{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# we will study the hyperparamter tuning of fully connected scatternet\n",
"\n",
"# first find optimal number of layers and neuron numbers\n",
"# second optimize the batch size and number of epochs for the best learned architecture\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading the dataset here"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-04T17:46:08.075289Z",
"start_time": "2018-09-04T17:46:07.653719Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n",
"/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n",
"/home/hegder/anaconda3/envs/deep/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset has been loaded\n",
"x-train (44999, 8)\n",
"x-test (55001, 8)\n",
"y-train (44999, 256)\n",
"y-test (55001, 256)\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"import h5py\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"#now load this dataset \n",
"h5f = h5py.File('./datasets/s8_sio2tio2_v2.h5','r')\n",
"X = h5f['sizes'][:]\n",
"Y = h5f['spectrum'][:]\n",
"\n",
"#get the ranges of the loaded data\n",
"num_layers = X.shape[1]\n",
"num_lpoints = Y.shape[1]\n",
"size_max = np.amax(X)\n",
"size_min = np.amin(X)\n",
"size_av = 0.5*(size_max + size_min)\n",
"\n",
"#this information is not given in the dataset\n",
"lam_min = 300\n",
"lam_max = 1200\n",
"lams = np.linspace(lam_min, lam_max, num_lpoints)\n",
"\n",
"#create a train - test split of the dataset\n",
"x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.55, random_state=42)\n",
"\n",
"# normalize inputs \n",
"x_train = (x_train - 50)/20 \n",
"x_test = (x_test - 50)/20 \n",
"\n",
"print(\"Dataset has been loaded\")\n",
"print(\"x-train\", x_train.shape)\n",
"print(\"x-test \", x_test.shape)\n",
"print(\"y-train\", y_train.shape)\n",
"print(\"y-test \", y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### create models here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-04T08:41:45.424541Z",
"start_time": "2018-09-04T08:41:44.270792Z"
}
},
"outputs": [],
"source": [
"import scnets as scn\n",
"from IPython.display import SVG\n",
"from keras.utils.vis_utils import model_to_dot\n",
"\n",
"#define and visualize the model here\n",
"#model = scn.fullycon(num_layers, num_lpoints, 4, 500, 2)\n",
"\n",
"model = scn.convprel(in_size=8, \n",
" out_size=256,\n",
" c1_nf=64,\n",
" clayers=3,\n",
" ker_size=3)\n",
"model.summary()\n",
"#SVG(model_to_dot(model).create(prog='dot', format='svg'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-04T08:53:28.967090Z",
"start_time": "2018-09-04T08:42:58.990636Z"
}
},
"outputs": [],
"source": [
"x_t, x_v, y_t, y_v = train_test_split(x_train, y_train, test_size=0.2, random_state=42)\n",
"history = model.fit(x_t, y_t,\n",
" batch_size=64,\n",
" epochs=250, \n",
" verbose=1,\n",
" validation_data=(x_v, y_v))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-04T09:13:20.325358Z",
"start_time": "2018-09-04T09:13:20.044281Z"
}
},
"outputs": [],
"source": [
"scn.plot_training_history(history, 64*2.56)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-04T17:16:38.203Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
]
}
],
"source": [
"\n",
"\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"from sklearn.metrics import fbeta_score, make_scorer\n",
"\n",
"def my_custom_score_func(ground_truth, predictions):\n",
" diff = np.abs(ground_truth - predictions)/np.abs(ground_truth)\n",
" return np.mean(diff)\n",
"\n",
"\n",
"\n",
"from sklearn.model_selection import GridSearchCV\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.wrappers.scikit_learn import KerasRegressor\n",
