - loss: 203.723 - 146s 1s/step - loss: 203.6515 - val_loss: 121.8628\n",
+ "\n",
+ "Epoch 00003: val_loss did not improve from 92.39297\n",
+ "Epoch 4/30\n",
+ "111/111 [==============================] - ETA: 2:18 - loss: 104.028 - ETA: 2:19 - loss: 96.812 - ETA: 2:19 - loss: 101.958 - ETA: 2:17 - loss: 118.246 - ETA: 2:15 - loss: 125.919 - ETA: 2:15 - loss: 128.942 - ETA: 2:14 - loss: 129.737 - ETA: 2:13 - loss: 129.735 - ETA: 2:12 - loss: 129.444 - ETA: 2:11 - loss: 129.738 - ETA: 2:09 - loss: 129.480 - ETA: 2:07 - loss: 129.084 - ETA: 2:05 - loss: 128.862 - ETA: 2:04 - loss: 128.488 - ETA: 2:03 - loss: 127.975 - ETA: 2:01 - loss: 127.515 - ETA: 1:59 - loss: 127.151 - ETA: 1:58 - loss: 127.601 - ETA: 1:57 - loss: 128.104 - ETA: 1:56 - loss: 128.629 - ETA: 1:55 - loss: 128.992 - ETA: 1:54 - loss: 129.299 - ETA: 1:53 - loss: 129.535 - ETA: 1:51 - loss: 130.004 - ETA: 1:50 - loss: 130.337 - ETA: 1:49 - loss: 130.596 - ETA: 1:47 - loss: 130.814 - ETA: 1:46 - loss: 131.236 - ETA: 1:45 - loss: 131.576 - ETA: 1:43 - loss: 131.830 - ETA: 1:42 - loss: 132.090 - ETA: 1:41 - loss: 133.135 - ETA: 1:39 - loss: 134.057 - ETA: 1:38 - loss: 134.867 - ETA: 1:37 - loss: 135.563 - ETA: 1:36 - loss: 136.198 - ETA: 1:34 - loss: 136.761 - ETA: 1:33 - loss: 137.295 - ETA: 1:32 - loss: 137.742 - ETA: 1:31 - loss: 138.133 - ETA: 1:29 - loss: 138.510 - ETA: 1:28 - loss: 138.837 - ETA: 1:27 - loss: 139.113 - ETA: 1:26 - loss: 139.352 - ETA: 1:24 - loss: 140.880 - ETA: 1:23 - loss: 142.280 - ETA: 1:22 - loss: 143.566 - ETA: 1:21 - loss: 144.780 - ETA: 1:19 - loss: 145.891 - ETA: 1:18 - loss: 146.919 - ETA: 1:17 - loss: 147.943 - ETA: 1:15 - loss: 148.910 - ETA: 1:14 - loss: 149.799 - ETA: 1:13 - loss: 150.624 - ETA: 1:12 - loss: 151.378 - ETA: 1:10 - loss: 152.077 - ETA: 1:09 - loss: 152.720 - ETA: 1:08 - loss: 153.305 - ETA: 1:06 - loss: 153.846 - ETA: 1:05 - loss: 154.343 - ETA: 1:04 - loss: 154.806 - ETA: 1:03 - loss: 155.266 - ETA: 1:01 - loss: 155.683 - ETA: 1:00 - loss: 156.066 - ETA: 59s - loss: 156.436 - ETA: 57s - loss: 156.77 - ETA: 56s - loss: 157.08 - ETA: 55s - loss: 157.35 - ETA: 54s - loss: 157.62 - ETA: 52s - loss: 157.86 - ETA: 51s - loss: 158.13 - ETA: 50s - loss: 158.39 - ETA: 48s - loss: 158.62 - ETA: 47s - loss: 158.83 - ETA: 46s - loss: 159.04 - ETA: 45s - loss: 159.22 - ETA: 43s - loss: 159.39 - ETA: 42s - loss: 159.54 - ETA: 41s - loss: 159.68 - ETA: 39s - loss: 159.81 - ETA: 38s - loss: 159.94 - ETA: 37s - loss: 160.06 - ETA: 36s - loss: 160.16 - ETA: 34s - loss: 160.25 - ETA: 33s - loss: 160.33 - ETA: 32s - loss: 160.40 - ETA: 30s - loss: 160.47 - ETA: 29s - loss: 160.53 - ETA: 28s - loss: 160.59 - ETA: 27s - loss: 160.64 - ETA: 25s - loss: 160.68 - ETA: 24s - loss: 160.71 - ETA: 23s - loss: 160.73 - ETA: 21s - loss: 160.75 - ETA: 20s - loss: 160.76 - ETA: 19s - loss: 160.77 - ETA: 18s - loss: 160.76 - ETA: 16s - loss: 160.75 - ETA: 15s - loss: 160.76 - ETA: 14s - loss: 160.77 - ETA: 12s - loss: 160.76 - ETA: 11s - loss: 160.78 - ETA: 10s - loss: 160.79 - ETA: 9s - loss: 160.8075 - ETA: 7s - loss: 160.812 - ETA: 6s - loss: 160.815 - ETA: 5s - loss: 160.813 - ETA: 3s - loss: 160.805 - ETA: 2s - loss: 160.792 - ETA: 1s - loss: 160.783 - ETA: 0s - loss: 160.770 - 146s 1s/step - loss: 160.7577 - val_loss: 122.0247\n",
+ "\n",
+ "Epoch 00004: val_loss did not improve from 92.39297\n",
+ "Epoch 5/30\n",
+ "111/111 [==============================] - ETA: 2:19 - loss: 112.082 - ETA: 2:25 - loss: 135.536 - ETA: 2:25 - loss: 144.390 - ETA: 2:24 - loss: 144.020 - ETA: 2:22 - loss: 143.410 - ETA: 2:19 - loss: 141.793 - ETA: 2:17 - loss: 139.802 - ETA: 2:16 - loss: 137.986 - ETA: 2:14 - loss: 151.207 - ETA: 2:13 - loss: 160.008 - ETA: 2:11 - loss: 166.054 - ETA: 2:10 - loss: 170.303 - ETA: 2:08 - loss: 173.039 - ETA: 2:07 - loss: 174.790 - ETA: 2:05 - loss: 175.919 - ETA: 2:03 - loss: 176.504 - ETA: 2:02 - loss: 176.662 - ETA: 2:00 - loss: 176.502 - ETA: 1:59 - loss: 176.106 - ETA: 1:57 - loss: 175.777 - ETA: 1:56 - loss: 175.220 - ETA: 1:55 - loss: 174.585 - ETA: 1:53 - loss: 174.093 - ETA: 1:52 - loss: 173.529 - ETA: 1:51 - loss: 172.877 - ETA: 1:49 - loss: 172.164 - ETA: 1:48 - loss: 171.424 - ETA: 1:46 - loss: 170.654 - ETA: 1:45 - loss: 169.867 - ETA: 1:44 - loss: 169.105 - ETA: 1:42 - loss: 169.016 - ETA: 1:41 - loss: 