diff --git a/example/3_example_DNN_training.ipynb b/example/3_example_DNN_training.ipynb index a5df66c..c41a71b 100644 --- a/example/3_example_DNN_training.ipynb +++ b/example/3_example_DNN_training.ipynb @@ -20,19 +20,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "id": "1be4e6e8-7757-4874-ba6a-680f497ebafc", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-05 14:28:38.199970: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import xarray as xr\n", @@ -69,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "id": "5b5fb223-c59f-408c-ac20-733c296586ab", "metadata": {}, "outputs": [], @@ -81,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "2aa7df27-8efa-410b-9210-fa83d8425d33", "metadata": {}, "outputs": [], @@ -114,18 +105,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "e4e5059d-fe8a-481e-98f3-6d1aacc8c4ee", "metadata": {}, "outputs": [], "source": [ - "# path to store results\n", - "out_path = './dnn_examples/results'\n", - "\n", "# JackKnife parameters\n", - "val_split_year = 2017\n", - "output_list = ['sig', 'slop', 'curv']\n", - "input_list = ['TG1', 'TG2', 'TG3', 'WG1', 'WG2', 'WG3', 'BIOMA1', 'BIOMA2']\n", + "JackKnife = {\n", + " 'val_split_year': 2017,\n", + " 'output_list': ['sig', 'slop', 'curv'],\n", + " 'input_list': ['TG1', 'TG2', 'TG3', 'WG1', 'WG2', 'WG3', 'BIOMA1', 'BIOMA2'],\n", + " 'out_path': './dnn_examples/results'\n", + "}\n", "\n", "# Training parameters\n", "searching_space = {\n", @@ -157,23 +148,67 @@ "source": [ "## Run the training\n", "\n", - "In this example, we will demonstrate how to run the training parralel pergrid with a dask cluster.\n", + "In this example, we will demonstrate how to run the training parralel per grid (partition) with a dask cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "41975f9b-15d2-4be0-948f-c29d892073b6", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# a function for training\n", + "def training_func(gpi_num, df, JackKnife, searching_space, optimize_space):\n", + " \n", + " # remove NA data\n", + " gpi_data = df.compute()\n", + " gpi_data.dropna(inplace=True)\n", + "\n", + " # add time to index\n", + " gpi_data.set_index(\"time\", inplace=True, drop=True)\n", "\n", - "To add a Dask cluster to this notebook, you can use the Dask JupyterLab extension (look for the Dask logo on the left tab of the JupyterLab interface):\n", + " gpi = JackknifeGPI(gpi_data,\n", + " JackKnife['val_split_year'],\n", + " JackKnife['input_list'],\n", + " JackKnife['output_list'],\n", + " outpath=f\"{JackKnife['out_path']}/gpi{gpi_num+1}\")\n", "\n", - "Click on the Dask logo;\n", - "click the `Scale` button, set up the number of workers to the number of available cores;\n", - "then click `<>` to add a code block.\n", - "Then a code cell will be added to this notebook. Please drop this cell below. By executing it, a Dask SLURMCluster will be created.\n", + " gpi.train(searching_space=searching_space,\n", + " optimize_space=optimize_space,\n", + " normalize_method='standard',\n", + " training_method='dnn',\n", + " performance_method='rmse',\n", + " verbose=2)\n", + "\n", + " gpi.export_best()\n", "\n", - "--ADD DASK SLURMCluster HERE--" + " return gpi.apr_perf, gpi.post_perf" ] }, { "cell_type": "markdown", - "id": "5252c553-88a5-4277-ae81-831cdaacd590", - "metadata": {}, + "id": "be5512c2-57c9-4401-8539-c2eaaa65d3c0", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ + "To add a Dask cluster to this notebook, you can use the Dask JupyterLab extension (look for the Dask logo on the left tab of the JupyterLab interface):\n", + "\n", + "Click on the Dask logo; click the Scale button, set up the number of workers to the number of available cores; then click <> to add a code block. Then a code cell will be added to this notebook. Please drop this cell below. By executing it, a Dask SLURMCluster will be created.\n", + "\n", "This is an example code of DASK cluster\n", "\n", "```python\n", @@ -186,7570 +221,688 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "03b52e16-246d-4b3b-a53f-6b626820f93b", + "execution_count": 20, + "id": "f97ca4d5-3313-4fe0-9fa5-5812c38da17e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dask.distributed import Client\n", + "\n", + "client = Client(\"tcp://127.0.0.1:39007\")\n", + "client" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8d24b179-6484-40f3-a1b1-d1f43021c574", "metadata": { "editable": true, + "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "39/39 - 1s - loss: 1.4965 - mae: 1.0097 - acc: 0.2862 - val_loss: 1.3360 - val_mae: 0.9692 - val_acc: 0.2422 - 670ms/epoch - 17ms/step\n", - "Epoch 2/5\n", - "39/39 - 0s - loss: 1.1287 - mae: 0.8863 - acc: 0.2416 - val_loss: 1.0883 - val_mae: 0.8765 - val_acc: 0.2422 - 60ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "39/39 - 0s - loss: 0.9632 - mae: 0.8147 - acc: 0.2805 - val_loss: 0.9537 - val_mae: 0.8152 - val_acc: 0.3011 - 56ms/epoch - 1ms/step\n", - "Epoch 4/5\n", - "39/39 - 0s - loss: 0.8553 - mae: 0.7605 - acc: 0.3604 - val_loss: 0.8517 - val_mae: 0.7616 - val_acc: 0.4141 - 57ms/epoch - 1ms/step\n", - "Epoch 5/5\n", - "39/39 - 0s - loss: 0.7693 - mae: 0.7120 - acc: 0.5139 - val_loss: 0.7689 - val_mae: 0.7141 - val_acc: 0.5794 - 54ms/epoch - 1ms/step\n", - "Epoch 1/5\n", - "9/9 - 1s - loss: 0.7398 - mae: 0.6895 - acc: 0.5659 - val_loss: 0.6410 - val_mae: 0.6399 - val_acc: 0.7103 - 1s/epoch - 116ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.5704 - mae: 0.5849 - acc: 0.6908 - val_loss: 0.5873 - val_mae: 0.5818 - val_acc: 0.7201 - 40ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.5460 - mae: 0.5687 - acc: 0.7064 - val_loss: 0.6024 - val_mae: 0.5918 - val_acc: 0.6907 - 39ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.5355 - mae: 0.5596 - acc: 0.7088 - val_loss: 0.5685 - val_mae: 0.5717 - val_acc: 0.7398 - 41ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.5075 - mae: 0.5431 - acc: 0.7215 - val_loss: 0.5467 - val_mae: 0.5672 - val_acc: 0.7201 - 39ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "25/25 - 1s - loss: 0.8745 - mae: 0.7605 - acc: 0.4390 - val_loss: 0.7753 - val_mae: 0.6978 - val_acc: 0.6039 - 1s/epoch - 43ms/step\n", - "Epoch 2/5\n", - "25/25 - 0s - loss: 0.6784 - mae: 0.6444 - acc: 0.6024 - val_loss: 0.6333 - val_mae: 0.6301 - val_acc: 0.6137 - 43ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "25/25 - 0s - loss: 0.6310 - mae: 0.6192 - acc: 0.6048 - val_loss: 0.6166 - val_mae: 0.6165 - val_acc: 0.6072 - 45ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "25/25 - 0s - loss: 0.6142 - mae: 0.6052 - acc: 0.6061 - val_loss: 0.6079 - val_mae: 0.6110 - val_acc: 0.6154 - 47ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "25/25 - 0s - loss: 0.5869 - mae: 0.5891 - acc: 0.6237 - val_loss: 0.5728 - val_mae: 0.5844 - val_acc: 0.6727 - 47ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "36/36 - 2s - loss: 0.8086 - mae: 0.7244 - acc: 0.5115 - val_loss: 0.7420 - val_mae: 0.6913 - val_acc: 0.5925 - 2s/epoch - 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mae: 0.7112 - acc: 0.5771 - val_loss: 0.7584 - val_mae: 0.6957 - val_acc: 0.6166 - 78ms/epoch - 1ms/step\n", - "Epoch 3/5\n", - "63/63 - 0s - loss: 0.7074 - mae: 0.6564 - acc: 0.6199 - val_loss: 0.6815 - val_mae: 0.6471 - val_acc: 0.6248 - 78ms/epoch - 1ms/step\n", - "Epoch 4/5\n", - "63/63 - 0s - loss: 0.6659 - mae: 0.6291 - acc: 0.6268 - val_loss: 0.6543 - val_mae: 0.6284 - val_acc: 0.6362 - 80ms/epoch - 1ms/step\n", - "Epoch 5/5\n", - "63/63 - 0s - loss: 0.6475 - mae: 0.6184 - acc: 0.6383 - val_loss: 0.6391 - val_mae: 0.6170 - val_acc: 0.6591 - 79ms/epoch - 1ms/step\n", - "Epoch 1/5\n", - "8/8 - 1s - loss: 0.9397 - mae: 0.7804 - acc: 0.4405 - val_loss: 0.9100 - val_mae: 0.7687 - val_acc: 0.4992 - 964ms/epoch - 121ms/step\n", - "Epoch 2/5\n", - "8/8 - 0s - loss: 0.8673 - mae: 0.7341 - acc: 0.5082 - val_loss: 0.8368 - val_mae: 0.7160 - val_acc: 0.5302 - 47ms/epoch - 6ms/step\n", - "Epoch 3/5\n", - "8/8 - 0s - loss: 0.8114 - mae: 0.6967 - acc: 0.5604 - val_loss: 0.7932 - val_mae: 0.6920 - 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"91/91 - 0s - loss: 0.5017 - mae: 0.5377 - acc: 0.7190 - val_loss: 0.5112 - val_mae: 0.5360 - val_acc: 0.7357 - 128ms/epoch - 1ms/step\n", - "Epoch 1/5\n", - "8/8 - 1s - loss: 0.8792 - mae: 0.7351 - acc: 0.5302 - val_loss: 0.8114 - val_mae: 0.7109 - val_acc: 0.6052 - 907ms/epoch - 113ms/step\n", - "Epoch 2/5\n", - "8/8 - 0s - loss: 0.7619 - mae: 0.6779 - acc: 0.6228 - val_loss: 0.7127 - val_mae: 0.6638 - val_acc: 0.6460 - 29ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "8/8 - 0s - loss: 0.7190 - mae: 0.6573 - acc: 0.6611 - val_loss: 0.6966 - val_mae: 0.6534 - val_acc: 0.6672 - 30ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "8/8 - 0s - loss: 0.6858 - mae: 0.6366 - acc: 0.6595 - val_loss: 0.6720 - val_mae: 0.6358 - val_acc: 0.6803 - 31ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "8/8 - 0s - loss: 0.6526 - mae: 0.6193 - acc: 0.6819 - val_loss: 0.6548 - val_mae: 0.6222 - val_acc: 0.6933 - 30ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "12/12 - 1s - loss: 0.7913 - mae: 0.7092 - acc: 0.4988 - val_loss: 0.6915 - val_mae: 0.6589 - val_acc: 0.6248 - 928ms/epoch - 77ms/step\n", - "Epoch 2/5\n", - "12/12 - 0s - loss: 0.6886 - mae: 0.6504 - acc: 0.6134 - val_loss: 0.6799 - val_mae: 0.6445 - val_acc: 0.6281 - 40ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "12/12 - 0s - loss: 0.6774 - mae: 0.6397 - acc: 0.6211 - val_loss: 0.6741 - val_mae: 0.6437 - val_acc: 0.6281 - 39ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "12/12 - 0s - loss: 0.6717 - mae: 0.6370 - acc: 0.6215 - val_loss: 0.6685 - val_mae: 0.6393 - val_acc: 0.6395 - 39ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "12/12 - 0s - loss: 0.6671 - mae: 0.6350 - acc: 0.6166 - val_loss: 0.6606 - val_mae: 0.6361 - val_acc: 0.6330 - 43ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "37/37 - 1s - loss: 0.8979 - mae: 0.7525 - acc: 0.5273 - val_loss: 0.8504 - val_mae: 0.7330 - val_acc: 0.5677 - 1s/epoch - 31ms/step\n", - "Epoch 2/5\n", - "37/37 - 0s - loss: 0.8087 - mae: 0.7074 - acc: 0.5844 - val_loss: 0.7979 - val_mae: 0.7084 - val_acc: 0.5808 - 69ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "37/37 - 0s - loss: 0.7777 - mae: 0.6941 - acc: 0.5914 - val_loss: 0.7812 - val_mae: 0.6959 - val_acc: 0.5905 - 68ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "37/37 - 0s - loss: 0.7738 - mae: 0.6898 - acc: 0.5897 - val_loss: 0.7824 - val_mae: 0.6999 - val_acc: 0.5922 - 69ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "37/37 - 0s - loss: 0.7650 - mae: 0.6860 - acc: 0.6007 - val_loss: 0.7790 - val_mae: 0.6976 - val_acc: 0.5987 - 72ms/epoch - 2ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2013/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2013/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model9735608708598509099/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model9735608708598509099/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "16/16 [==============================] - 0s 884us/step\n", - "INFO:tensorflow:Assets written to: /tmp/tmp_model8365092277182578156/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model8365092277182578156/assets\n" - ] + "outputs": [], + "source": [ + "from dask.distributed import wait" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "76fef52a-8993-44a4-bf2b-79700070316a", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "54/54 [==============================] - 0s 705us/step\n", - "Epoch 1/5\n", - "38/38 - 1s - loss: 1.1348 - mae: 0.8574 - acc: 0.2831 - val_loss: 1.0555 - val_mae: 0.8334 - val_acc: 0.3878 - 650ms/epoch - 17ms/step\n", - "Epoch 2/5\n", - "38/38 - 0s - loss: 0.9966 - mae: 0.8091 - acc: 0.3830 - val_loss: 0.9682 - val_mae: 0.7986 - val_acc: 0.4224 - 54ms/epoch - 1ms/step\n", - "Epoch 3/5\n", - "38/38 - 0s - loss: 0.9046 - mae: 0.7702 - acc: 0.4276 - val_loss: 0.8876 - val_mae: 0.7612 - val_acc: 0.4406 - 52ms/epoch - 1ms/step\n", - "Epoch 4/5\n", - "38/38 - 0s - loss: 0.8249 - mae: 0.7328 - acc: 0.4837 - val_loss: 0.8200 - val_mae: 0.7286 - val_acc: 0.5281 - 51ms/epoch - 1ms/step\n", - "Epoch 5/5\n", - "38/38 - 0s - loss: 0.7672 - mae: 0.7032 - acc: 0.5724 - val_loss: 0.7759 - val_mae: 0.7056 - val_acc: 0.6749 - 53ms/epoch - 1ms/step\n", - "Epoch 1/5\n", - "25/25 - 2s - loss: 0.8508 - mae: 0.7330 - acc: 0.5233 - val_loss: 0.7711 - val_mae: 0.7028 - val_acc: 0.5627 - 2s/epoch - 61ms/step\n", - "Epoch 2/5\n", - "25/25 - 0s - loss: 0.7175 - mae: 0.6635 - acc: 0.5761 - val_loss: 0.7173 - val_mae: 0.6660 - val_acc: 0.5842 - 78ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "25/25 - 0s - loss: 0.6711 - mae: 0.6344 - acc: 0.5993 - val_loss: 0.6993 - val_mae: 0.6448 - val_acc: 0.6238 - 72ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "25/25 - 0s - loss: 0.6433 - mae: 0.6160 - acc: 0.6405 - val_loss: 0.6604 - val_mae: 0.6165 - val_acc: 0.6568 - 72ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "25/25 - 0s - loss: 0.6036 - mae: 0.5930 - acc: 0.6678 - val_loss: 0.6300 - val_mae: 0.6025 - val_acc: 0.6551 - 71ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "34/34 - 1s - loss: 0.7377 - mae: 0.6685 - acc: 0.5869 - val_loss: 0.6407 - val_mae: 0.6269 - val_acc: 0.6865 - 806ms/epoch - 24ms/step\n", - "Epoch 2/5\n", - "34/34 - 0s - loss: 0.5839 - mae: 0.5917 - acc: 0.6781 - val_loss: 0.5936 - val_mae: 0.5839 - val_acc: 0.6865 - 55ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "34/34 - 0s - loss: 0.5386 - mae: 0.5645 - acc: 0.6913 - val_loss: 0.5732 - val_mae: 0.5752 - val_acc: 0.6964 - 54ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "34/34 - 0s - loss: 0.5247 - mae: 0.5557 - acc: 0.7037 - val_loss: 0.5504 - val_mae: 0.5596 - val_acc: 0.7294 - 51ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "34/34 - 0s - loss: 0.5210 - mae: 0.5542 - acc: 0.7086 - val_loss: 0.5412 - val_mae: 0.5586 - val_acc: 0.7244 - 53ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "31/31 - 2s - loss: 0.7852 - mae: 0.6981 - acc: 0.5312 - val_loss: 0.7040 - val_mae: 0.6564 - val_acc: 0.5842 - 2s/epoch - 52ms/step\n", - "Epoch 2/5\n", - "31/31 - 0s - loss: 0.6715 - mae: 0.6391 - acc: 0.6087 - val_loss: 0.6798 - val_mae: 0.6408 - val_acc: 0.6155 - 88ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "31/31 - 0s - loss: 0.6537 - mae: 0.6280 - acc: 0.6257 - val_loss: 0.6787 - val_mae: 0.6373 - val_acc: 0.6254 - 87ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "31/31 - 0s - loss: 0.6436 - mae: 0.6228 - acc: 0.6368 - val_loss: 0.6665 - val_mae: 0.6320 - val_acc: 0.6353 - 88ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "31/31 - 0s - loss: 0.6282 - mae: 0.6131 - acc: 0.6414 - val_loss: 0.6518 - val_mae: 0.6215 - val_acc: 0.6436 - 81ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "11/11 - 1s - loss: 1.0042 - mae: 0.8049 - acc: 0.4957 - val_loss: 0.9474 - val_mae: 0.7791 - val_acc: 0.5693 - 1s/epoch - 134ms/step\n", - "Epoch 2/5\n", - "11/11 - 0s - loss: 0.8550 - mae: 0.7270 - acc: 0.5493 - val_loss: 0.7683 - val_mae: 0.6764 - val_acc: 0.6799 - 56ms/epoch - 5ms/step\n", - "Epoch 3/5\n", - "11/11 - 0s - loss: 0.7135 - mae: 0.6566 - acc: 0.6595 - val_loss: 0.6801 - val_mae: 0.6416 - val_acc: 0.6716 - 56ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "11/11 - 0s - loss: 0.6581 - mae: 0.6253 - acc: 0.6682 - val_loss: 0.6554 - val_mae: 0.6242 - val_acc: 0.7063 - 60ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "11/11 - 0s - loss: 0.6308 - mae: 0.6091 - acc: 0.6752 - val_loss: 0.6384 - val_mae: 0.6145 - val_acc: 0.6931 - 59ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "11/11 - 1s - loss: 0.9075 - mae: 0.7490 - acc: 0.5390 - val_loss: 0.7713 - val_mae: 0.6885 - val_acc: 0.5875 - 1s/epoch - 104ms/step\n", - "Epoch 2/5\n", - "11/11 - 0s - loss: 0.7509 - mae: 0.6744 - acc: 0.6273 - val_loss: 0.7310 - val_mae: 0.6673 - val_acc: 0.6485 - 49ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "11/11 - 0s - loss: 0.7158 - mae: 0.6527 - acc: 0.6409 - val_loss: 0.7118 - val_mae: 0.6566 - val_acc: 0.6617 - 43ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "11/11 - 0s - loss: 0.6772 - mae: 0.6356 - acc: 0.6541 - val_loss: 0.6851 - val_mae: 0.6423 - val_acc: 0.6601 - 45ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "11/11 - 0s - loss: 0.6341 - mae: 0.6127 - acc: 0.6694 - val_loss: 0.6430 - val_mae: 0.6163 - val_acc: 0.6700 - 49ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "9/9 - 1s - loss: 0.8586 - mae: 0.7335 - acc: 0.5749 - val_loss: 0.7312 - val_mae: 0.6751 - val_acc: 0.6353 - 1s/epoch - 126ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.7297 - mae: 0.6692 - acc: 0.6418 - val_loss: 0.7098 - val_mae: 0.6614 - val_acc: 0.6865 - 43ms/epoch - 5ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.6886 - mae: 0.6454 - acc: 0.6587 - val_loss: 0.6467 - val_mae: 0.6257 - val_acc: 0.7096 - 44ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.6405 - mae: 0.6216 - acc: 0.6793 - val_loss: 0.6164 - val_mae: 0.6026 - val_acc: 0.7244 - 46ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.6176 - mae: 0.6110 - acc: 0.6822 - val_loss: 0.5999 - val_mae: 0.5958 - val_acc: 0.7294 - 48ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "14/14 - 1s - loss: 0.9188 - mae: 0.7512 - acc: 0.5638 - val_loss: 0.8249 - val_mae: 0.7141 - val_acc: 0.6353 - 817ms/epoch - 58ms/step\n", - "Epoch 2/5\n", - "14/14 - 0s - loss: 0.7782 - mae: 0.6885 - acc: 0.6306 - val_loss: 0.7622 - val_mae: 0.6808 - val_acc: 0.6485 - 46ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "14/14 - 0s - loss: 0.7319 - mae: 0.6628 - acc: 0.6517 - val_loss: 0.7394 - val_mae: 0.6721 - val_acc: 0.6898 - 44ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "14/14 - 0s - loss: 0.7109 - mae: 0.6514 - acc: 0.6587 - val_loss: 0.7110 - val_mae: 0.6509 - val_acc: 0.6832 - 47ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "14/14 - 0s - loss: 0.6839 - mae: 0.6383 - acc: 0.6661 - val_loss: 0.7056 - val_mae: 0.6467 - val_acc: 0.6634 - 45ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 1.0366 - mae: 0.8177 - acc: 0.3669 - val_loss: 1.0209 - val_mae: 0.8126 - val_acc: 0.3548 - 680ms/epoch - 68ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.9986 - mae: 0.7959 - acc: 0.3875 - val_loss: 0.9808 - val_mae: 0.7915 - val_acc: 0.4010 - 32ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.9648 - mae: 0.7745 - acc: 0.4783 - val_loss: 0.9504 - val_mae: 0.7744 - val_acc: 0.5446 - 34ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.9418 - mae: 0.7620 - acc: 0.5588 - val_loss: 0.9322 - val_mae: 0.7654 - val_acc: 0.5578 - 33ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.9224 - mae: 0.7532 - acc: 0.5658 - val_loss: 0.9146 - val_mae: 0.7569 - val_acc: 0.5710 - 34ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "162/162 - 1s - loss: 0.7177 - mae: 0.6598 - acc: 0.6121 - val_loss: 0.6766 - val_mae: 0.6370 - val_acc: 0.6568 - 1s/epoch - 6ms/step\n", - "Epoch 2/5\n", - "162/162 - 0s - loss: 0.6209 - mae: 0.6048 - acc: 0.6570 - val_loss: 0.6320 - val_mae: 0.6114 - val_acc: 0.6782 - 223ms/epoch - 1ms/step\n", - "Epoch 3/5\n", - "162/162 - 0s - loss: 0.6009 - mae: 0.5948 - acc: 0.6707 - val_loss: 0.6152 - val_mae: 0.5992 - val_acc: 0.7030 - 231ms/epoch - 1ms/step\n", - "Epoch 4/5\n", - "162/162 - 0s - loss: 0.5860 - mae: 0.5861 - acc: 0.6768 - val_loss: 0.6215 - val_mae: 0.5996 - val_acc: 0.6980 - 239ms/epoch - 1ms/step\n", - "Epoch 5/5\n", - "162/162 - 0s - loss: 0.5729 - mae: 0.5787 - acc: 0.6925 - val_loss: 0.5957 - val_mae: 0.5865 - val_acc: 0.7145 - 242ms/epoch - 1ms/step\n", - "Epoch 1/5\n", - "10/10 - 2s - loss: 0.9074 - mae: 0.7679 - acc: 0.3706 - val_loss: 0.8094 - val_mae: 0.7294 - val_acc: 0.5825 - 2s/epoch - 166ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.7544 - mae: 0.6885 - acc: 0.5790 - val_loss: 0.7094 - val_mae: 0.6636 - val_acc: 0.5875 - 56ms/epoch - 6ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.7132 - mae: 0.6646 - acc: 0.5827 - val_loss: 0.6917 - val_mae: 0.6510 - val_acc: 0.5908 - 58ms/epoch - 6ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.6720 - mae: 0.6360 - acc: 0.6401 - val_loss: 0.6725 - val_mae: 0.6327 - val_acc: 0.6584 - 57ms/epoch - 6ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.6478 - mae: 0.6245 - acc: 0.6641 - val_loss: 0.6509 - val_mae: 0.6181 - val_acc: 0.6766 - 60ms/epoch - 6ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2016/assets\n" - ] + "tags": [] + }, + "outputs": [], + "source": [ + "# Use client.map with enumerate to parallelize the loop across workers\n", + "futures = [\n", + " client.submit(training_func, gpi_num, df, JackKnife, searching_space, optimize_space) for gpi_num, df in enumerate(train_bags)\n", + "]\n", + "\n", + "# Wait for all computations to finish\n", + "wait(futures)\n", + "\n", + "# Get the results\n", + "results = client.gather(futures)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "12e262ee-3d20-4d4b-b889-48d06549735b", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2016/assets\n" - ] + "tags": [] + }, + "outputs": [], + "source": [ + "# Close the Dask client\n", + "client.