"import scnets as scn\n",
"#model = KerasClassifier(build_fn=scn.fullycon, in_size=8, out_size=250, N_gpus=1, epochs=500, verbose=0)\n",
"\n",
"model = KerasRegressor(build_fn=scn.conv1dmodel, \n",
" in_size=8, \n",
" out_size=256, \n",
" c1_nf=64,\n",
" clayers=3,\n",
" ker_size=3,\n",
" epochs=250, \n",
" verbose=0)\n",
"my_score = make_scorer(my_custom_score_func, greater_is_better=False)\n",
"\n",
"\n",
"\n",
"param_grid = dict(ker_size=[3, 5],\n",
" clayers=[3,4,5],\n",
" batch_size=[32,64]) \n",
"grid = GridSearchCV(estimator=model, \n",
" param_grid=param_grid, \n",
" n_jobs=1, \n",
" scoring='explained_variance',\n",
" verbose=1)\n",
"grid_result = grid.fit(x_train, y_train)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-04T14:25:50.558503Z",
"start_time": "2018-09-04T14:25:50.539227Z"
}
},
"outputs": [],
"source": [
"grid_result.cv_results_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-04T14:26:16.796674Z",
"start_time": "2018-09-04T14:26:16.790511Z"
}
},
"outputs": [],
"source": [
"grid_result.best_params_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-09-04T15:01:18.552044Z",
"start_time": "2018-09-04T15:01:18.538960Z"
}
},
"outputs": [],
"source": [
"bestidx = np.argsort(grid_result.cv_results_['mean_test_score'])\n",
"print(idx)\n",
"print(np.flip(idx))\n",
"parlist = grid_result.cv_results_['params']\n",
"bestlist = [parlist[indx] for indx in bestidx]\n",
"bestlist\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'mean_fit_time': array([ 440.59602499, 481.73871398, 477.10135643, 504.1547633 ,\n",
" 488.27373465, 564.76856009, 571.80046884, 569.00620119,\n",
" 650.78964178, 740.00103672, 597.98015889, 659.03726633,\n",
" 476.14166268, 508.75317804, 505.10563159, 556.87875859,\n",
" 556.58007542, 542.48226706, 591.323385 , 616.57070398,\n",
" 658.64879243, 891.65342418, 1055.23733377, 993.75831501,\n",
" 647.80959813, 629.8490928 , 516.74331371, 787.65950712,\n",
" 635.2311732 , 545.72071338, 904.7078863 , 685.55618747,\n",
" 530.5135781 , 815.14902997, 821.7310555 , 685.34639025,\n",
" 240.90511258, 246.83409182, 259.09067996, 262.85167106,\n",
" 228.2099692 , 392.25437156, 374.78024157, 452.21288816,\n",
" 459.90044618, 495.74129097, 501.12204981, 368.91637723,\n",
" 240.11106253, 237.93171946, 261.74841015, 265.67413298,\n",
" 274.72749496, 378.51081745, 436.21683431, 241.44164371,\n",
" 274.60629002, 330.47249166, 394.98302094, 318.71901139,\n",
" 237.12692181, 248.02535923, 254.61501988, 243.65626915,\n",
" 298.34629599, 273.75161084, 318.28928526, 312.33878072,\n",
" 309.61013468, 335.33733678, 316.16733519, 284.3205506 ]),\n",
"\n",
" 'params': [{'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 1, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 2, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 3, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 32, 'clayers': 4, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 1, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 2, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 3, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 64, 'clayers': 4, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 1, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 2, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 3, 'ker_size': 7},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 3},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 5},\n",