168.850 - ETA: 1:40 - loss: 168.616 - ETA: 1:39 - loss: 168.354 - ETA: 1:38 - loss: 168.121 - ETA: 1:36 - loss: 167.862 - ETA: 1:35 - loss: 167.541 - ETA: 1:34 - loss: 167.204 - ETA: 1:32 - loss: 166.837 - ETA: 1:31 - loss: 166.461 - ETA: 1:30 - loss: 166.135 - ETA: 1:28 - loss: 165.795 - ETA: 1:27 - loss: 165.434 - ETA: 1:26 - loss: 165.062 - ETA: 1:25 - loss: 164.674 - ETA: 1:23 - loss: 164.287 - ETA: 1:22 - loss: 163.915 - ETA: 1:21 - loss: 163.551 - ETA: 1:20 - loss: 163.181 - ETA: 1:18 - loss: 162.821 - ETA: 1:17 - loss: 162.455 - ETA: 1:16 - loss: 162.084 - ETA: 1:14 - loss: 161.733 - ETA: 1:13 - loss: 161.365 - ETA: 1:12 - loss: 161.106 - ETA: 1:10 - loss: 160.841 - ETA: 1:09 - loss: 160.566 - ETA: 1:08 - loss: 160.285 - ETA: 1:06 - loss: 159.998 - ETA: 1:05 - loss: 159.700 - ETA: 1:04 - loss: 160.089 - ETA: 1:03 - loss: 160.450 - ETA: 1:01 - loss: 160.825 - ETA: 1:00 - loss: 161.177 - ETA: 59s - loss: 161.492 - ETA: 57s - loss: 161.77 - ETA: 56s - loss: 162.02 - ETA: 55s - loss: 162.24 - ETA: 54s - loss: 162.44 - ETA: 52s - loss: 162.69 - ETA: 51s - loss: 162.95 - ETA: 50s - loss: 163.18 - ETA: 48s - loss: 163.40 - ETA: 47s - loss: 163.58 - ETA: 46s - loss: 163.76 - ETA: 45s - loss: 163.91 - ETA: 43s - loss: 164.04 - ETA: 42s - loss: 164.15 - ETA: 41s - loss: 164.25 - ETA: 39s - loss: 164.35 - ETA: 38s - loss: 164.43 - ETA: 37s - loss: 164.49 - ETA: 36s - loss: 164.54 - ETA: 34s - loss: 164.58 - ETA: 33s - loss: 164.61 - ETA: 32s - loss: 164.64 - ETA: 30s - loss: 164.65 - ETA: 29s - loss: 164.66 - ETA: 28s - loss: 164.67 - ETA: 27s - loss: 164.66 - ETA: 25s - loss: 164.65 - ETA: 24s - loss: 164.62 - ETA: 23s - loss: 164.60 - ETA: 21s - loss: 164.56 - ETA: 20s - loss: 164.52 - ETA: 19s - loss: 164.46 - ETA: 18s - loss: 164.40 - ETA: 16s - loss: 164.34 - ETA: 15s - loss: 164.27 - ETA: 14s - loss: 164.19 - ETA: 12s - loss: 164.11 - ETA: 11s - loss: 164.02 - ETA: 10s - loss: 163.93 - ETA: 9s - loss: 163.8482 - ETA: 7s - loss: 163.753 - ETA: 6s - loss: 163.655 - ETA: 5s - loss: 163.553 - ETA: 3s - loss: 163.446 - ETA: 2s - loss: 163.352 - ETA: 1s - loss: 163.259 - ETA: 0s - loss: 163.168 - 145s 1s/step - loss: 163.0780 - val_loss: 133.1988\n",
+ "\n",
+ "Epoch 00005: val_loss did not improve from 92.39297\n",
+ "Epoch 6/30\n",
+ "111/111 [==============================] - ETA: 2:21 - loss: 81.16 - ETA: 2:27 - loss: 96.56 - ETA: 2:21 - loss: 101.400 - ETA: 2:19 - loss: 105.108 - ETA: 2:17 - loss: 107.264 - ETA: 2:15 - loss: 111.120 - ETA: 2:15 - loss: 114.102 - ETA: 2:13 - loss: 116.077 - ETA: 2:11 - loss: 116.874 - ETA: 2:09 - loss: 117.988 - ETA: 2:08 - loss: 118.516 - ETA: 2:07 - loss: 124.904 - ETA: 2:05 - loss: 131.394 - ETA: 2:05 - loss: 136.276 - ETA: 2:03 - loss: 140.185 - ETA: 2:02 - loss: 143.595 - ETA: 2:01 - loss: 146.308 - ETA: 2:00 - loss: 148.494 - ETA: 1:58 - loss: 150.402 - ETA: 1:57 - loss: 151.830 - ETA: 1:56 - loss: 152.957 - ETA: 1:54 - loss: 154.967 - ETA: 1:53 - loss: 156.621 - ETA: 1:51 - loss: 158.018 - ETA: 1:50 - loss: 159.124 - ETA: 1:49 - loss: 160.016 - ETA: 1:47 - loss: 160.720 - ETA: 1:46 - loss: 161.269 - ETA: 1:45 - loss: 163.167 - ETA: 1:44 - loss: 164.799 - ETA: 1:43 - loss: 166.225 - ETA: 1:41 - loss: 167.427 - ETA: 1:40 - loss: 168.431 - ETA: 1:39 - loss: 169.458 - ETA: 1:38 - loss: 170.324 - ETA: 1:36 - loss: 171.077 - ETA: 1:35 - loss: 171.700 - ETA: 1:33 - loss: 172.260 - ETA: 1:32 - loss: 172.706 - ETA: 1:31 - loss: 173.059 - ETA: 1:30 - loss: 173.343 - ETA: 1:28 - loss: 173.922 - ETA: 1:27 - loss: 174.470 - ETA: 1:26 - loss: 174.924 - ETA: 1:25 - loss: 175.327 - ETA: 1:23 - loss: 175.664 - ETA: 1:22 - loss: 175.936 - ETA: 1:21 - loss: 176.148 - ETA: 1:19 - loss: 176.316 - ETA: 1:18 - loss: 176.469 - ETA: 1:17 - loss: 176.586 - ETA: 1:15 - loss: 176.692 - ETA: 1:14 - loss: 176.786 - ETA: 1:13 - loss: 176.857 - ETA: 1:12 - loss: 176.886 - ETA: 1:10 - loss: 176.883 - ETA: 1:09 - loss: 176.864 - ETA: 1:08 - loss: 176.812 - ETA: 1:06 - loss: 176.741 - ETA: 1:05 - loss: 176.649 - ETA: 1:04 - loss: 176.540 - ETA: 1:03 - loss: 176.422 - ETA: 1:01 - loss: 176.286 - ETA: 1:00 - loss: 176.139 - ETA: 59s - loss: 175.991 - ETA: 57s - loss: 175.83 - ETA: 56s - loss: 175.66 - ETA: 55s - loss: 175.48 - ETA: 54s - loss: 175.29 - ETA: 52s - loss: 175.10 - ETA: 51s - loss: 174.90 - ETA: 50s - loss: 174.70 - ETA: 48s - loss: 174.49 - ETA: 47s - loss: 174.28 - ETA: 46s - loss: 174.06 - ETA: 45s - loss: 173.83 - ETA: 43s - loss: 173.59 - ETA: 42s - loss: 173.34 - ETA: 41s - loss: 173.10 - ETA: 40s - loss: 172.85 - ETA: 38s - loss: 172.60 - ETA: 37s - loss: 172.35 - ETA: 36s - loss: 172.09 - ETA: 34s - loss: 171.85 - ETA: 33s - loss: 171.60 - ETA: 32s - loss: 171.37 - ETA: 30s - loss: 171.16 - ETA: 29s - loss: 170.96 - ETA: 28s - loss: 170.75 - ETA: 27s - loss: 170.58 - ETA: 25s - loss: 170.40 - ETA: 24s - loss: 170.23 - ETA: 23s - loss: 170.05 - ETA: 21s - loss: 169.88 - ETA: 20s - loss: 169.71 - ETA: 19s - loss: 169.56 - ETA: 18s - loss: 169.42 - ETA: 16s - loss: 169.26 - ETA: 15s - loss: 169.11 - ETA: 14s - loss: 168.97 - ETA: 12s - loss: 168.81 - ETA: 11s - loss: 168.66 - ETA: 10s - loss: 168.50 - ETA: 9s - loss: 168.3425 - ETA: 7s - loss: 168.177 - ETA: 6s - loss: 168.008 - ETA: 5s - loss: 167.838 - ETA: 3s - loss: 167.671 - ETA: 2s - loss: 167.503 - ETA: 1s - loss: 167.345 - ETA: 0s - loss: 167.186 - 145s 1s/step - loss: 167.0307 - val_loss: 98.0347\n",
+ "\n",
+ "Epoch 00006: val_loss did not improve from 92.39297\n",
+ "Epoch 7/30\n",
+ "111/111 [==============================] - ETA: 2:20 - loss: 71.83 - ETA: 2:21 - loss: 98.50 - ETA: 2:17 - loss: 103.093 - ETA: 2:16 - loss: 109.957 - ETA: 2:13 - loss: 122.467 - ETA: 2:13 - loss: 128.408 - ETA: 2:13 - loss: 133.029 - ETA: 2:11 - loss: 135.587 - ETA: 2:11 - loss: 137.157 - ETA: 2:09 - loss: 137.732 - ETA: 2:08 - loss: 137.688 - ETA: 2:06 - loss: 137.226 - ETA: 2:05 - loss: 136.596 - ETA: 2:04 - loss: 140.677 - ETA: 2:02 - loss: 143.765 - ETA: 2:01 - loss: 146.131 - ETA: 2:00 - loss: 147.815 - ETA: 1:58 - loss: 149.003 - ETA: 1:57 - loss: 149.825 - ETA: 1:56 - loss: 150.353 - ETA: 1:54 - loss: 150.896 - ETA: 1:53 - loss: 151.268 - ETA: 1:52 - loss: 151.422 - ETA: 1:51 - loss: 151.474 - ETA: 1:49 - loss: 151.437 - ETA: 1:48 - loss: 151.314 - ETA: 1:47 - loss: 151.145 - ETA: 1:45 - loss: 150.935 - ETA: 1:44 - loss: 150.843 - ETA: 1:43 - loss: 150.710 - ETA: 1:42 - loss: 150.576 - ETA: 1:41 - loss: 150.406 - ETA: 1:40 - loss: 150.241 - ETA: 1:39 - loss: 150.031 - ETA: 1:37 - loss: 149.784 - ETA: 1:36 - loss: 149.493 - ETA: 1:35 - loss: 149.188 - ETA: 1:33 - loss: 148.946 - ETA: 1:32 - loss: 148.680 - ETA: 1:31 - loss: 148.399 - ETA: 1:29 - loss: 148.097 - ETA: 1:28 - loss: 147.822 - ETA: 1:27 - loss: 147.543 - ETA: 1:26 - loss: 147.264 - ETA: 1:24 - loss: 147.001 - ETA: 1:23 - loss: 146.813 - ETA: 1:22 - loss: 146.608 - ETA: 1:20 - loss: 146.389 - ETA: 1:19 - loss: 146.171 - ETA: 1:18 - loss: 145.948 - ETA: 1:17 - loss: 145.721 - ETA: 1:15 - loss: 145.483 - ETA: 1:14 - loss: 145.230 - ETA: 1:13 - loss: 144.968 - ETA: 1:11 - loss: 144.705 - ETA: 1:10 - loss: 144.439 - ETA: 1:09 - loss: 144.168 - ETA: 1:08 - loss: 143.895 - ETA: 1:06 - loss: 143.621 - ETA: 1:05 - loss: 143.365 - ETA: 1:04 - loss: 143.151 - ETA: 1:02 - loss: 142.938 - ETA: 1:01 - loss: 142.758 - ETA: 1:00 - loss: 142.571 - ETA: 59s - loss: 142.378 - ETA: 57s - loss: 142.17 - ETA: 56s - loss: 141.98 - ETA: 55s - loss: 141.78 - ETA: 53s - loss: 141.59 - ETA: 52s - loss: 141.44 - ETA: 51s - loss: 141.28 - ETA: 50s - loss: 141.70 - ETA: 48s - loss: 142.10 - ETA: 47s - loss: 142.47 - ETA: 46s - loss: 142.82 - ETA: 44s - loss: 143.14 - ETA: 43s - loss: 143.49 - ETA: 42s - loss: 143.84 - ETA: 41s - loss: 144.17 - ETA: 39s - loss: 144.49 - ETA: 38s - loss: 144.80 - ETA: 37s - loss: 145.08 - ETA: 35s - loss: 145.35 - ETA: 34s - loss: 145.64 - ETA: 33s - loss: 145.91 - ETA: 32s - loss: 146.17 - ETA: 30s - loss: 146.41 - ETA: 29s - loss: 146.64 - ETA: 28s - loss: 146.85 - ETA: 26s - loss: 147.04 - ETA: 25s - loss: 147.28 - ETA: 24s - loss: 147.50 - ETA: 23s - loss: 147.70 - ETA: 21s - loss: 147.89 - ETA: 20s - loss: 148.07 - ETA: 19s - loss: 148.23 - ETA: 17s - loss: 148.39 - ETA: 16s - loss: 148.54 - ETA: 15s - loss: 148.69 - ETA: 14s - loss: 148.82 - ETA: 12s - loss: 148.95 - ETA: 11s - loss: 149.07 - ETA: 10s - loss: 149.19 - ETA: 9s - loss: 149.2955 - ETA: 7s - loss: 149.392 - ETA: 6s - loss: 149.481 - ETA: 5s - loss: 149.608 - ETA: 3s - loss: 149.726 - ETA: 2s - loss: 149.836 - ETA: 1s - loss: 149.942 - ETA: 0s - loss: 150.042 - 147s 1s/step - loss: 150.1401 - val_loss: 113.0906\n",