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "c93bf79b-723c-465a-8f06-e859909ac56a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" }, + "tags": [] + }, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model14471771070037154195/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model14471771070037154195/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "17/17 [==============================] - 0s 788us/step\n", - "Epoch 1/5\n", - "42/42 - 1s - loss: 0.8451 - mae: 0.7419 - acc: 0.6160 - val_loss: 0.8033 - val_mae: 0.7223 - val_acc: 0.6556 - 682ms/epoch - 16ms/step\n", - "Epoch 2/5\n", - "42/42 - 0s - loss: 0.7436 - mae: 0.6852 - acc: 0.6652 - val_loss: 0.7194 - val_mae: 0.6726 - val_acc: 0.6737 - 66ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "42/42 - 0s - loss: 0.6856 - mae: 0.6476 - acc: 0.6670 - val_loss: 0.6787 - val_mae: 0.6455 - val_acc: 0.6798 - 66ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "42/42 - 0s - loss: 0.6536 - mae: 0.6286 - acc: 0.6795 - val_loss: 0.6560 - val_mae: 0.6296 - val_acc: 0.6979 - 65ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "42/42 - 0s - loss: 0.6330 - mae: 0.6164 - acc: 0.6916 - val_loss: 0.6429 - val_mae: 0.6216 - val_acc: 0.7024 - 76ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "9/9 - 1s - loss: 0.9558 - mae: 0.7825 - acc: 0.5612 - val_loss: 0.8445 - val_mae: 0.7259 - val_acc: 0.6450 - 1s/epoch - 137ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.7515 - mae: 0.6807 - acc: 0.6364 - val_loss: 0.7448 - val_mae: 0.6867 - val_acc: 0.6329 - 52ms/epoch - 6ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.6909 - mae: 0.6543 - acc: 0.6489 - val_loss: 0.6834 - val_mae: 0.6508 - val_acc: 0.6828 - 52ms/epoch - 6ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.6530 - mae: 0.6309 - acc: 0.6655 - val_loss: 0.6695 - val_mae: 0.6402 - val_acc: 0.6782 - 53ms/epoch - 6ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.6379 - mae: 0.6201 - acc: 0.6704 - val_loss: 0.6575 - val_mae: 0.6302 - val_acc: 0.6813 - 54ms/epoch - 6ms/step\n", - "Epoch 1/5\n", - "26/26 - 1s - loss: 0.9946 - mae: 0.7977 - acc: 0.5200 - val_loss: 0.9175 - val_mae: 0.7516 - val_acc: 0.6133 - 1s/epoch - 54ms/step\n", - "Epoch 2/5\n", - "26/26 - 0s - loss: 0.8959 - mae: 0.7367 - acc: 0.6032 - val_loss: 0.8452 - val_mae: 0.7201 - val_acc: 0.6118 - 66ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "26/26 - 0s - loss: 0.8199 - mae: 0.7107 - acc: 0.6024 - val_loss: 0.8038 - val_mae: 0.7012 - val_acc: 0.6284 - 69ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "26/26 - 0s - loss: 0.8033 - mae: 0.7024 - acc: 0.6081 - val_loss: 0.7875 - val_mae: 0.6953 - val_acc: 0.6224 - 67ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "26/26 - 0s - loss: 0.7879 - mae: 0.6955 - acc: 0.6111 - val_loss: 0.7732 - val_mae: 0.6856 - val_acc: 0.6239 - 65ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "25/25 - 1s - loss: 0.8246 - mae: 0.7275 - acc: 0.5552 - val_loss: 0.6846 - val_mae: 0.6571 - val_acc: 0.7069 - 1s/epoch - 47ms/step\n", - "Epoch 2/5\n", - "25/25 - 0s - loss: 0.6016 - mae: 0.5976 - acc: 0.6916 - val_loss: 0.6004 - val_mae: 0.5936 - val_acc: 0.7356 - 57ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "25/25 - 0s - loss: 0.5631 - mae: 0.5701 - acc: 0.6999 - val_loss: 0.5800 - val_mae: 0.5810 - val_acc: 0.7356 - 59ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "25/25 - 0s - loss: 0.5528 - mae: 0.5645 - acc: 0.7041 - val_loss: 0.5744 - val_mae: 0.5777 - val_acc: 0.7417 - 56ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "25/25 - 0s - loss: 0.5419 - mae: 0.5569 - acc: 0.7094 - val_loss: 0.5689 - val_mae: 0.5720 - val_acc: 0.7402 - 56ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "11/11 - 1s - loss: 0.9547 - mae: 0.7727 - acc: 0.5831 - val_loss: 0.8264 - val_mae: 0.7145 - val_acc: 0.6420 - 675ms/epoch - 61ms/step\n", - "Epoch 2/5\n", - "11/11 - 0s - loss: 0.7564 - mae: 0.6780 - acc: 0.6444 - val_loss: 0.6908 - val_mae: 0.6526 - val_acc: 0.6752 - 33ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "11/11 - 0s - loss: 0.6553 - mae: 0.6335 - acc: 0.6606 - val_loss: 0.6526 - val_mae: 0.6333 - val_acc: 0.7069 - 34ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "11/11 - 0s - loss: 0.6116 - mae: 0.6080 - acc: 0.6844 - val_loss: 0.6248 - val_mae: 0.6088 - val_acc: 0.7160 - 35ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "11/11 - 0s - loss: 0.5827 - mae: 0.5876 - acc: 0.6999 - val_loss: 0.6053 - val_mae: 0.5966 - val_acc: 0.7251 - 37ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "9/9 - 2s - loss: 1.0322 - mae: 0.8227 - acc: 0.2944 - val_loss: 1.0348 - val_mae: 0.8325 - val_acc: 0.3263 - 2s/epoch - 186ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 1.0253 - mae: 0.8211 - acc: 0.3371 - val_loss: 1.0285 - val_mae: 0.8304 - val_acc: 0.3263 - 40ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 1.0127 - mae: 0.8160 - acc: 0.3371 - val_loss: 1.0087 - val_mae: 0.8216 - val_acc: 0.3157 - 38ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.9841 - mae: 0.8042 - acc: 0.3813 - val_loss: 0.9712 - val_mae: 0.8049 - val_acc: 0.4789 - 38ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.9316 - mae: 0.7816 - acc: 0.4955 - val_loss: 0.9077 - val_mae: 0.7746 - val_acc: 0.5695 - 37ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "189/189 - 2s - loss: 0.6662 - mae: 0.6387 - acc: 0.6206 - val_loss: 0.6466 - val_mae: 0.6305 - val_acc: 0.6873 - 2s/epoch - 10ms/step\n", - "Epoch 2/5\n", - "189/189 - 0s - loss: 0.6078 - mae: 0.6062 - acc: 0.6746 - val_loss: 0.6313 - val_mae: 0.6230 - val_acc: 0.6813 - 357ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "189/189 - 0s - loss: 0.5933 - mae: 0.5962 - acc: 0.6807 - val_loss: 0.5961 - val_mae: 0.6071 - val_acc: 0.7190 - 377ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "189/189 - 0s - loss: 0.5749 - mae: 0.5892 - acc: 0.6924 - val_loss: 0.5822 - val_mae: 0.5949 - val_acc: 0.7115 - 375ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "189/189 - 0s - loss: 0.5699 - mae: 0.5858 - acc: 0.7003 - val_loss: 0.5927 - val_mae: 0.6010 - val_acc: 0.7251 - 369ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "49/49 - 1s - loss: 0.8185 - mae: 0.7113 - acc: 0.5964 - val_loss: 0.6579 - val_mae: 0.6339 - val_acc: 0.6299 - 1s/epoch - 23ms/step\n", - "Epoch 2/5\n", - "49/49 - 0s - loss: 0.6454 - mae: 0.6192 - acc: 0.6285 - val_loss: 0.6263 - val_mae: 0.6097 - val_acc: 0.6465 - 95ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "49/49 - 0s - loss: 0.6173 - mae: 0.6002 - acc: 0.6542 - val_loss: 0.6118 - val_mae: 0.5974 - val_acc: 0.6873 - 86ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "49/49 - 0s - loss: 0.5972 - mae: 0.5873 - acc: 0.6712 - val_loss: 0.5956 - val_mae: 0.5890 - val_acc: 0.6979 - 84ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "49/49 - 0s - loss: 0.5809 - mae: 0.5774 - acc: 0.6837 - val_loss: 0.5890 - val_mae: 0.5853 - val_acc: 0.7100 - 113ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 1.0137 - mae: 0.8137 - acc: 0.5299 - val_loss: 0.9908 - val_mae: 0.8100 - val_acc: 0.5770 - 1s/epoch - 145ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.9523 - mae: 0.7827 - acc: 0.5907 - val_loss: 0.8952 - val_mae: 0.7598 - val_acc: 0.6239 - 40ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.8322 - mae: 0.7131 - acc: 0.6489 - val_loss: 0.7522 - val_mae: 0.6761 - val_acc: 0.6692 - 46ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.7118 - mae: 0.6537 - acc: 0.6867 - val_loss: 0.6632 - val_mae: 0.6353 - val_acc: 0.7039 - 41ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.6404 - mae: 0.6145 - acc: 0.6946 - val_loss: 0.6246 - val_mae: 0.6140 - val_acc: 0.7205 - 44ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "19/19 - 1s - loss: 0.8516 - mae: 0.7292 - acc: 0.5650 - val_loss: 0.6899 - val_mae: 0.6442 - val_acc: 0.6450 - 791ms/epoch - 42ms/step\n", - "Epoch 2/5\n", - "19/19 - 0s - loss: 0.6315 - mae: 0.6137 - acc: 0.6527 - val_loss: 0.6263 - val_mae: 0.6090 - val_acc: 0.6903 - 45ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "19/19 - 0s - loss: 0.5881 - mae: 0.5847 - acc: 0.6841 - val_loss: 0.6168 - val_mae: 0.5992 - val_acc: 0.7175 - 45ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "19/19 - 0s - loss: 0.5607 - mae: 0.5682 - acc: 0.7011 - val_loss: 0.5786 - val_mae: 0.5779 - val_acc: 0.7311 - 48ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "19/19 - 0s - loss: 0.5342 - mae: 0.5530 - acc: 0.7166 - val_loss: 0.5742 - val_mae: 0.5778 - val_acc: 0.7311 - 46ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 