" {'batch_size': 32, 'c1_nf': 96, 'clayers': 4, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 1, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 2, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 3, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 32, 'clayers': 4, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 1, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 2, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 3, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 64, 'clayers': 4, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 1, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 2, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 3, 'ker_size': 7},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 3},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 5},\n",
" {'batch_size': 64, 'c1_nf': 96, 'clayers': 4, 'ker_size': 7}],\n",
"\n",
"\n",
" 'mean_test_score': array([0.00235561, 0.00175559, 0.00191115, 0.00255561, 0.00228894,\n",
" 0.00233339, 0.00240005, 0.00260006, 0.00262228, 0.00222227,\n",
" 0.00222227, 0.00235561, 0.00191115, 0.0021556 , 0.00242228,\n",
" 0.00217783, 0.00217783, 0.00233339, 0.00231116, 0.00202227,\n",
" 0.00206671, 0.00251117, 0.00237783, 0.00253339, 0.00197782,\n",
" 0.00222227, 0.00220005, 0.0019556 , 0.00208894, 0.00240005,\n",
" 0.00220005, 0.00204449, 0.00222227, 0.00228894, 0.00231116,\n",
" 0.00246672, 0.00200004, 0.00182226, 0.00164448, 0.00197782,\n",
" 0.0026445 , 0.00208894, 0.00222227, 0.00248894, 0.00217783,\n",
" 0.00208894, 0.00253339, 0.0024445 , 0.00204449, 0.00188893,\n",
" 0.00197782, 0.00233339, 0.00231116, 0.00231116, 0.00233339,\n",
" 0.0021556 , 0.00231116, 0.0021556 , 0.00220005, 0.00233339,\n",
" 0.0021556 , 0.00220005, 0.00251117, 0.00191115, 0.00197782,\n",
" 0.0021556 , 0.00224449, 0.00231116, 0.00228894, 0.00220005,\n",
" 0.00235561, 0.00211116]),\n",
" 'std_test_score': array([5.45313915e-04, 1.36982222e-04, 5.71830319e-04, 3.09583745e-04,\n",
" 4.63034070e-04, 1.88601629e-04, 3.57010463e-04, 2.49388000e-04,\n",
" 2.06134688e-04, 4.75566024e-04, 1.74937538e-04, 4.01290372e-04,\n",
" 2.06146739e-04, 5.14564491e-04, 3.28118192e-04, 3.09506255e-04,\n",
" 3.45776617e-04, 5.65777616e-04, 6.28186970e-05, 2.45451156e-04,\n",
" 3.03064138e-04, 4.69393085e-04, 3.70577110e-04, 2.37355340e-04,\n",
" 5.14534288e-04, 2.99865023e-04, 1.08929121e-04, 3.28165699e-04,\n",
" 1.91227745e-04, 1.44095217e-04, 1.96333991e-04, 1.13377551e-04,\n",
" 6.28899342e-05, 4.93994775e-04, 5.05855189e-04, 4.25227642e-04,\n",
" 5.25013167e-04, 1.66308696e-04, 1.91169192e-04, 7.89511257e-04,\n",
" 3.09609747e-04, 2.20000709e-04, 1.36980546e-04, 2.99872491e-04,\n",
" 2.06155046e-04, 3.70481852e-04, 1.88604774e-04, 2.74052629e-04,\n",
" 4.15813294e-04, 2.99823569e-04, 2.06148816e-04, 1.44067451e-04,\n",
" 3.50039135e-04, 2.79388549e-04, 3.81084580e-04, 3.62491814e-04,\n",
" 2.26678399e-04, 4.08631950e-04, 4.35536821e-04, 2.49505205e-04,\n",
" 4.53303035e-04, 1.44064707e-04, 5.77012935e-04, 8.31603039e-05,\n",
" 5.34338425e-04, 2.79402571e-04, 2.45459743e-04, 1.36996891e-04,\n",
" 3.86260180e-04, 1.88498951e-04, 3.09579276e-04, 3.70571422e-04]),\n",
" 'rank_test_score': array([16, 71, 66, 4, 30, 19, 13, 3, 2, 34, 34, 16, 66, 47, 12, 44, 44,\n",
" 19, 24, 59, 56, 7, 15, 5, 61, 34, 39, 65, 53, 13, 39, 57, 34, 30,\n",
" 24, 10, 60, 70, 72, 61, 1, 53, 34, 9, 44, 53, 5, 11, 57, 69, 61,\n",
" 19, 24, 24, 19, 47, 24, 47, 39, 19, 47, 39, 7, 66, 61, 47, 33, 24,\n",
" 30, 39, 16, 52], dtype=int32),\n",
"\n",
"\n",
" 'mean_train_score': array([0.00231116, 0.00187782, 0.0018556 , 0.00231116, 0.00222227,\n",