+ "\n",
+ "Epoch 00007: val_loss did not improve from 92.39297\n",
+ "Epoch 8/30\n",
+ "111/111 [==============================] - ETA: 2:19 - loss: 77.97 - ETA: 2:23 - loss: 144.575 - ETA: 2:27 - loss: 155.006 - ETA: 2:31 - loss: 156.541 - ETA: 2:34 - loss: 154.273 - ETA: 2:32 - loss: 150.721 - ETA: 2:29 - loss: 147.186 - ETA: 2:26 - loss: 145.579 - ETA: 2:22 - loss: 143.798 - ETA: 2:20 - loss: 143.491 - ETA: 2:18 - loss: 142.928 - ETA: 2:15 - loss: 142.038 - ETA: 2:12 - loss: 141.001 - ETA: 2:11 - loss: 141.040 - ETA: 2:08 - loss: 140.734 - ETA: 2:07 - loss: 140.367 - ETA: 2:05 - loss: 140.081 - ETA: 2:03 - loss: 139.700 - ETA: 2:02 - loss: 139.361 - ETA: 2:00 - loss: 139.050 - ETA: 1:59 - loss: 138.678 - ETA: 1:58 - loss: 138.268 - ETA: 1:56 - loss: 137.802 - ETA: 1:55 - loss: 137.299 - ETA: 1:53 - loss: 136.844 - ETA: 1:52 - loss: 136.367 - ETA: 1:50 - loss: 135.919 - ETA: 1:49 - loss: 135.674 - ETA: 1:47 - loss: 135.447 - ETA: 1:46 - loss: 135.189 - ETA: 1:45 - loss: 134.944 - ETA: 1:43 - loss: 134.668 - ETA: 1:42 - loss: 134.368 - ETA: 1:41 - loss: 134.074 - ETA: 1:39 - loss: 133.888 - ETA: 1:38 - loss: 133.747 - ETA: 1:37 - loss: 133.580 - ETA: 1:36 - loss: 133.417 - ETA: 1:34 - loss: 133.281 - ETA: 1:33 - loss: 133.170 - ETA: 1:32 - loss: 133.032 - ETA: 1:30 - loss: 132.902 - ETA: 1:29 - loss: 132.754 - ETA: 1:27 - loss: 132.591 - ETA: 1:26 - loss: 132.459 - ETA: 1:25 - loss: 132.308 - ETA: 1:23 - loss: 132.274 - ETA: 1:22 - loss: 132.224 - ETA: 1:21 - loss: 132.158 - ETA: 1:19 - loss: 132.077 - ETA: 1:18 - loss: 132.032 - ETA: 1:17 - loss: 131.982 - ETA: 1:15 - loss: 131.942 - ETA: 1:14 - loss: 131.899 - ETA: 1:13 - loss: 131.837 - ETA: 1:11 - loss: 131.774 - ETA: 1:10 - loss: 131.698 - ETA: 1:09 - loss: 131.620 - ETA: 1:07 - loss: 131.529 - ETA: 1:06 - loss: 131.435 - ETA: 1:05 - loss: 131.340 - ETA: 1:04 - loss: 131.237 - ETA: 1:02 - loss: 131.138 - ETA: 1:01 - loss: 132.019 - ETA: 1:00 - loss: 132.853 - ETA: 58s - loss: 133.637 - ETA: 57s - loss: 134.37 - ETA: 56s - loss: 135.07 - ETA: 55s - loss: 135.72 - ETA: 53s - loss: 136.35 - ETA: 52s - loss: 136.95 - ETA: 51s - loss: 137.52 - ETA: 50s - loss: 138.06 - ETA: 48s - loss: 138.57 - ETA: 47s - loss: 139.05 - ETA: 46s - loss: 139.50 - ETA: 45s - loss: 139.92 - ETA: 43s - loss: 140.32 - ETA: 42s - loss: 140.70 - ETA: 41s - loss: 141.06 - ETA: 39s - loss: 141.39 - ETA: 38s - loss: 141.71 - ETA: 37s - loss: 142.01 - ETA: 35s - loss: 142.29 - ETA: 34s - loss: 142.55 - ETA: 33s - loss: 142.80 - ETA: 32s - loss: 143.03 - ETA: 30s - loss: 143.25 - ETA: 29s - loss: 143.45 - ETA: 28s - loss: 143.64 - ETA: 26s - loss: 143.85 - ETA: 25s - loss: 144.05 - ETA: 24s - loss: 144.23 - ETA: 22s - loss: 144.42 - ETA: 21s - loss: 144.58 - ETA: 20s - loss: 144.74 - ETA: 18s - loss: 144.89 - ETA: 17s - loss: 145.03 - ETA: 16s - loss: 145.17 - ETA: 14s - loss: 145.31 - ETA: 13s - loss: 145.45 - ETA: 12s - loss: 145.58 - ETA: 10s - loss: 145.69 - ETA: 9s - loss: 145.8073 - ETA: 8s - loss: 145.908 - ETA: 6s - loss: 146.001 - ETA: 5s - loss: 146.090 - ETA: 4s - loss: 146.173 - ETA: 2s - loss: 146.251 - ETA: 1s - loss: 146.323 - ETA: 0s - loss: 146.391 - 152s 1s/step - loss: 146.4575 - val_loss: 130.2334\n",
+ "\n",
+ "Epoch 00008: val_loss did not improve from 92.39297\n",
+ "Epoch 9/30\n",
+ "111/111 [==============================] - ETA: 2:17 - loss: 88.04 - ETA: 2:26 - loss: 90.59 - ETA: 2:36 - loss: 100.187 - ETA: 2:41 - loss: 103.375 - ETA: 2:44 - loss: 117.527 - ETA: 2:42 - loss: 125.915 - ETA: 2:36 - loss: 130.735 - ETA: 2:30 - loss: 133.228 - ETA: 2:26 - loss: 134.321 - ETA: 2:23 - loss: 134.956 - ETA: 2:19 - loss: 135.895 - ETA: 2:17 - loss: 136.239 - ETA: 2:15 - loss: 136.271 - ETA: 2:14 - loss: 136.102 - ETA: 2:13 - loss: 135.743 - ETA: 2:11 - loss: 135.216 - ETA: 2:09 - loss: 134.642 - ETA: 2:08 - loss: 134.047 - ETA: 2:05 - loss: 133.434 - ETA: 2:04 - loss: 133.251 - ETA: 2:02 - loss: 133.001 - ETA: 2:00 - loss: 132.659 - ETA: 1:59 - loss: 132.280 - ETA: 1:57 - loss: 131.844 - ETA: 1:55 - loss: 131.422 - ETA: 1:54 - loss: 130.969 - ETA: 1:52 - loss: 130.518 - ETA: 1:50 - loss: 130.090 - ETA: 1:49 - loss: 129.724 - ETA: 1:47 - loss: 129.341 - ETA: 1:46 - loss: 128.957 - ETA: 1:44 - loss: 128.572 - ETA: 1:43 - loss: 128.193 - ETA: 1:41 - loss: 127.856 - ETA: 1:40 - loss: 127.519 - ETA: 1:39 - loss: 127.200 - ETA: 1:37 - loss: 126.869 - ETA: 1:36 - loss: 126.786 - ETA: 1:34 - loss: 126.717 - ETA: 1:33 - loss: 126.629 - ETA: 1:31 - loss: 126.533 - ETA: 1:30 - loss: 126.441 - ETA: 1:29 - loss: 126.345 - ETA: 1:27 - loss: 126.279 - ETA: 1:26 - loss: 126.203 - ETA: 1:25 - loss: 126.108 - ETA: 1:23 - loss: 126.013 - ETA: 1:22 - loss: 125.909 - ETA: 1:20 - loss: 125.796 - ETA: 1:19 - loss: 125.670 - ETA: 1:18 - loss: 125.534 - ETA: 1:16 - loss: 125.412 - ETA: 1:15 - loss: 125.285 - ETA: 1:14 - loss: 125.210 - ETA: 1:12 - loss: 125.161 - ETA: 1:11 - loss: 125.110 - ETA: 1:10 - loss: 125.043 - ETA: 1:08 - loss: 124.969 - ETA: 1:07 - loss: 124.898 - ETA: 1:06 - loss: 124.820 - ETA: 1:04 - loss: 124.741 - ETA: 1:03 - loss: 124.660 - ETA: 1:02 - loss: 124.617 - ETA: 1:00 - loss: 124.567 - ETA: 59s - loss: 124.511 - ETA: 58s - loss: 124.44 - ETA: 56s - loss: 124.41 - ETA: 55s - loss: 124.37 - ETA: 54s - loss: 124.37 - ETA: 53s - loss: 124.36 - ETA: 51s - loss: 124.35 - ETA: 50s - loss: 124.34 - ETA: 49s - loss: 124.33 - ETA: 47s - loss: 124.31 - ETA: 46s - loss: 124.29 - ETA: 45s - loss: 124.26 - ETA: 43s - loss: 124.23 - ETA: 42s - loss: 124.21 - ETA: 41s - loss: 124.20 - ETA: 40s - loss: 124.18 - ETA: 38s - loss: 124.19 - ETA: 37s - loss: 124.20 - ETA: 36s - loss: 124.21 - ETA: 34s - loss: 124.21 - ETA: 33s - loss: 124.20 - ETA: 32s - loss: 124.20 - ETA: 30s - loss: 124.19 - ETA: 29s - loss: 124.18 - ETA: 28s - loss: 124.17 - ETA: 27s - loss: 124.15 - ETA: 25s - loss: 124.13 - ETA: 24s - loss: 124.11 - ETA: 23s - loss: 124.09 - ETA: 21s - loss: 124.07 - ETA: 20s - loss: 124.04 - ETA: 19s - loss: 124.03 - ETA: 18s - loss: 124.00 - ETA: 16s - loss: 123.98 - ETA: 15s - loss: 123.95 - ETA: 14s - loss: 123.92 - ETA: 12s - loss: 123.89 - ETA: 11s - loss: 123.86 - ETA: 10s - loss: 123.82 - ETA: 8s - loss: 124.1051 - ETA: 7s - loss: 124.370 - ETA: 6s - loss: 124.623 - ETA: 5s - loss: 124.872 - ETA: 3s - loss: 125.110 - ETA: 2s - loss: 125.339 - ETA: 1s - loss: 125.561 - ETA: 0s - loss: 125.775 - 144s 1s/step - loss: 125.9853 - val_loss: 130.9303\n",
+ "\n",
+ "Epoch 00009: val_loss did not improve from 92.39297\n",
+ "Epoch 10/30\n",
+ "111/111 [==============================] - ETA: 2:21 - loss: 1210.77 - ETA: 2:15 - loss: 934.7905 - ETA: 2:14 - loss: 782.613 - ETA: 2:14 - loss: 681.525 - ETA: 2:14 - loss: 609.044 - ETA: 2:12 - loss: 588.306 - ETA: 2:11 - loss: 566.383 - ETA: 2:10 - loss: 544.780 - ETA: 2:09 - loss: 524.345 - ETA: 2:08 - loss: 505.886 - ETA: 2:07 - loss: 511.448 - ETA: 2:05 - loss: 513.057 - ETA: 2:04 - loss: 511.740 - ETA: 2:03 - loss: 508.796 - ETA: 2:01 - loss: 504.554 - ETA: 2:00 - loss: 499.437 - ETA: 1:59 - loss: 493.812 - ETA: 1:57 - loss: 488.255 - ETA: 1:56 - loss: 482.670 - ETA: 1:55 - loss: 476.949 - ETA: 1:54 - loss: 471.199 - ETA: 1:53 - loss: 465.460 - ETA: 1:51 - loss: 459.686 - ETA: 1:50 - loss: 453.951 - ETA: 1:49 - loss: 448.323 - ETA: 1:48 - loss: 442.806 - ETA: 1:46 - loss: 437.412 - ETA: 1:45 - loss: 432.148 - ETA: 1:44 - loss: 427.018 - ETA: 1:42 - loss: 422.021 - ETA: 1:41 - loss: 417.173 - ETA: 1:40 - loss: 412.454 - ETA: 1:39 - loss: 407.861 - ETA: 1:37 - loss: 403.388 - ETA: 1:36 - loss: 399.051 - ETA: 1:35 - loss: 394.835 - ETA: 1:33 - loss: 390.725 - ETA: 1:32 - loss: 386.741 - ETA: 1:32 - loss: 382.880 - ETA: 1:30 - loss: 379.130 - ETA: 1:29 - loss: 375.485 - ETA: 1:28 - loss: 371.950 - ETA: 1:26 - loss: 368.516 - ETA: 1:25 - loss: 365.174 - ETA: 1:24 - loss: 361.918 - ETA: 1:23 - loss: 358.770 - ETA: 1:22 - loss: 355.700 - ETA: 1:21 - loss: 352.714 - ETA: 1:20 - loss: 349.830 - ETA: 1:19 - loss: 347.015 - ETA: 1:17 - loss: 344.268 - ETA: 1:16 - loss: 341.580 - ETA: 1:15 - loss: 338.947 - ETA: 1:13 - loss: 336.376 - ETA: 1:12 - loss: 333.867 - ETA: 1:11 - loss: 331.420 - ETA: 1:09 - loss: 329.028 - ETA: 1:08 - loss: 326.693 - ETA: 1:07 - loss: 324.409 - ETA: 1:06 - loss: 322.177 - ETA: 1:04 - loss: 319.989 - ETA: 1:03 - loss: 317.870 - ETA: 1:02 - loss: 315.792 - ETA: 1:00 - loss: 313.757 - ETA: 59s - loss: 311.766 - ETA: 58s - loss: 309.81 - ETA: 56s - loss: 307.90 - ETA: 55s - loss: 306.02 - ETA: 54s - loss: 304.19 - ETA: 52s - loss: 302.39 - ETA: 51s - loss: 300.62 - ETA: 50s - loss: 298.89 - ETA: 49s - loss: 297.21 - ETA: 47s - loss: 295.58 - ETA: 46s - loss: 293.99 - ETA: 45s - loss: 292.44 - ETA: 43s - loss: 290.90 - ETA: 42s - loss: 289.40 - ETA: 41s - loss: 287.92 - ETA: 40s - loss: 286.47 - ETA: 38s - loss: 285.04 - ETA: 37s - loss: 283.63 - ETA: 36s - loss: 282.25 - ETA: 34s - loss: 280.88 - ETA: 33s - loss: 279.54 - ETA: 32s - loss: 278.22 - ETA: 30s - loss: 276.91 - ETA: 29s - loss: 275.63 - ETA: 28s - loss: 274.36 - ETA: 27s - loss: 273.12 - ETA: 25s - loss: 271.89 - ETA: 24s - loss: 270.68 - ETA: 23s - loss: 269.50 - ETA: 21s - loss: 268.34 - ETA: 20s - loss: 267.26 - ETA: 19s - loss: 266.19 - ETA: 18s - loss: 265.14 - ETA: 16s - loss: 264.10 - ETA: 15s - loss: 263.09 - ETA: 14s - loss: 262.09 - ETA: 12s - loss: 261.11 - ETA: 11s - loss: 260.15 - ETA: 10s - loss: 259.23 - ETA: 9s - loss: 258.3324 - ETA: 7s - loss: 257.447 - ETA: 6s - loss: 256.573 - ETA: 5s - loss: 255.715 - ETA: 3s - loss: 254.870 - ETA: 2s - loss: 254.035 - ETA: 1s - loss: 253.208 - ETA: 0s - loss: 252.394 - 145s 1s/step - loss: 251.5949 - val_loss: 130.1973\n",
+ "\n",
+ "Epoch 00010: val_loss did not improve from 92.39297\n",
+ "Epoch 11/30\n",
+ "111/111 [==============================] - ETA: 2:17 - loss: 82.14 - ETA: 2:17 - loss: 83.89 - ETA: 2:15 - loss: 84.48 - ETA: 2:15 - loss: 85.05 - ETA: 2:14 - loss: 86.04 - ETA: 2:12 - loss: 86.46 - ETA: 2:12 - loss: 87.33 - ETA: 2:10 - loss: 111.623 - ETA: 2:08 - loss: 129.906 - ETA: 2:07 - loss: 142.461 - ETA: 2:06 - loss: 151.321 - ETA: 2:04 - loss: 157.634 - ETA: 2:03 - loss: 162.239 - ETA: 2:02 - loss: 165.486 - ETA: 2:01 - loss: 169.006 - ETA: 1:59 - loss: 172.688 - ETA: 1:58 - loss: 175.484 - ETA: 1:57 - loss: 177.585 - ETA: 1:56 - loss: 179.140 - ETA: 1:55 - loss: 180.395 - ETA: 1:53 - loss: 181.293 - ETA: 1:52 - loss: 181.904 - ETA: 1:51 - loss: 182.251 - ETA: 1:49 - loss: 182.366 - ETA: 1:48 - loss: 182.317 - ETA: 1:47 - loss: 182.141 - ETA: 1:46 - loss: 181.882 - ETA: 1:44 - loss: 181.555 - ETA: 1:43 - loss: 181.183 - ETA: 1:42 - loss: 180.781 - ETA: 1:41 - loss: 180.315 - ETA: 1:40 - loss: 179.827 - ETA: 1:39 - loss: 179.303 - ETA: 1:37 - loss: 178.748 - ETA: 1:36 - loss: 178.183 - ETA: 1:35 - loss: 177.645 - ETA: 1:34 - loss: 177.087 - ETA: 1:32 - loss: 176.505 - ETA: 1:31 - loss: 175.921 - ETA: 1:30 - loss: 175.330 - ETA: 1:28 - loss: 174.726 - ETA: 1:27 - loss: 174.133 - ETA: 1:26 - loss: 173.527 - ETA: 1:25 - loss: 172.912 - ETA: 1:23 - loss: 172.293 - ETA: 1:22 - loss: 171.673 - ETA: 1:21 - loss: 171.056 - ETA: 1:20 - loss: 170.446 - ETA: 1:18 - loss: 169.833 - ETA: 1:17 - loss: 169.236 - ETA: 1:16 - loss: 168.662 - ETA: 1:14 - loss: 168.091 - ETA: 1:13 - loss: 167.523 - ETA: 1:12 - loss: 166.957 - ETA: 1:11 - loss: 166.405 - ETA: 1:09 - loss: 165.859 - ETA: 1:08 - loss: 165.322 - ETA: 1:07 - loss: 164.796 - ETA: 1:06 - loss: 164.282 - ETA: 1:04 - loss: 163.782 - ETA: 1:03 - loss: 163.425 - ETA: 1:02 - loss: 163.069 - ETA: 1:00 - loss: 162.713 - ETA: 59s - loss: 162.354 - ETA: 58s - loss: 162.00 - ETA: 57s - loss: 161.65 - ETA: 55s - loss: 161.55 - ETA: 54s - loss: 161.43 - ETA: 53s - loss: 161.30 - ETA: 52s - loss: 161.17 - ETA: 50s - loss: 161.10 - ETA: 49s - loss: 161.02 - ETA: 48s - loss: 160.93 - ETA: 47s - loss: 160.84 - ETA: 45s - loss: 160.75 - ETA: 44s - loss: 160.65 - ETA: 43s - loss: 160.54 - ETA: 41s - loss: 160.43 - ETA: 40s - loss: 160.31 - ETA: 39s - loss: 160.19 - ETA: 38s - loss: 160.07 - ETA: 36s - loss: 159.95 - ETA: 35s - loss: 159.82 - ETA: 34s - loss: 159.70 - ETA: 33s - loss: 159.56 - ETA: 31s - loss: 159.42 - ETA: 30s - loss: 159.29 - ETA: 29s - loss: 159.17 - ETA: 27s - loss: 159.04 - ETA: 26s - loss: 158.90 - ETA: 25s - loss: 158.77 - ETA: 24s - loss: 158.64 - ETA: 22s - loss: 158.51 - ETA: 21s - loss: 158.38 - ETA: 20s - loss: 158.26 - ETA: 19s - loss: 158.15 - ETA: 17s - loss: 158.03 - ETA: 16s - loss: 157.90 - ETA: 15s - loss: 157.77 - ETA: 13s - loss: 157.65 - ETA: 12s - loss: 157.53 - ETA: 11s - loss: 157.41 - ETA: 10s - loss: 157.29 - ETA: 8s - loss: 157.1734 - ETA: 7s - loss: 157.048 - ETA: 6s - loss: 156.919 - ETA: 5s - loss: 156.788 - ETA: 3s - loss: 156.656 - ETA: 2s - loss: 156.538 - ETA: 1s - loss: 156.418 - ETA: 0s - loss: 156.298 - 143s 1s/step - loss: 156.1802 - val_loss: 120.8451\n",
+ "\n",
+ "Epoch 00011: val_loss did not improve from 92.39297\n",