1.0067 - mae: 0.8091 - acc: 0.3923 - val_loss: 1.0000 - val_mae: 0.8073 - val_acc: 0.4079 - 1s/epoch - 147ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.9639 - mae: 0.7857 - acc: 0.4388 - val_loss: 0.9631 - val_mae: 0.7903 - val_acc: 0.4260 - 43ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.9323 - mae: 0.7705 - acc: 0.4550 - val_loss: 0.9228 - val_mae: 0.7718 - val_acc: 0.4637 - 38ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.8959 - mae: 0.7513 - acc: 0.4958 - val_loss: 0.8789 - val_mae: 0.7495 - val_acc: 0.4698 - 38ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.8455 - mae: 0.7276 - acc: 0.5385 - val_loss: 0.8180 - val_mae: 0.7170 - val_acc: 0.5363 - 39ms/epoch - 4ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2009/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2009/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model11773041152100058256/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model11773041152100058256/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8/8 [==============================] - 0s 873us/step\n", - "Epoch 1/5\n", - "42/42 - 1s - loss: 0.9453 - mae: 0.7822 - acc: 0.4129 - val_loss: 0.8731 - val_mae: 0.7579 - val_acc: 0.4797 - 676ms/epoch - 16ms/step\n", - "Epoch 2/5\n", - "42/42 - 0s - loss: 0.7988 - mae: 0.7108 - acc: 0.5597 - val_loss: 0.7667 - val_mae: 0.6988 - val_acc: 0.6421 - 62ms/epoch - 1ms/step\n", - "Epoch 3/5\n", - "42/42 - 0s - loss: 0.7199 - mae: 0.6685 - acc: 0.6500 - val_loss: 0.7091 - val_mae: 0.6627 - val_acc: 0.6812 - 64ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "42/42 - 0s - loss: 0.6770 - mae: 0.6435 - acc: 0.6699 - val_loss: 0.6751 - val_mae: 0.6402 - val_acc: 0.6932 - 65ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "42/42 - 0s - loss: 0.6477 - mae: 0.6253 - acc: 0.6786 - val_loss: 0.6532 - val_mae: 0.6261 - val_acc: 0.6992 - 65ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "33/33 - 1s - loss: 0.8000 - mae: 0.7074 - acc: 0.5529 - val_loss: 0.7466 - val_mae: 0.6760 - val_acc: 0.6135 - 1s/epoch - 43ms/step\n", - "Epoch 2/5\n", - "33/33 - 0s - loss: 0.7106 - mae: 0.6541 - acc: 0.6093 - val_loss: 0.7171 - val_mae: 0.6637 - val_acc: 0.6180 - 108ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "33/33 - 0s - loss: 0.6979 - mae: 0.6488 - acc: 0.6139 - val_loss: 0.7009 - val_mae: 0.6567 - val_acc: 0.6256 - 115ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "33/33 - 0s - loss: 0.6940 - mae: 0.6460 - acc: 0.6255 - val_loss: 0.6915 - val_mae: 0.6515 - val_acc: 0.6271 - 145ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "33/33 - 0s - loss: 0.6870 - mae: 0.6420 - acc: 0.6180 - val_loss: 0.6910 - val_mae: 0.6523 - val_acc: 0.6226 - 188ms/epoch - 6ms/step\n", - "Epoch 1/5\n", - "11/11 - 1s - loss: 1.0229 - mae: 0.8205 - acc: 0.2890 - val_loss: 1.0226 - val_mae: 0.8289 - val_acc: 0.4286 - 1s/epoch - 119ms/step\n", - "Epoch 2/5\n", - "11/11 - 0s - loss: 0.9925 - mae: 0.8076 - acc: 0.4859 - val_loss: 0.9858 - val_mae: 0.8115 - val_acc: 0.5504 - 37ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "11/11 - 0s - loss: 0.9476 - mae: 0.7861 - acc: 0.5288 - val_loss: 0.9261 - val_mae: 0.7809 - val_acc: 0.5774 - 37ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "11/11 - 0s - loss: 0.8862 - mae: 0.7549 - acc: 0.6191 - val_loss: 0.8497 - val_mae: 0.7385 - val_acc: 0.6496 - 37ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "11/11 - 0s - loss: 0.8155 - mae: 0.7125 - acc: 0.6428 - val_loss: 0.7818 - val_mae: 0.6909 - val_acc: 0.6180 - 36ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "28/28 - 1s - loss: 0.9347 - mae: 0.7791 - acc: 0.4848 - val_loss: 0.7406 - val_mae: 0.6850 - val_acc: 0.6165 - 1s/epoch - 52ms/step\n", - "Epoch 2/5\n", - "28/28 - 0s - loss: 0.6837 - mae: 0.6451 - acc: 0.6127 - val_loss: 0.6812 - val_mae: 0.6499 - val_acc: 0.6075 - 63ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "28/28 - 0s - loss: 0.6635 - mae: 0.6332 - acc: 0.6108 - val_loss: 0.6685 - val_mae: 0.6426 - val_acc: 0.6256 - 64ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "28/28 - 0s - loss: 0.6618 - mae: 0.6321 - 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val_loss: 0.6213 - val_mae: 0.6066 - val_acc: 0.6939 - 37ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "13/13 - 0s - loss: 0.5997 - mae: 0.5928 - acc: 0.6881 - val_loss: 0.6034 - val_mae: 0.5940 - val_acc: 0.7136 - 37ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "13/13 - 0s - loss: 0.5693 - mae: 0.5754 - acc: 0.7049 - val_loss: 0.5759 - val_mae: 0.5777 - val_acc: 0.7169 - 44ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "13/13 - 0s - loss: 0.5462 - mae: 0.5622 - acc: 0.7086 - val_loss: 0.5714 - val_mae: 0.5797 - val_acc: 0.7136 - 38ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "13/13 - 1s - loss: 0.7038 - mae: 0.6625 - acc: 0.6152 - val_loss: 0.6123 - val_mae: 0.6176 - val_acc: 0.7169 - 936ms/epoch - 72ms/step\n", - "Epoch 2/5\n", - "13/13 - 0s - loss: 0.5848 - mae: 0.5909 - acc: 0.7094 - val_loss: 0.5883 - val_mae: 0.5894 - val_acc: 0.7283 - 85ms/epoch - 7ms/step\n", - "Epoch 3/5\n", - "13/13 - 0s - loss: 0.5550 - mae: 0.5762 - acc: 0.7201 - val_loss: 0.5569 - val_mae: 0.5752 - val_acc: 0.7332 - 55ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "13/13 - 0s - loss: 0.5284 - mae: 0.5602 - acc: 0.7279 - val_loss: 0.5537 - val_mae: 0.5658 - val_acc: 0.7250 - 57ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "13/13 - 0s - loss: 0.5112 - mae: 0.5506 - acc: 0.7340 - val_loss: 0.5227 - val_mae: 0.5559 - val_acc: 0.7463 - 67ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "9/9 - 1s - loss: 0.8234 - mae: 0.7180 - acc: 0.5439 - val_loss: 0.7031 - val_mae: 0.6683 - val_acc: 0.5925 - 1s/epoch - 131ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.6887 - mae: 0.6526 - acc: 0.6193 - val_loss: 0.6856 - val_mae: 0.6503 - val_acc: 0.6105 - 43ms/epoch - 5ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.6710 - mae: 0.6379 - acc: 0.6242 - val_loss: 0.6870 - val_mae: 0.6469 - val_acc: 0.5941 - 44ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.6627 - mae: 0.6330 - acc: 0.6246 - val_loss: 0.6623 - val_mae: 0.6391 - val_acc: 0.6301 - 43ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.6521 - mae: 0.6274 - acc: 0.6361 - val_loss: 0.6490 - val_mae: 0.6314 - val_acc: 0.6285 - 46ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "19/19 - 1s - loss: 0.7865 - mae: 0.7031 - acc: 0.5225 - val_loss: 0.7154 - val_mae: 0.6637 - val_acc: 0.6105 - 1s/epoch - 74ms/step\n", - "Epoch 2/5\n", - "19/19 - 0s - loss: 0.6895 - mae: 0.6497 - acc: 0.6119 - val_loss: 0.6726 - val_mae: 0.6441 - val_acc: 0.6203 - 53ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "19/19 - 0s - loss: 0.6737 - mae: 0.6416 - acc: 0.6193 - val_loss: 0.6825 - val_mae: 0.6538 - val_acc: 0.6154 - 53ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "19/19 - 0s - loss: 0.6744 - mae: 0.6411 - acc: 0.6189 - val_loss: 0.6708 - val_mae: 0.6503 - val_acc: 0.6219 - 56ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "19/19 - 0s - loss: 0.6659 - mae: 0.6385 - acc: 0.6201 - val_loss: 0.6600 - val_mae: 0.6436 - val_acc: 0.6252 - 55ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "11/11 - 2s - loss: 0.9437 - mae: 0.7632 - acc: 0.4971 - val_loss: 0.8906 - val_mae: 0.7387 - val_acc: 0.6105 - 2s/epoch - 162ms/step\n", - "Epoch 2/5\n", - "11/11 - 0s - loss: 0.8323 - mae: 0.7057 - acc: 0.6299 - val_loss: 0.7629 - val_mae: 0.6811 - val_acc: 0.6465 - 60ms/epoch - 5ms/step\n", - "Epoch 3/5\n", - "11/11 - 0s - loss: 0.7199 - mae: 0.6580 - acc: 0.6418 - val_loss: 0.6894 - val_mae: 0.6477 - val_acc: 0.6661 - 62ms/epoch - 6ms/step\n", - "Epoch 4/5\n", - "11/11 - 0s - loss: 0.6765 - mae: 0.6339 - acc: 0.6508 - val_loss: 0.6732 - val_mae: 0.6280 - val_acc: 0.6759 - 65ms/epoch - 6ms/step\n", - "Epoch 5/5\n", - "11/11 - 0s - loss: 0.6539 - mae: 0.6198 - acc: 0.6553 - val_loss: 0.6612 - val_mae: 0.6257 - val_acc: 0.6727 - 65ms/epoch - 6ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 0.9461 - mae: 0.7817 - acc: 0.5775 - val_loss: 0.8897 - val_mae: 0.7685 - val_acc: 0.6088 - 736ms/epoch - 74ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.8647 - mae: 0.7429 - acc: 0.6139 - val_loss: 0.8196 - val_mae: 0.7326 - val_acc: 0.6416 - 37ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - 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val_mae: 0.7520 - val_acc: 0.5728 - 45ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "11/11 - 0s - loss: 0.8858 - mae: 0.7357 - acc: 0.5770 - val_loss: 0.8786 - val_mae: 0.7381 - val_acc: 0.5777 - 43ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "45/45 - 1s - loss: 0.7799 - mae: 0.7134 - acc: 0.5033 - val_loss: 0.7373 - val_mae: 0.6883 - val_acc: 0.5761 - 950ms/epoch - 21ms/step\n", - "Epoch 2/5\n", - "45/45 - 0s - loss: 0.6795 - mae: 0.6515 - acc: 0.5898 - val_loss: 0.6866 - val_mae: 0.6626 - val_acc: 0.6137 - 85ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "45/45 - 0s - loss: 0.6524 - mae: 0.6352 - acc: 0.6225 - val_loss: 0.6702 - val_mae: 0.6466 - val_acc: 0.6383 - 73ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "45/45 - 0s - loss: 0.6239 - mae: 0.6178 - acc: 0.6434 - val_loss: 0.6466 - val_mae: 0.6309 - val_acc: 0.6448 - 79ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "45/45 - 0s - loss: 0.6002 - mae: 0.6032 - acc: 0.6664 - val_loss: 0.6312 - val_mae: 0.6172 - val_acc: 0.6612 - 80ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "9/9 - 2s - loss: 0.8531 - mae: 0.7377 - acc: 0.4172 - val_loss: 0.7322 - val_mae: 0.6862 - val_acc: 0.6023 - 2s/epoch - 170ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.7054 - mae: 0.6576 - acc: 0.6066 - val_loss: 0.6771 - val_mae: 0.6544 - val_acc: 0.6137 - 35ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.6695 - mae: 0.6393 - acc: 0.6197 - val_loss: 0.6572 - val_mae: 0.6385 - val_acc: 0.6170 - 36ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.6501 - mae: 0.6282 - acc: 0.6250 - val_loss: 0.6548 - val_mae: 0.6384 - val_acc: 0.6219 - 40ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.6438 - mae: 0.6233 - acc: 0.6299 - val_loss: 0.6372 - val_mae: 0.6255 - val_acc: 0.6285 - 36ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "10/10 - 2s - loss: 1.0258 - mae: 0.8197 - acc: 0.3266 - val_loss: 1.0305 - val_mae: 0.8323 - val_acc: 0.3339 - 2s/epoch - 197ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 1.0221 - mae: 0.8204 - acc: 0.3266 - val_loss: 1.0286 - val_mae: 0.8329 - val_acc: 0.3339 - 38ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 1.0199 - mae: 0.8209 - acc: 0.3074 - val_loss: 1.0283 - val_mae: 0.8336 - val_acc: 0.3224 - 42ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 1.0192 - mae: 0.8217 - acc: 0.3074 - val_loss: 1.0283 - val_mae: 0.8340 - val_acc: 0.3224 - 38ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 1.0189 - mae: 0.8220 - acc: 0.3074 - val_loss: 1.0282 - val_mae: 0.8340 - val_acc: 0.3224 - 40ms/epoch - 4ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2015/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi1/all_years/optimized_model_2015/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model1150385945659884709/assets\n" - ] - }, - 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loss: 1.0645 - mae: 0.8405 - acc: 0.2609 - val_loss: 0.9097 - val_mae: 0.7895 - val_acc: 0.3568 - 712ms/epoch - 19ms/step\n", - "Epoch 2/5\n", - "38/38 - 0s - loss: 0.8636 - mae: 0.7559 - acc: 0.4622 - val_loss: 0.7855 - val_mae: 0.7248 - val_acc: 0.5745 - 59ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "38/38 - 0s - loss: 0.7747 - mae: 0.7068 - acc: 0.5965 - val_loss: 0.7168 - val_mae: 0.6848 - val_acc: 0.6214 - 64ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "38/38 - 0s - loss: 0.7227 - mae: 0.6755 - acc: 0.6149 - val_loss: 0.6673 - val_mae: 0.6528 - val_acc: 0.6281 - 65ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "38/38 - 0s - loss: 0.6832 - mae: 0.6495 - acc: 0.6179 - val_loss: 0.6317 - val_mae: 0.6277 - val_acc: 0.6466 - 72ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "8/8 - 1s - loss: 0.9051 - mae: 0.7750 - acc: 0.5361 - val_loss: 0.8396 - val_mae: 0.7497 - val_acc: 0.5628 - 802ms/epoch - 100ms/step\n", - "Epoch 2/5\n", - "8/8 - 0s - loss: 0.8276 - mae: 0.7345 - acc: 0.6107 - val_loss: 0.7711 - val_mae: 0.7099 - val_acc: 0.6332 - 31ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "8/8 - 0s - loss: 0.7731 - mae: 0.7064 - acc: 0.6481 - val_loss: 0.7209 - val_mae: 0.6831 - val_acc: 0.6583 - 34ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "8/8 - 0s - loss: 0.7271 - mae: 0.6807 - acc: 0.6565 - val_loss: 0.6725 - val_mae: 0.6533 - val_acc: 0.6616 - 31ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "8/8 - 0s - loss: 0.6854 - mae: 0.6538 - acc: 0.6598 - val_loss: 0.6323 - val_mae: 0.6266 - val_acc: 0.6683 - 32ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "14/14 - 1s - loss: 0.9346 - mae: 0.7827 - acc: 0.4316 - val_loss: 0.8096 - val_mae: 0.7193 - val_acc: 0.5243 - 782ms/epoch - 56ms/step\n", - "Epoch 2/5\n", - "14/14 - 0s - loss: 0.8202 - mae: 0.7196 - acc: 0.6204 - val_loss: 0.7739 - val_mae: 0.6989 - val_acc: 0.6784 - 38ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "14/14 - 0s - loss: 0.7762 - mae: 0.7004 - acc: 0.6674 - val_loss: 0.7322 - val_mae: 0.6782 - val_acc: 0.6834 - 39ms/epoch - 3ms/step\n", - 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val_acc: 0.6903 - 82ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "15/15 - 0s - loss: 0.6418 - mae: 0.6164 - acc: 0.6890 - val_loss: 0.5938 - val_mae: 0.6009 - val_acc: 0.6980 - 76ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "36/36 - 1s - loss: 1.0033 - mae: 0.8033 - acc: 0.3646 - val_loss: 0.9093 - val_mae: 0.7671 - val_acc: 0.4052 - 1s/epoch - 35ms/step\n", - "Epoch 2/5\n", - "36/36 - 0s - loss: 0.9372 - mae: 0.7647 - acc: 0.4248 - val_loss: 0.8837 - val_mae: 0.7517 - val_acc: 0.4499 - 98ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "36/36 - 0s - loss: 0.9165 - mae: 0.7552 - acc: 0.4537 - val_loss: 0.9025 - val_mae: 0.7572 - val_acc: 0.4407 - 106ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "36/36 - 0s - loss: 0.9030 - mae: 0.7499 - acc: 0.4576 - val_loss: 0.8460 - val_mae: 0.7319 - val_acc: 0.4607 - 108ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "36/36 - 0s - loss: 0.8851 - mae: 0.7417 - acc: 0.4815 - val_loss: 0.8250 - val_mae: 0.7217 - val_acc: 0.4761 - 109ms/epoch - 3ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi2/all_years/optimized_model_2007/assets\n" - 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val_acc: 0.7163 - 281ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "170/170 - 0s - loss: 0.4555 - mae: 0.5124 - acc: 0.7458 - val_loss: 0.4322 - val_mae: 0.4963 - val_acc: 0.7414 - 276ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "23/23 - 1s - loss: 0.7158 - mae: 0.6732 - acc: 0.6975 - val_loss: 0.5824 - val_mae: 0.5952 - val_acc: 0.6677 - 1s/epoch - 51ms/step\n", - "Epoch 2/5\n", - "23/23 - 0s - loss: 0.5724 - mae: 0.5843 - acc: 0.7058 - val_loss: 0.5315 - val_mae: 0.5605 - val_acc: 0.6897 - 42ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "23/23 - 0s - loss: 0.5401 - mae: 0.5666 - acc: 0.7230 - val_loss: 0.5072 - val_mae: 0.5488 - val_acc: 0.7085 - 46ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "23/23 - 0s - loss: 0.5224 - mae: 0.5568 - acc: 0.7262 - val_loss: 0.4934 - val_mae: 0.5399 - val_acc: 0.7085 - 47ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "23/23 - 0s - loss: 0.5101 - mae: 0.5500 - acc: 0.7277 - val_loss: 0.4841 - val_mae: 0.5377 - val_acc: 0.7116 - 47ms/epoch - 2ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi3/all_years/optimized_model_2013/assets\n" - 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69ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "16/16 - 1s - loss: 0.9923 - mae: 0.8164 - acc: 0.4201 - val_loss: 0.8470 - val_mae: 0.7482 - val_acc: 0.5237 - 681ms/epoch - 43ms/step\n", - "Epoch 2/5\n", - "16/16 - 0s - loss: 0.7487 - mae: 0.6935 - acc: 0.5874 - val_loss: 0.6991 - val_mae: 0.6604 - val_acc: 0.6088 - 39ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "16/16 - 0s - loss: 0.6550 - mae: 0.6408 - acc: 0.6317 - val_loss: 0.6469 - val_mae: 0.6309 - val_acc: 0.6388 - 41ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "16/16 - 0s - loss: 0.6100 - mae: 0.6127 - acc: 0.6478 - val_loss: 0.6084 - val_mae: 0.6064 - val_acc: 0.6451 - 41ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "16/16 - 0s - loss: 0.5847 - mae: 0.5963 - acc: 0.6613 - val_loss: 0.5848 - val_mae: 0.5922 - val_acc: 0.6562 - 43ms/epoch - 3ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi3/all_years/optimized_model_2015/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi3/all_years/optimized_model_2015/assets\n" - 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"[[0.03197]\n", - " [0.19132]\n", - " [0.19998]]\n", - "=========================================\n", - "Epoch 1/5\n", - "39/39 - 1s - loss: 1.1086 - mae: 0.8495 - acc: 0.4129 - val_loss: 0.8535 - val_mae: 0.7589 - val_acc: 0.4732 - 724ms/epoch - 19ms/step\n", - "Epoch 2/5\n", - "39/39 - 0s - loss: 0.8364 - mae: 0.7493 - acc: 0.5415 - val_loss: 0.7160 - val_mae: 0.6970 - val_acc: 0.5333 - 64ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "39/39 - 0s - loss: 0.7459 - mae: 0.7020 - acc: 0.5838 - val_loss: 0.6647 - val_mae: 0.6691 - val_acc: 0.5789 - 66ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "39/39 - 0s - loss: 0.7030 - mae: 0.6783 - acc: 0.6094 - val_loss: 0.6279 - val_mae: 0.6483 - val_acc: 0.6114 - 65ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "39/39 - 0s - loss: 0.6673 - mae: 0.6577 - acc: 0.6310 - val_loss: 0.5965 - val_mae: 0.6306 - val_acc: 0.6228 - 69ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 0.6840 - mae: 0.6660 - acc: 0.6229 - val_loss: 0.5125 - val_mae: 0.5789 - val_acc: 0.7057 - 891ms/epoch - 89ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.5297 - mae: 0.5728 - acc: 0.7221 - val_loss: 0.4776 - val_mae: 0.5525 - val_acc: 0.7285 - 40ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.5046 - mae: 0.5567 - acc: 0.7282 - val_loss: 0.4658 - val_mae: 0.5447 - val_acc: 0.7447 - 42ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.4886 - mae: 0.5452 - acc: 0.7429 - val_loss: 0.4516 - val_mae: 0.5322 - val_acc: 0.7431 - 40ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.4768 - mae: 0.5381 - acc: 0.7453 - val_loss: 0.4459 - val_mae: 0.5260 - val_acc: 0.7480 - 42ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "12/12 - 1s - loss: 0.8726 - mae: 0.7769 - acc: 0.5008 - val_loss: 0.7185 - val_mae: 0.7035 - val_acc: 0.5805 - 669ms/epoch - 56ms/step\n", - "Epoch 2/5\n", - "12/12 - 0s - loss: 0.7169 - mae: 0.6893 - acc: 0.6111 - val_loss: 0.5954 - val_mae: 0.6345 - val_acc: 0.6585 - 38ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "12/12 - 0s - loss: 0.6167 - mae: 0.6324 - acc: 0.6713 - val_loss: 0.5251 - val_mae: 0.5890 - val_acc: 0.6813 - 39ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "12/12 - 0s - loss: 0.5647 - mae: 0.5982 - acc: 0.6900 - val_loss: 0.4917 - val_mae: 0.5633 - val_acc: 0.6976 - 39ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "12/12 - 0s - loss: 0.5360 - mae: 0.5785 - acc: 0.7103 - val_loss: 0.4782 - val_mae: 0.5518 - val_acc: 0.7024 - 39ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "30/30 - 1s - loss: 0.8716 - mae: 0.7719 - acc: 0.5309 - val_loss: 0.7480 - val_mae: 0.7181 - val_acc: 0.5951 - 1s/epoch - 46ms/step\n", - "Epoch 2/5\n", - "30/30 - 0s - loss: 0.7597 - mae: 0.7102 - acc: 0.5846 - val_loss: 0.6594 - val_mae: 0.6665 - val_acc: 0.6065 - 92ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "30/30 - 0s - loss: 0.7019 - mae: 0.6725 - acc: 0.6005 - val_loss: 0.6240 - val_mae: 