" 0.00217783, 0.00228894, 0.00236672, 0.00232227, 0.00252228,\n",
" 0.00213338, 0.00241117, 0.00214449, 0.00204449, 0.00235561,\n",
" 0.00205561, 0.00217783, 0.00238894, 0.00213338, 0.00230005,\n",
" 0.0021556 , 0.0023445 , 0.00238894, 0.00236672, 0.00203338,\n",
" 0.00208894, 0.00232228, 0.00203338, 0.00258895, 0.00216671,\n",
" 0.00221117, 0.00217783, 0.0022556 , 0.00224449, 0.00246672,\n",
" 0.00240006, 0.00187782, 0.00183338, 0.00153337, 0.00223338,\n",
" 0.00186671, 0.00204449, 0.00218894, 0.00227783, 0.00203338,\n",
" 0.00257784, 0.00235561, 0.0023445 , 0.00200004, 0.00210005,\n",
" 0.00204449, 0.00247784, 0.00233338, 0.00230005, 0.00223338,\n",
" 0.00222227, 0.00228894, 0.0023445 , 0.00228894, 0.00248895,\n",
" 0.00188893, 0.00211116, 0.00233339, 0.00236672, 0.00213338,\n",
" 0.00204449, 0.00248895, 0.00238895, 0.00233338, 0.00231116,\n",
" 0.0023445 , 0.00241116]),\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"{'mean_fit_time': array([ 893.33654523, 898.53078222, 1119.14394736, 1130.1775128 ,\n",
" 956.51246222, 964.45365715, 1209.3984166 , 1582.91039157,\n",
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" 6.97112622, 18.87349827, 223.00445851, 57.7875855 ,\n",
" 100.70476936, 43.95356933, 134.83849082, 11.29690679,\n",
" 5.42330543, 6.19267952, 4.92641743]),\n",
" 'params': [{'N_hidden': 1, 'N_neurons': 250},\n",
" {'N_hidden': 1, 'N_neurons': 500},\n",
" {'N_hidden': 1, 'N_neurons': 1000},\n",
" {'N_hidden': 2, 'N_neurons': 250},\n",
" {'N_hidden': 2, 'N_neurons': 500},\n",
" {'N_hidden': 2, 'N_neurons': 1000},\n",
" {'N_hidden': 3, 'N_neurons': 250},\n",
" {'N_hidden': 3, 'N_neurons': 500},\n",
" {'N_hidden': 3, 'N_neurons': 1000},\n",
" {'N_hidden': 4, 'N_neurons': 250},\n",
" {'N_hidden': 4, 'N_neurons': 500},\n",
" {'N_hidden': 4, 'N_neurons': 1000},\n",
" {'N_hidden': 5, 'N_neurons': 250},\n",
" {'N_hidden': 5, 'N_neurons': 500},\n",
" {'N_hidden': 5, 'N_neurons': 1000}],\n",
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" 0.0023 , 0.00255, 0.00255, 0.0024 , 0.0027 , 0.0021 , 0.00225,\n",
" 0.0022 ]),\n",
" 'split1_test_score': array([0.0024 , 0.00225, 0.0022 , 0.0022 , 0.00235, 0.0022 , 0.0021 ,\n",
" 0.00195, 0.00215, 0.0022 , 0.00185, 0.00195, 0.002 , 0.00195,\n",
" 0.0021 ]),\n",
" 'split2_test_score': array([0.00255, 0.00315, 0.00275, 0.00295, 0.00355, 0.0032 , 0.00275,\n",
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" 0.00305]),\n",
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" 0.00243333, 0.00273333, 0.00228333, 0.00235 , 0.00245 ]),\n",
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" 2.67706307e-04, 3.48807492e-04, 4.90464632e-04, 6.53622385e-04,\n",
" 3.32498956e-04, 3.74165739e-04, 4.26223728e-04]),\n",
" 'rank_test_score': array([11, 4, 8, 5, 1, 6, 15, 9, 6, 3, 11, 2, 14, 13, 9],\n",
" dtype=int32),\n",
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" 0.002125, 0.002625, 0.0025 , 0.002775, 0.002625, 0.0027 ,\n",
" 0.0023 , 0.00245 , 0.002275]),\n",
" 'split1_train_score': array([0.002975, 0.0025 , 0.00255 , 0.0028 , 0.0029 , 0.002475,\n",
" 0.002575, 0.0027 , 0.002575, 0.002375, 0.002475, 0.002475,\n",
" 0.002325, 0.002425, 0.002375]),\n",
" 'split2_train_score': array([0.00215 , 0.0022 , 0.00215 , 0.002375, 0.00205 , 0.002175,\n",
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" 0.00215 , 0.002175, 0.002175]),\n",
" 'mean_train_score': array([0.00255833, 0.00236667, 0.00233333, 0.002575 , 0.0025 ,\n",
" 0.00234167, 0.00228333, 0.00250833, 0.00235833, 0.0025 ,\n",
" 0.00248333, 0.00256667, 0.00225833, 0.00235 , 0.002275 ]),\n",