+ "Epoch 00011: early stopping\n",
+ "4/4 [==============================] - ETA: 2s - loss: 88.71 - ETA: 1s - loss: 92.99 - ETA: 0s - loss: 94.99 - ETA: 0s - loss: 94.33 - 3s 617ms/step - loss: 94.3345\n",
+ "Val Score: 94.33446502685547\n",
+ "====================================================================================\n",
+ "\n",
+ "\n",
+ "Computation time : 88.165 min\n"
]
}
],
"source": [
"from processing.models import fit_and_evaluate\n",
+ "t0 = time()\n",
"n_folds=2\n",
"epochs=30\n",
"batch_size=8\n",
@@ -641,14 +735,29 @@
" model.compile(loss=\"mean_absolute_percentage_error\", optimizer=opt)\n",
" t_x, val_x, t_y, val_y = custom_shuffle_split(trainAttrX,train_dataset,trainY,test_size = 0.1) \n",
" model_history.append(fit_and_evaluate(t_x, val_x, t_y, val_y, epochs, batch_size,model,es,cp))\n",
- " print(\"=======\"*12, end=\"\\n\\n\\n\")"
+ " print(\"=======\"*12, end=\"\\n\\n\\n\")\n",
+ "\n",
+ "print(\"Computation time : \", round((time() - t0)/60,3), \"min\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import matplotlib.pyplot as plt \n",
"\n",
@@ -661,9 +770,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.title('Validation loss vs Epochs')\n",
"plt.plot(model_history[0].history['val_loss'], label='Training Fold 1')\n",
@@ -674,13 +796,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import load_model\n",
"\n",
- "model = load_model('superposition_injection.h5')"
+ "#model = load_model('clean_notebooks/superposition_injection.h5')"
]
},
{
@@ -697,13 +819,6 @@
"outputs": [],
"source": []
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -720,9 +835,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:predicting ...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "avg. FVC: 2690.479018721756, std FVC 832.7709592986739\n",
+ "mean difference : 23.46%, std: 23.43%\n",
+ "competition score : -4.6089146124620175\n"
+ ]
+ }
+ ],
"source": [
"from postprocessing.evaluate import evaluate_hybrid, compute_score\n",
"\n",
@@ -733,9 +865,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "35/35 [==============================] - ETA: 1:20 - loss: 88.34 - ETA: 27s - loss: 96.2808 - ETA: 28s - loss: 95.329 - ETA: 26s - loss: 93.686 - ETA: 24s - loss: 95.828 - ETA: 23s - loss: 96.293 - ETA: 21s - loss: 102.11 - ETA: 20s - loss: 101.70 - ETA: 18s - loss: 98.6838 - ETA: 17s - loss: 97.543 - ETA: 17s - loss: 96.316 - ETA: 16s - loss: 97.854 - ETA: 15s - loss: 97.472 - ETA: 14s - loss: 96.384 - ETA: 13s - loss: 96.501 - ETA: 12s - loss: 96.732 - ETA: 12s - loss: 100.53 - ETA: 11s - loss: 99.3360 - ETA: 10s - loss: 99.497 - ETA: 9s - loss: 98.590 - ETA: 9s - loss: 97.62 - ETA: 8s - loss: 96.97 - ETA: 7s - loss: 103.975 - ETA: 7s - loss: 103.135 - ETA: 6s - loss: 102.916 - ETA: 5s - loss: 103.809 - ETA: 5s - loss: 103.880 - ETA: 4s - loss: 103.673 - ETA: 3s - loss: 104.146 - ETA: 3s - loss: 103.427 - ETA: 2s - loss: 103.145 - ETA: 1s - loss: 102.842 - ETA: 1s - loss: 102.092 - ETA: 0s - loss: 104.518 - ETA: 0s - loss: 104.499 - 24s 628ms/step - loss: 104.4996\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "104.49958801269531"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"model.evaluate([trainAttrX, train_dataset], trainY)"
]
@@ -749,9 +899,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:predicting ...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "avg. FVC: 2690.479018721756, std FVC 832.7709592986739\n",
+ "mean difference : 28.07%, std: 26.37%\n",
+ "competition score : -4.6092218738085515\n"
+ ]
+ }
+ ],
"source": [
"preds = evaluate_hybrid(model, df, testAttrX, test_dataset, testY, sc)\n",
"conf, score = compute_score(testY,preds.flatten())\n",
@@ -760,9 +927,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9/9 [==============================] - ETA: 5s - loss: 99.14 - ETA: 4s - loss: 91.89 - ETA: 3s - loss: 93.06 - ETA: 3s - loss: 94.78 - ETA: 2s - loss: 91.39 - ETA: 1s - loss: 90.71 - ETA: 1s - loss: 88.88 - ETA: 0s - loss: 87.44 - ETA: 0s - loss: 88.91 - 6s 618ms/step - loss: 88.9136\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "88.91364288330078"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"model.evaluate([testAttrX, test_dataset], testY)"
]
@@ -774,6 +959,13 @@