0.6436 - val_acc: 0.6244 - 74ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "30/30 - 0s - loss: 0.6812 - mae: 0.6615 - acc: 0.6151 - val_loss: 0.6132 - val_mae: 0.6347 - val_acc: 0.6325 - 73ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "30/30 - 0s - loss: 0.6670 - mae: 0.6515 - acc: 0.6249 - val_loss: 0.6023 - val_mae: 0.6281 - val_acc: 0.6407 - 71ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "16/16 - 2s - loss: 0.8409 - mae: 0.7489 - acc: 0.4817 - val_loss: 0.5943 - val_mae: 0.6239 - val_acc: 0.6195 - 2s/epoch - 100ms/step\n", - "Epoch 2/5\n", - "16/16 - 0s - loss: 0.6644 - mae: 0.6493 - acc: 0.6225 - val_loss: 0.5771 - val_mae: 0.6143 - val_acc: 0.6374 - 65ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "16/16 - 0s - loss: 0.6102 - mae: 0.6140 - acc: 0.6424 - val_loss: 0.5499 - val_mae: 0.5930 - val_acc: 0.6618 - 69ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "16/16 - 0s - loss: 0.5966 - mae: 0.6060 - acc: 0.6660 - val_loss: 0.5218 - val_mae: 0.5755 - val_acc: 0.7057 - 70ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "16/16 - 0s - loss: 0.5537 - mae: 0.5821 - acc: 0.7111 - val_loss: 0.5317 - val_mae: 0.5821 - val_acc: 0.7285 - 75ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "9/9 - 1s - loss: 0.8024 - mae: 0.7355 - acc: 0.5049 - val_loss: 0.6463 - val_mae: 0.6642 - val_acc: 0.6553 - 1s/epoch - 126ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.6504 - mae: 0.6548 - acc: 0.6383 - val_loss: 0.5746 - val_mae: 0.6160 - val_acc: 0.6130 - 42ms/epoch - 5ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.6035 - mae: 0.6197 - acc: 0.6412 - val_loss: 0.5620 - val_mae: 0.6064 - val_acc: 0.6520 - 43ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.5878 - mae: 0.6100 - acc: 0.6672 - val_loss: 0.5447 - val_mae: 0.5958 - val_acc: 0.6748 - 43ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.5798 - mae: 0.6078 - acc: 0.6794 - val_loss: 0.5380 - val_mae: 0.5898 - val_acc: 0.6780 - 44ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "99/99 - 1s - loss: 0.7384 - mae: 0.6952 - acc: 0.5785 - val_loss: 0.6008 - val_mae: 0.6285 - val_acc: 0.6439 - 1s/epoch - 13ms/step\n", - "Epoch 2/5\n", - "99/99 - 0s - loss: 0.6456 - mae: 0.6417 - acc: 0.6347 - val_loss: 0.5653 - val_mae: 0.6107 - val_acc: 0.6618 - 182ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "99/99 - 0s - loss: 0.6254 - mae: 0.6305 - acc: 0.6505 - val_loss: 0.5410 - val_mae: 0.5946 - val_acc: 0.6683 - 204ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "99/99 - 0s - loss: 0.6078 - mae: 0.6204 - acc: 0.6517 - val_loss: 0.5451 - val_mae: 0.5992 - val_acc: 0.6699 - 195ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "99/99 - 0s - loss: 0.6013 - mae: 0.6172 - acc: 0.6627 - val_loss: 0.5323 - val_mae: 0.5880 - val_acc: 0.6748 - 198ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "25/25 - 1s - loss: 0.7662 - mae: 0.7116 - acc: 0.5838 - val_loss: 0.5949 - val_mae: 0.6278 - val_acc: 0.6341 - 1s/epoch - 46ms/step\n", - "Epoch 2/5\n", - "25/25 - 0s - loss: 0.6181 - mae: 0.6263 - acc: 0.6591 - val_loss: 0.5331 - val_mae: 0.5850 - val_acc: 0.6829 - 68ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "25/25 - 0s - loss: 0.5700 - mae: 0.5923 - acc: 0.6937 - val_loss: 0.5153 - val_mae: 0.5746 - val_acc: 0.6992 - 76ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "25/25 - 0s - loss: 0.5435 - mae: 0.5770 - acc: 0.7124 - val_loss: 0.5059 - val_mae: 0.5671 - val_acc: 0.7041 - 74ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "25/25 - 0s - loss: 0.5258 - mae: 0.5661 - acc: 0.7160 - val_loss: 0.5016 - val_mae: 0.5659 - val_acc: 0.7122 - 73ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "39/39 - 1s - loss: 0.6179 - mae: 0.6234 - acc: 0.6635 - val_loss: 0.4927 - val_mae: 0.5616 - val_acc: 0.7171 - 835ms/epoch - 21ms/step\n", - "Epoch 2/5\n", - "39/39 - 0s - loss: 0.5203 - mae: 0.5658 - acc: 0.7124 - val_loss: 0.4781 - val_mae: 0.5492 - val_acc: 0.7333 - 69ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "39/39 - 0s - loss: 0.4960 - mae: 0.5496 - acc: 0.7376 - val_loss: 0.4605 - val_mae: 0.5377 - val_acc: 0.7398 - 70ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "39/39 - 0s - loss: 0.4842 - mae: 0.5442 - acc: 0.7376 - val_loss: 0.4661 - val_mae: 0.5400 - val_acc: 0.7333 - 68ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "39/39 - 0s - loss: 0.4777 - mae: 0.5376 - acc: 0.7445 - val_loss: 0.4590 - val_mae: 0.5339 - val_acc: 0.7431 - 72ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "9/9 - 1s - loss: 0.7498 - mae: 0.7052 - acc: 0.5033 - val_loss: 0.5718 - val_mae: 0.6138 - val_acc: 0.6569 - 779ms/epoch - 87ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.5915 - mae: 0.6108 - acc: 0.6770 - val_loss: 0.5205 - val_mae: 0.5780 - val_acc: 0.6699 - 36ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.5545 - mae: 0.5902 - acc: 0.6855 - val_loss: 0.4996 - val_mae: 0.5688 - val_acc: 0.7024 - 43ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.5354 - mae: 0.5779 - acc: 0.7144 - val_loss: 0.5013 - val_mae: 0.5681 - val_acc: 0.7154 - 39ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.5237 - mae: 0.5701 - acc: 0.7213 - val_loss: 0.4819 - val_mae: 0.5545 - val_acc: 0.7203 - 35ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "67/67 - 1s - loss: 0.7607 - mae: 0.7096 - acc: 0.6282 - val_loss: 0.5656 - val_mae: 0.6140 - val_acc: 0.6780 - 833ms/epoch - 12ms/step\n", - "Epoch 2/5\n", - "67/67 - 0s - loss: 0.5759 - mae: 0.5999 - acc: 0.6896 - val_loss: 0.5055 - val_mae: 0.5713 - val_acc: 0.7138 - 114ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "67/67 - 0s - loss: 0.5324 - mae: 0.5715 - acc: 0.7217 - val_loss: 0.4937 - val_mae: 0.5602 - val_acc: 0.7236 - 103ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "67/67 - 0s - loss: 0.5152 - mae: 0.5607 - acc: 0.7307 - val_loss: 0.4838 - val_mae: 0.5505 - val_acc: 0.7236 - 105ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "67/67 - 0s - loss: 0.5030 - mae: 0.5533 - acc: 0.7343 - val_loss: 0.4783 - val_mae: 0.5441 - val_acc: 0.7268 - 101ms/epoch - 2ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi4/all_years/optimized_model_2014/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi4/all_years/optimized_model_2014/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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val_mae: 0.6221 - val_acc: 0.6642 - 76ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "42/42 - 0s - loss: 0.6266 - mae: 0.6255 - acc: 0.6458 - val_loss: 0.5390 - val_mae: 0.5834 - val_acc: 0.6657 - 70ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "42/42 - 0s - loss: 0.5886 - mae: 0.6057 - acc: 0.6548 - val_loss: 0.5209 - val_mae: 0.5734 - val_acc: 0.6822 - 72ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "42/42 - 0s - loss: 0.5731 - mae: 0.5969 - acc: 0.6627 - val_loss: 0.5096 - val_mae: 0.5667 - val_acc: 0.6852 - 69ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "29/29 - 1s - loss: 1.6521 - mae: 1.0153 - acc: 0.2101 - val_loss: 1.0918 - val_mae: 0.8660 - val_acc: 0.2084 - 917ms/epoch - 32ms/step\n", - "Epoch 2/5\n", - "29/29 - 0s - loss: 1.0806 - mae: 0.8610 - acc: 0.1985 - val_loss: 0.9480 - val_mae: 0.8152 - val_acc: 0.2399 - 60ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "29/29 - 0s - loss: 1.0054 - mae: 0.8333 - acc: 0.2484 - val_loss: 0.9167 - val_mae: 0.8030 - val_acc: 0.2909 - 64ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "29/29 - 0s - loss: 0.9829 - mae: 0.8253 - acc: 0.3493 - val_loss: 0.9063 - val_mae: 0.7999 - val_acc: 0.3673 - 60ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "29/29 - 0s - loss: 0.9736 - mae: 0.8220 - acc: 0.3752 - val_loss: 0.9008 - val_mae: 0.7976 - val_acc: 0.3658 - 58ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 0.8737 - mae: 0.7711 - acc: 0.4750 - val_loss: 0.6879 - val_mae: 0.6792 - val_acc: 0.5937 - 817ms/epoch - 82ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.6752 - mae: 0.6618 - acc: 0.6266 - val_loss: 0.5802 - val_mae: 0.6098 - val_acc: 0.6192 - 44ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.5934 - mae: 0.6094 - acc: 0.6424 - val_loss: 0.5363 - val_mae: 0.5858 - val_acc: 0.6552 - 43ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.5574 - mae: 0.5913 - acc: 0.6619 - val_loss: 0.5085 - val_mae: 0.5705 - val_acc: 0.6762 - 44ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.5330 - mae: 0.5757 - 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val_loss: 0.5280 - val_mae: 0.5791 - val_acc: 0.6764 - 78ms/epoch - 7ms/step\n", - "Epoch 5/5\n", - "12/12 - 0s - loss: 0.5707 - mae: 0.5951 - acc: 0.6515 - val_loss: 0.5145 - val_mae: 0.5702 - val_acc: 0.6894 - 81ms/epoch - 7ms/step\n", - "Epoch 1/5\n", - "23/23 - 1s - loss: 0.6856 - mae: 0.6621 - acc: 0.6031 - val_loss: 0.5411 - val_mae: 0.5834 - val_acc: 0.6260 - 967ms/epoch - 42ms/step\n", - "Epoch 2/5\n", - "23/23 - 0s - loss: 0.5508 - mae: 0.5851 - acc: 0.6531 - val_loss: 0.4896 - val_mae: 0.5504 - val_acc: 0.6862 - 58ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "23/23 - 0s - loss: 0.5239 - mae: 0.5665 - acc: 0.6934 - val_loss: 0.4739 - val_mae: 0.5415 - val_acc: 0.7171 - 55ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "23/23 - 0s - loss: 0.5018 - mae: 0.5560 - acc: 0.7068 - val_loss: 0.4782 - val_mae: 0.5403 - val_acc: 0.7252 - 55ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "23/23 - 0s - loss: 0.4981 - mae: 0.5506 - acc: 0.7170 - val_loss: 0.4611 - val_mae: 0.5365 - val_acc: 0.7252 - 55ms/epoch - 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mae: 0.6625 - acc: 0.5848 - val_loss: 0.6208 - val_mae: 0.6308 - val_acc: 0.5837 - 140ms/epoch - 1ms/step\n", - "Epoch 3/5\n", - "95/95 - 0s - loss: 0.6480 - mae: 0.6395 - acc: 0.6002 - val_loss: 0.5922 - val_mae: 0.6190 - val_acc: 0.5984 - 144ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "95/95 - 0s - loss: 0.6270 - mae: 0.6284 - acc: 