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" 2.55223214e-04, 1.94722024e-04, 1.12422813e-04, 9.64653075e-05,\n",
" 7.72801541e-05, 1.24163870e-04, 8.16496581e-05])}\n",
" \n",
" \n",
" {'mean_fit_time': array([ 685.01906315, 1809.28454868, 336.60541034, 878.23016135]),\n",
" 'mean_score_time': array([ 1.38006322, 1.27389534, 0.6934317 , 0.69225407]),\n",
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" 'mean_train_score': array([ 0.00245833, 0.00236667, 0.00248333, 0.00253333]),\n",
" 'param_batch_size': masked_array(data = [32 32 64 64],\n",
" mask = [False False False False],\n",
" fill_value = ?),\n",
" 'param_epochs': masked_array(data = [200 500 200 500],\n",
" mask = [False False False False],\n",
" fill_value = ?),\n",
" 'params': ({'batch_size': 32, 'epochs': 200},\n",
" {'batch_size': 32, 'epochs': 500},\n",
" {'batch_size': 64, 'epochs': 200},\n",
" {'batch_size': 64, 'epochs': 500}),\n",
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" 'split1_train_score': array([ 0.002675, 0.002725, 0.002675, 0.002825]),\n",
" 'split2_test_score': array([ 0.0029 , 0.00305, 0.003 , 0.00325]),\n",
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" 'std_test_score': array([ 0.00034721, 0.00037712, 0.00040069, 0.00044969]),\n",
" 'std_train_score': array([ 0.00015321, 0.00026952, 0.00023658, 0.00025685])}\n",
" \n",
" \n",
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" 644.64875857, 630.75466394]),\n",
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" 19.13357172]),\n",
" 'mean_score_time': array([1.9509182 , 2.09144211, 2.07234033, 1.05850196, 1.09700545,\n",
" 1.07024908]),\n",
" 'std_score_time': array([0.09494565, 0.09207867, 0.09335411, 0.04954752, 0.04209056,\n",
" 0.05320864]),\n",
" 'param_batch_size': masked_array(data=[32, 32, 32, 64, 64, 64],\n",
" mask=[False, False, False, False, False, False],\n",
" fill_value='?',\n",
" dtype=object),\n",
" 'param_ker_size': masked_array(data=[3, 5, 7, 3, 5, 7],\n",
" mask=[False, False, False, False, False, False],\n",
" fill_value='?',\n",
" dtype=object),\n",
" 'params': [{'batch_size': 32, 'ker_size': 3},\n",
" {'batch_size': 32, 'ker_size': 5},\n",
" {'batch_size': 32, 'ker_size': 7},\n",
" {'batch_size': 64, 'ker_size': 3},\n",
" {'batch_size': 64, 'ker_size': 5},\n",
" {'batch_size': 64, 'ker_size': 7}],\n",
" 'split0_test_score': array([0.00225, 0.0017 , 0.0027 , 0.00225, 0.00205, 0.0024 ]),\n",
" 'split1_test_score': array([0.0021 , 0.0023 , 0.0023 , 0.0019 , 0.002 , 0.00165]),\n",
" 'split2_test_score': array([0.00325, 0.00215, 0.0021 , 0.00245, 0.0024 , 0.0024 ]),\n",
" 'mean_test_score': array([0.00253333, 0.00205 , 0.00236667, 0.0022 , 0.00215 ,\n",
" 0.00215 ]),\n",
" 'std_test_score': array([0.00051045, 0.00025495, 0.00024944, 0.0002273 , 0.00017795,\n",
" 0.00035355]),\n",
" 'rank_test_score': array([1, 6, 2, 3, 4, 4], dtype=int32),\n",
" 'split0_train_score': array([0.001875, 0.001625, 0.002525, 0.002175, 0.0019 , 0.002275]),\n",
" 'split1_train_score': array([0.0027 , 0.00275 , 0.00245 , 0.0022 , 0.002125, 0.002475]),\n",
" 'split2_train_score': array([0.0024 , 0.00205 , 0.0019 , 0.0021 , 0.00225 , 0.001775]),\n",
" 'mean_train_score': array([0.002325 , 0.00214167, 0.00229167, 0.00215833, 0.00209167,\n",
" 0.002175 ]),\n",
" 'std_train_score': array([3.40954542e-04, 4.63830668e-04, 2.78637558e-04, 4.24918293e-05,\n",
" 1.44817893e-04, 2.94392029e-04])}\n",
" "
]
},
{
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}
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