"# G - Sample submission file"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -798,7 +990,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.3"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/clean_notebooks/superposition_injection.h5 b/clean_notebooks/superposition_injection.h5
index 58bcae7f79f185b60ba33b403081259b50293bdc..646d23c2026865fa5b31d5ccec59a24533dcc987 100644
Binary files a/clean_notebooks/superposition_injection.h5 and b/clean_notebooks/superposition_injection.h5 differ
diff --git a/processing/models.py b/processing/models.py
index 6546270ca88c30c597fe1167aee3457e9204cdbb..b0968c1a40193df3e041a442eb48fdf32399ce3e 100644
--- a/processing/models.py
+++ b/processing/models.py
@@ -17,7 +17,7 @@ from tensorflow import TensorShape
from sklearn.model_selection import train_test_split
-def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False):
+def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False, type_cnn='simple'):
# initialize the input shape and channel dimension, assuming
# TensorFlow/channels-last ordering
inputShape = (height, width, depth)
@@ -41,18 +41,19 @@ def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False):
# flatten the volume, then FC => RELU => BN => DROPOUT
x = Flatten()(x)
x = Dense(16)(x)
- x = Activation("relu")(x) #sigmoid ou tanh
+ x = Activation("tanh")(x) #sigmoid ou tanh
x = BatchNormalization(axis=chanDim)(x)
x = Dropout(0.5)(x)
# apply another FC layer, this one to match the number of nodes
# coming out of the MLP
x = Dense(4)(x)
- x = Activation("relu")(x) #sigmoid ou tanh
+ x = Activation("tanh")(x) #sigmoid ou tanh
# check to see if the regression node should be added
# la couche suivante ne sert à rien pour l'hybride, à garder pour le modèle cnn seule
- if regress:
- x = Dense(1, activation="linear")(x)
+ if type_cnn == 'simple':
+ if regress:
+ x = Dense(1, activation="linear")(x)
# construct the CNN
model = Model(inputs, x)
# return the CNN
@@ -61,28 +62,32 @@ def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False):
def create_mlp(dim, regress=True):
# define our MLP network
model = Sequential()
- model.add(Dense(8, input_dim=dim, activation="relu"))#tanh
- model.add(Dense(4, activation="relu")) # tanh
+ model.add(Dense(8, input_dim=dim, activation="tanh"))#tanh
+ model.add(Dropout(0.3))
+ model.add(Dense(4, activation="tanh")) # tanh
# add dense for regression
- model.add(Dense(1, activation="linear")) #tanh
+ #model.add(Dense(1, activation="linear")) #tanh
# return our model
return model
def create_mlp2(dim,regress = True): #mieux que mlp,
model = Sequential()
model.add(GaussianNoise(0.2, input_shape=(dim,)))
- model.add(Dense(8, activation="relu"))
- model.add(Dense(4, activation="relu"))
+ model.add(Dropout(0.5))
+ #modified from relu to tanh
+ model.add(Dense(8, activation="tanh"))
+ model.add(Dropout(0.5))
+ model.add(Dense(4, activation="tanh"))
# add dense for regression
- model.add(Dense(1)) # couche à enlever trop de couches dans ce modele, et surtout pas à 1, idem pour mlp pour hybride
+ #model.add(Dense(1)) # couche à enlever trop de couches dans ce modele, et surtout pas à 1, idem pour mlp pour hybride
return model
def create_hybrid(nb_attributes,shape=(240,240,1)):
# create cnn and mlp models
mlp = create_mlp(nb_attributes)
- cnn = create_cnn(*shape)
+ cnn = create_cnn(*shape,type_cnn='hybrid')
combinedInput = concatenate([mlp.output, cnn.output])
- x = Dense(4, activation="relu")(combinedInput) #tanh
+ x = Dense(4, activation="tanh")(combinedInput) #tanh
x = Dense(1, activation="linear")(x)
model = Model(inputs=[mlp.input, cnn.input], outputs=x)
return model
@@ -142,7 +147,7 @@ def create_transfer_learning(new_model, custom_model, modify_name,input_channel
x = new.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.5)(x)
- x = Dense(1)(x)
+ #x = Dense(1)(x)
model = Model(new.input, x)
for layer in new.layers:
@@ -155,7 +160,7 @@ def create_hybrid_transfer(nb_attributes,new_model, custom_model, modify_name,in
mlp = create_mlp(nb_attributes)
cnn = create_transfer_learning(new_model, custom_model, modify_name,input_channel)
combinedInput = concatenate([mlp.output, cnn.output])
- x = Dense(4, activation="relu")(combinedInput)
+ x = Dense(4, activation="tanh")(combinedInput)
x = Dense(1, activation="linear")(x)
model = Model(inputs=[mlp.input, cnn.input], outputs=x)
return model