0.6104 - val_loss: 0.5822 - val_mae: 0.6134 - val_acc: 0.6033 - 141ms/epoch - 1ms/step\n", - "Epoch 5/5\n", - "95/95 - 0s - loss: 0.6179 - mae: 0.6225 - acc: 0.6190 - val_loss: 0.5773 - val_mae: 0.6104 - val_acc: 0.6065 - 154ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "12/12 - 1s - loss: 0.9246 - mae: 0.7843 - acc: 0.4262 - val_loss: 0.7779 - val_mae: 0.7241 - val_acc: 0.5220 - 1s/epoch - 100ms/step\n", - "Epoch 2/5\n", - "12/12 - 0s - loss: 0.7983 - mae: 0.7217 - acc: 0.5486 - val_loss: 0.6816 - val_mae: 0.6687 - val_acc: 0.5756 - 47ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "12/12 - 0s - loss: 0.7287 - mae: 0.6855 - acc: 0.5848 - val_loss: 0.6321 - val_mae: 0.6490 - val_acc: 0.6000 - 78ms/epoch - 6ms/step\n", - "Epoch 4/5\n", - "12/12 - 0s - loss: 0.6877 - mae: 0.6613 - acc: 0.6035 - val_loss: 0.6143 - val_mae: 0.6356 - val_acc: 0.6130 - 56ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "12/12 - 0s - loss: 0.6671 - mae: 0.6501 - acc: 0.6169 - val_loss: 0.5857 - val_mae: 0.6144 - val_acc: 0.6325 - 55ms/epoch - 5ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi4/all_years/optimized_model_2015/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi4/all_years/optimized_model_2015/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model11430846860087388993/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model11430846860087388993/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "16/16 [==============================] - 0s 1ms/step\n", - "INFO:tensorflow:Assets written to: /tmp/tmp_model5891970174023066182/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp_model5891970174023066182/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "54/54 [==============================] - 0s 690us/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi4/best_optimized_model_2015/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi4/best_optimized_model_2015/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=========================================\n", - " GPI 4 done\n", - " aprior performance(RMSE): \n", - "[[0.15324]\n", - " [0.06293]\n", - " [0.15922]]\n", - "=========================================\n", - " post performance(RMSE): \n", - "[[0.33866]\n", - " [0.16024]\n", - " [0.18208]]\n", - "=========================================\n", - "Epoch 1/5\n", - "38/38 - 1s - loss: 1.1207 - mae: 0.8430 - acc: 0.2742 - val_loss: 0.9641 - val_mae: 0.7805 - val_acc: 0.3361 - 685ms/epoch - 18ms/step\n", - "Epoch 2/5\n", - "38/38 - 0s - loss: 0.8920 - mae: 0.7524 - acc: 0.4108 - val_loss: 0.8272 - val_mae: 0.7194 - val_acc: 0.4420 - 59ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "38/38 - 0s - loss: 0.7748 - mae: 0.6983 - acc: 0.4706 - val_loss: 0.7414 - val_mae: 0.6782 - val_acc: 0.5479 - 59ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "38/38 - 0s - loss: 0.7050 - mae: 0.6620 - acc: 0.5652 - val_loss: 0.6917 - val_mae: 0.6540 - val_acc: 0.5916 - 66ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "38/38 - 0s - loss: 0.6659 - mae: 0.6413 - acc: 0.5946 - val_loss: 0.6677 - val_mae: 0.6428 - val_acc: 0.6017 - 61ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "30/30 - 1s - loss: 0.7208 - mae: 0.6651 - acc: 0.5917 - val_loss: 0.6583 - val_mae: 0.6360 - val_acc: 0.6437 - 1s/epoch - 42ms/step\n", - "Epoch 2/5\n", - "30/30 - 0s - loss: 0.6479 - mae: 0.6264 - acc: 0.6194 - val_loss: 0.6439 - val_mae: 0.6203 - val_acc: 0.6504 - 81ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "30/30 - 0s - loss: 0.6264 - mae: 0.6112 - acc: 0.6304 - val_loss: 0.6218 - val_mae: 0.6170 - val_acc: 0.6790 - 87ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "30/30 - 0s - loss: 0.6090 - mae: 0.6063 - acc: 0.6497 - val_loss: 0.6118 - val_mae: 0.6108 - val_acc: 0.6908 - 83ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "30/30 - 0s - loss: 0.5961 - mae: 0.5989 - acc: 0.6674 - val_loss: 0.5964 - val_mae: 0.5989 - val_acc: 0.6924 - 86ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "159/159 - 2s - loss: 0.6858 - mae: 0.6483 - acc: 0.6220 - val_loss: 0.6335 - val_mae: 0.6183 - val_acc: 0.6773 - 2s/epoch - 12ms/step\n", - "Epoch 2/5\n", - "159/159 - 0s - loss: 0.6214 - mae: 0.6150 - acc: 0.6678 - val_loss: 0.6179 - val_mae: 0.6107 - val_acc: 0.6891 - 244ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "159/159 - 0s - loss: 0.5827 - mae: 0.5930 - acc: 0.6846 - val_loss: 0.5963 - val_mae: 0.6056 - val_acc: 0.6403 - 246ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "159/159 - 0s - loss: 0.5477 - mae: 0.5752 - acc: 0.6997 - val_loss: 0.5374 - val_mae: 0.5639 - val_acc: 0.7244 - 242ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "159/159 - 0s - loss: 0.5273 - mae: 0.5626 - acc: 0.7098 - val_loss: 0.5602 - val_mae: 0.5831 - val_acc: 0.7176 - 256ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "15/15 - 2s - loss: 0.9388 - mae: 0.7892 - acc: 0.4773 - val_loss: 0.8675 - val_mae: 0.7491 - val_acc: 0.5042 - 2s/epoch - 101ms/step\n", - "Epoch 2/5\n", - "15/15 - 0s - loss: 0.8262 - mae: 0.7246 - acc: 0.5135 - val_loss: 0.8037 - val_mae: 0.7166 - val_acc: 0.5109 - 52ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "15/15 - 0s - loss: 0.7562 - mae: 0.6899 - acc: 0.5980 - val_loss: 0.7501 - val_mae: 0.6838 - val_acc: 0.6336 - 49ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "15/15 - 0s - loss: 0.7119 - mae: 0.6660 - acc: 0.6140 - val_loss: 0.7282 - val_mae: 0.6698 - val_acc: 0.6185 - 57ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "15/15 - 0s - loss: 0.7015 - mae: 0.6575 - acc: 0.6165 - val_loss: 0.7130 - val_mae: 0.6610 - val_acc: 0.6303 - 50ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "8/8 - 2s - loss: 0.9447 - mae: 0.7947 - acc: 0.3999 - val_loss: 0.8525 - val_mae: 0.7371 - val_acc: 0.4353 - 2s/epoch - 195ms/step\n", - "Epoch 2/5\n", - "8/8 - 0s - loss: 0.7460 - mae: 0.6824 - acc: 0.5084 - val_loss: 0.6895 - val_mae: 0.6464 - val_acc: 0.6487 - 35ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "8/8 - 0s - loss: 0.6661 - mae: 0.6345 - acc: 0.6177 - val_loss: 0.6670 - val_mae: 0.6305 - val_acc: 0.6487 - 42ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "8/8 - 0s - loss: 0.6449 - mae: 0.6203 - acc: 0.6203 - val_loss: 0.6455 - val_mae: 0.6184 - val_acc: 0.6454 - 36ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "8/8 - 0s - loss: 0.6326 - mae: 0.6139 - acc: 0.6207 - val_loss: 0.6415 - val_mae: 0.6187 - val_acc: 0.6487 - 37ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 0.7345 - mae: 0.6713 - acc: 0.5807 - val_loss: 0.6659 - val_mae: 0.6377 - val_acc: 0.6403 - 895ms/epoch - 89ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.6450 - mae: 0.6209 - acc: 0.6274 - val_loss: 0.6522 - val_mae: 0.6288 - val_acc: 0.6588 - 40ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.6289 - mae: 0.6128 - acc: 0.6489 - val_loss: 0.6305 - val_mae: 0.6163 - val_acc: 0.6622 - 42ms/epoch - 4ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.6186 - mae: 0.6049 - acc: 0.6489 - val_loss: 0.6178 - val_mae: 0.6091 - val_acc: 0.6756 - 44ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.6086 - mae: 0.6011 - acc: 0.6573 - val_loss: 0.6089 - val_mae: 0.6045 - val_acc: 0.6992 - 41ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "10/10 - 1s - loss: 0.8395 - mae: 0.7338 - acc: 0.5946 - val_loss: 0.8088 - val_mae: 0.7177 - val_acc: 0.6387 - 696ms/epoch - 70ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.7799 - mae: 0.7037 - acc: 0.6371 - val_loss: 0.7568 - val_mae: 0.6910 - val_acc: 0.6790 - 33ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.7297 - mae: 0.6799 - acc: 0.6505 - val_loss: 0.7141 - val_mae: 0.6696 - val_acc: 0.6790 - 34ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.6894 - mae: 0.6587 - acc: 0.6497 - val_loss: 0.6804 - val_mae: 0.6503 - val_acc: 0.6857 - 50ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.6594 - mae: 0.6417 - acc: 0.6548 - val_loss: 0.6534 - val_mae: 0.6355 - val_acc: 0.6941 - 36ms/epoch - 4ms/step\n", - "Epoch 1/5\n", - "14/14 - 1s - loss: 0.8868 - mae: 0.7617 - acc: 0.5597 - val_loss: 0.8168 - val_mae: 0.7282 - val_acc: 0.6387 - 783ms/epoch - 56ms/step\n", - "Epoch 2/5\n", - "14/14 - 0s - loss: 0.7574 - mae: 0.6944 - acc: 0.6148 - val_loss: 0.7167 - val_mae: 0.6684 - val_acc: 0.6588 - 37ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "14/14 - 0s - loss: 0.6811 - mae: 0.6448 - acc: 0.6190 - val_loss: 0.6675 - val_mae: 0.6344 - val_acc: 0.6639 - 41ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "14/14 - 0s - loss: 0.6589 - mae: 0.6300 - acc: 0.6190 - val_loss: 0.6595 - val_mae: 0.6314 - val_acc: 0.6622 - 55ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "14/14 - 0s - loss: 0.6524 - mae: 0.6268 - acc: 0.6198 - val_loss: 0.6528 - val_mae: 0.6281 - val_acc: 0.6723 - 47ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "24/24 - 2s - loss: 0.8418 - mae: 0.7324 - acc: 0.5786 - val_loss: 0.7580 - val_mae: 0.6817 - val_acc: 0.6387 - 2s/epoch - 66ms/step\n", - "Epoch 2/5\n", - "24/24 - 0s - loss: 0.6989 - mae: 0.6537 - acc: 0.6224 - val_loss: 0.6860 - val_mae: 0.6451 - val_acc: 0.6639 - 60ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "24/24 - 0s - loss: 0.6470 - mae: 0.6270 - acc: 0.6602 - val_loss: 0.6385 - val_mae: 0.6229 - val_acc: 0.6773 - 61ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "24/24 - 0s - loss: 0.6184 - mae: 0.6111 - acc: 0.6606 - val_loss: 0.6086 - val_mae: 0.6040 - val_acc: 0.7025 - 63ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "24/24 - 0s - loss: 0.5917 - mae: 0.5962 - acc: 0.6674 - val_loss: 0.6033 - val_mae: 0.6028 - val_acc: 0.6773 - 58ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "238/238 - 2s - loss: 0.7253 - mae: 0.6777 - acc: 0.5971 - val_loss: 0.6941 - val_mae: 0.6526 - val_acc: 0.6218 - 2s/epoch - 10ms/step\n", - "Epoch 2/5\n", - "238/238 - 1s - loss: 0.6783 - mae: 0.6475 - acc: 0.6177 - val_loss: 0.6634 - val_mae: 0.6331 - val_acc: 0.6303 - 535ms/epoch - 2ms/step\n", - "Epoch 3/5\n", - "238/238 - 0s - loss: 0.6679 - mae: 0.6396 - acc: 0.6161 - val_loss: 0.6783 - val_mae: 0.6422 - val_acc: 0.6387 - 458ms/epoch - 2ms/step\n", - "Epoch 4/5\n", - "238/238 - 0s - loss: 0.6580 - mae: 0.6339 - acc: 0.6207 - val_loss: 0.6827 - val_mae: 0.6468 - val_acc: 0.6487 - 426ms/epoch - 2ms/step\n", - "Epoch 5/5\n", - "238/238 - 0s - loss: 0.6548 - mae: 0.6314 - acc: 0.6236 - val_loss: 0.6678 - val_mae: 0.6364 - val_acc: 0.6269 - 433ms/epoch - 2ms/step\n", - "Epoch 1/5\n", - "17/17 - 2s - loss: 0.9372 - mae: 0.7887 - acc: 0.4655 - val_loss: 0.8929 - val_mae: 0.7660 - val_acc: 0.5782 - 2s/epoch - 94ms/step\n", - "Epoch 2/5\n", - "17/17 - 0s - loss: 0.8573 - mae: 0.7436 - acc: 0.5421 - val_loss: 0.8481 - val_mae: 0.7399 - val_acc: 0.5815 - 50ms/epoch - 3ms/step\n", - "Epoch 3/5\n", - "17/17 - 0s - loss: 0.8210 - mae: 0.7259 - acc: 0.5736 - val_loss: 0.8131 - val_mae: 0.7230 - val_acc: 0.5950 - 49ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "17/17 - 0s - loss: 0.7823 - mae: 0.7094 - acc: 0.5744 - val_loss: 0.7853 - val_mae: 0.7060 - val_acc: 0.5966 - 52ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "17/17 - 0s - loss: 0.7639 - mae: 0.6997 - acc: 0.5770 - val_loss: 0.7771 - val_mae: 0.7010 - val_acc: 0.5950 - 55ms/epoch - 3ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi5/all_years/optimized_model_2014/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi5/all_years/optimized_model_2014/assets\n" - ] - 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acc: 0.5937 - val_loss: 0.6857 - val_mae: 0.6544 - val_acc: 0.6492 - 2s/epoch - 173ms/step\n", - "Epoch 2/5\n", - "12/12 - 0s - loss: 0.6287 - mae: 0.6211 - acc: 0.6422 - val_loss: 0.6098 - val_mae: 0.6138 - val_acc: 0.6627 - 73ms/epoch - 6ms/step\n", - "Epoch 3/5\n", - "12/12 - 0s - loss: 0.5908 - mae: 0.6000 - acc: 0.6620 - val_loss: 0.5972 - val_mae: 0.6050 - val_acc: 0.6847 - 75ms/epoch - 6ms/step\n", - "Epoch 4/5\n", - "12/12 - 0s - loss: 0.5755 - mae: 0.5920 - acc: 0.6730 - val_loss: 0.5827 - val_mae: 0.5947 - val_acc: 0.6914 - 80ms/epoch - 7ms/step\n", - "Epoch 5/5\n", - "12/12 - 0s - loss: 0.5692 - mae: 0.5883 - acc: 0.6726 - val_loss: 0.5871 - val_mae: 0.6001 - val_acc: 0.6897 - 72ms/epoch - 6ms/step\n", - "Epoch 1/5\n", - "13/13 - 1s - loss: 0.8678 - mae: 0.7409 - acc: 0.5823 - val_loss: 0.6895 - val_mae: 0.6446 - val_acc: 0.7049 - 1s/epoch - 107ms/step\n", - "Epoch 2/5\n", - "13/13 - 0s - loss: 0.6228 - mae: 0.6098 - acc: 0.6751 - val_loss: 0.5910 - val_mae: 0.5909 - val_acc: 0.7116 - 62ms/epoch - 5ms/step\n", - "Epoch 3/5\n", - "13/13 - 0s - loss: 0.5646 - mae: 0.5795 - acc: 0.6857 - val_loss: 0.5721 - val_mae: 0.5849 - val_acc: 0.7083 - 62ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "13/13 - 0s - loss: 0.5385 - mae: 0.5672 - acc: 0.6966 - val_loss: 0.5570 - val_mae: 0.5723 - val_acc: 0.7150 - 55ms/epoch - 4ms/step\n", - "Epoch 5/5\n", - "13/13 - 0s - loss: 0.5301 - mae: 0.5610 - acc: 0.6966 - val_loss: 0.5426 - val_mae: 0.5687 - val_acc: 0.7234 - 63ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "9/9 - 1s - loss: 0.9272 - mae: 0.7825 - acc: 0.5730 - val_loss: 0.8023 - val_mae: 0.7182 - val_acc: 0.6560 - 1s/epoch - 147ms/step\n", - "Epoch 2/5\n", - "9/9 - 0s - loss: 0.7031 - mae: 0.6583 - acc: 0.6414 - val_loss: 0.7034 - val_mae: 0.6635 - val_acc: 0.6728 - 45ms/epoch - 5ms/step\n", - "Epoch 3/5\n", - "9/9 - 0s - loss: 0.6460 - mae: 0.6261 - acc: 0.6367 - val_loss: 0.6571 - val_mae: 0.6346 - val_acc: 0.6627 - 47ms/epoch - 5ms/step\n", - "Epoch 4/5\n", - "9/9 - 0s - loss: 0.6299 - mae: 0.6175 - acc: 0.6591 - val_loss: 0.6561 - val_mae: 0.6344 - val_acc: 0.6678 - 45ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "9/9 - 0s - loss: 0.6153 - mae: 0.6087 - acc: 0.6624 - val_loss: 0.6349 - val_mae: 0.6235 - val_acc: 0.6948 - 49ms/epoch - 5ms/step\n", - "Epoch 1/5\n", - "18/18 - 1s - loss: 0.9981 - mae: 0.8167 - acc: 0.3354 - val_loss: 0.9768 - val_mae: 0.8105 - val_acc: 0.3676 - 1s/epoch - 70ms/step\n", - "Epoch 2/5\n", - "18/18 - 0s - loss: 0.9650 - mae: 0.8046 - acc: 0.3426 - val_loss: 0.9317 - val_mae: 0.7920 - val_acc: 0.3676 - 65ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "18/18 - 0s - loss: 0.8953 - mae: 0.7767 - acc: 0.3477 - val_loss: 0.8411 - val_mae: 0.7478 - val_acc: 0.5953 - 62ms/epoch - 3ms/step\n", - "Epoch 4/5\n", - "18/18 - 0s - loss: 0.8018 - mae: 0.7300 - acc: 0.5916 - val_loss: 0.7750 - val_mae: 0.7047 - val_acc: 0.6509 - 57ms/epoch - 3ms/step\n", - "Epoch 5/5\n", - "18/18 - 0s - loss: 0.7413 - mae: 0.6899 - acc: 0.6241 - val_loss: 0.7144 - val_mae: 0.6694 - val_acc: 0.6509 - 53ms/epoch - 3ms/step\n", - "Epoch 1/5\n", - "10/10 - 2s - loss: 0.9973 - mae: 0.8139 - acc: 0.5114 - val_loss: 0.9500 - val_mae: 0.7935 - val_acc: 0.5801 - 2s/epoch - 185ms/step\n", - "Epoch 2/5\n", - "10/10 - 0s - loss: 0.9085 - mae: 0.7626 - acc: 0.5793 - val_loss: 0.8616 - val_mae: 0.7413 - val_acc: 0.6037 - 44ms/epoch - 4ms/step\n", - "Epoch 3/5\n", - "10/10 - 0s - loss: 0.8383 - mae: 0.7255 - acc: 0.5840 - val_loss: 0.7959 - val_mae: 0.7069 - val_acc: 0.6121 - 57ms/epoch - 6ms/step\n", - "Epoch 4/5\n", - "10/10 - 0s - loss: 0.7666 - mae: 0.6905 - acc: 0.5941 - val_loss: 0.7393 - val_mae: 0.6754 - val_acc: 0.6138 - 46ms/epoch - 5ms/step\n", - "Epoch 5/5\n", - "10/10 - 0s - loss: 0.7199 - mae: 0.6664 - acc: 0.6139 - val_loss: 0.7447 - val_mae: 0.6798 - val_acc: 0.6256 - 52ms/epoch - 5ms/step\n", - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi5/all_years/optimized_model_2015/assets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - 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] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:tensorflow:Assets written to: ./dnn_examples/results/gpi5/best_optimized_model_2015/assets\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=========================================\n", - " GPI 5 done\n", - " aprior performance(RMSE): \n", - "[[0.24258]\n", - " [0.09105]\n", - " [0.27377]]\n", - "=========================================\n", - " post performance(RMSE): \n", - "[[0.06875]\n", - " [0.13189]\n", - " [0.30721]]\n", - "=========================================\n", - "Training is finished! Shutdown the cluster to release resources.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-05 14:52:28,929 - distributed.client - ERROR - Failed to reconnect to scheduler after 30.00 seconds, closing client\n" + "GPI 1\n", + " aprior performance(RMSE):\n", + "[[0.26438]\n", + " [0.03295]\n", + " [0.14362]]\n", + "post performance(RMSE):\n", + "[[0.34483]\n", + " [0.00631]\n", + " [0.00686]]\n", + "=========================================\n", + "GPI 2\n", + " aprior performance(RMSE):\n", + "[[0.37801]\n", + " [0.02598]\n", + " [0.26245]]\n", + "post performance(RMSE):\n", + "[[0.70249]\n", + " [0.22075]\n", + " [0.24423]]\n", + "=========================================\n", + "GPI 3\n", + " aprior performance(RMSE):\n", + "[[0.31875]\n", + " [0.24323]\n", + " [0.05353]]\n", + "post performance(RMSE):\n", + "[[0.03498]\n", + " [0.19958]\n", + " [0.24324]]\n", + "=========================================\n", + "GPI 4\n", + " aprior performance(RMSE):\n", + "[[0.19431]\n", + " [0.10026]\n", + " [0.16398]]\n", + "post performance(RMSE):\n", + "[[0.20526]\n", + " [0.02813]\n", + " [0.21003]]\n", + "=========================================\n", + "GPI 5\n", + " aprior performance(RMSE):\n", + "[[0.23724]\n", + " [0.1104 ]\n", + " [0.28052]]\n", + "post performance(RMSE):\n", + "[[0.10751]\n", + " [0.08874]\n", + " [0.26091]]\n", + "=========================================\n" ] } ], "source": [ - "aprior, post = [], []\n", - "# Loop all gpi\n", - "for gpi_num, df in enumerate(train_bags):\n", - " \n", - " # remove NA data\n", - " gpi_data = df.compute()\n", - " gpi_data.dropna(inplace=True)\n", - "\n", - " # add time to index\n", - " gpi_data.set_index(\"time\", inplace=True, drop=True)\n", - "\n", - " gpi = JackknifeGPI(gpi_data,\n", - " val_split_year,\n", - " input_list,\n", - " output_list,\n", - " outpath=f'{out_path}/gpi{gpi_num+1}')\n", - "\n", - " gpi.train(searching_space=searching_space,\n", - " optimize_space=optimize_space,\n", - " normalize_method='standard',\n", - " training_method='dnn',\n", - " performance_method='rmse',\n", - " verbose=2)\n", - "\n", - " gpi.export_best()\n", - "\n", - " # Export apriori performance\n", - " path_apriori_performance = f'{gpi.outpath}/apriori_performance_{gpi.best_year}'\n", - " with open(path_apriori_performance, 'wb') as f:\n", - " pickle.dump(gpi.apr_perf, f)\n", - "\n", - " # Export postpriori performance\n", - " path_postpriori_performance = f'{gpi.outpath}/postpriori_performance_{gpi.best_year}'\n", - " with open(path_postpriori_performance, 'wb') as f:\n", - " pickle.dump(gpi.post_perf, f)\n", - "\n", - " aprior.append(gpi.apr_perf)\n", - " post.append(gpi.post_perf)\n", - " print(\"=========================================\")\n", - " print(f\" GPI {gpi_num + 1} done\")\n", - " print(\" aprior performance(RMSE): \")\n", - " print(gpi.apr_perf)\n", - " print(\"=========================================\")\n", - " print(\" post performance(RMSE): \")\n", - " print(gpi.post_perf)\n", - " print(\"=========================================\")\n", - "print(\"Training is finished! Shutdown the cluster to release resources.\")" + "# print the results\n", + "for gpi_num, performance in enumerate(results):\n", + " print(f\"GPI {(gpi_num + 1)}\")\n", + " print(\" aprior performance(RMSE):\")\n", + " print(performance[0])\n", + " print(\"post performance(RMSE):\")\n", + " print(performance[1])\n", + " print(\"=========================================\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "e2144daf-8f9a-45b2-bbf3-88e207c76922", + "id": "7d90eec7-119b-4ab1-ba64-764babf5f453", "metadata": {}, "outputs": [], "source": []