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bartzbeielsteinbartzbeielstein
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v0.0.52
DataParallel cuda handling
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‎notebooks/11_spot_hpt_torch_fashion_mnist.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'11-torch_p040025_1min_5init_2023-05-06_20-54-49'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pickle\n",
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"import socket\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"spotPython 0.0.51\n",
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"spotRiver 0.0.92\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"pip list | grep \"spot[RiverPython]\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"outputs": [],
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},
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{
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"cell_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.0.0\n",
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"MPS device: mps\n"
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]
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}
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],
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"source": [
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"print(torch.__version__)\n",
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"# Check that MPS is available\n",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(torch.Size([60000, 28, 28]), torch.Size([10000, 28, 28]))"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train, test = load_data()\n",
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"train.data.shape, test.data.shape"
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]
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},
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{
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"cell_type": "code",
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},
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"cell_type": "code",
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"execution_count": 11,
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},
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"cell_type": "code",
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"execution_count": 12,
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},
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"cell_type": "code",
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},
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"cell_type": "code",
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},
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"cell_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"| name | type | default | lower | upper | transform |\n",
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"|------------|--------|-----------|---------|---------|-----------------------|\n",
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"| l1 | int | 5 | 2 | 9 | transform_power_2_int |\n",
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"| l2 | int | 5 | 2 | 9 | transform_power_2_int |\n",
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"| lr | float | 0.001 | 0.0001 | 0.1 | None |\n",
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"| batch_size | int | 4 | 1 | 4 | transform_power_2_int |\n",
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"| epochs | int | 3 | 1 | 4 | transform_power_2_int |\n",
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"| k_folds | int | 2 | 0 | 0 | None |\n"
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]
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}
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],
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"source": [
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"print(gen_design_table(fun_control))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[5.e+00, 5.e+00, 1.e-03, 4.e+00, 3.e+00, 2.e+00]])"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X0 = get_default_values(fun_control)\n",
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"river_hyper_dict_default = fun_control[\"core_model_hyper_dict\"]\n",
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[5.e+00, 5.e+00, 1.e-03, 4.e+00, 3.e+00, 2.e+00]])"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from spotPython.hyperparameters.values import get_default_hyperparameters_as_array\n",
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"hyper_dict=TorchHyperDict().load()\n",
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1, 2000] loss: 2.452\n",
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"[1, 4000] loss: 1.163\n",
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"[2, 2000] loss: 2.328\n",
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"[2, 4000] loss: 1.163\n",
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"[3, 2000] loss: 2.328\n",
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"[3, 4000] loss: 1.164\n",
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"[4, 2000] loss: 2.329\n",
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"[4, 4000] loss: 1.163\n",
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"Accuracy on hold-out set: 0.10116666666666667\n",
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"Loss on hold-out set: 2.320516836643219\n",
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"[1, 2000] loss: 2.351\n",
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"[1, 4000] loss: 1.166\n",
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"[1, 6000] loss: 0.777\n",
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"[1, 8000] loss: 0.583\n",
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"[1, 10000] loss: 0.466\n",
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"[1, 12000] loss: 0.388\n",
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"[1, 14000] loss: 0.333\n",
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"[1, 16000] loss: 0.291\n",
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"[1, 18000] loss: 0.259\n",
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"[2, 2000] loss: 2.330\n",
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"[2, 4000] loss: 1.164\n",
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"[2, 6000] loss: 0.776\n",
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"[2, 8000] loss: 0.583\n",
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"[2, 10000] loss: 0.467\n",
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"[2, 12000] loss: 0.388\n",
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"[2, 14000] loss: 0.333\n",
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"[2, 16000] loss: 0.291\n",
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"[2, 18000] loss: 0.259\n",
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"[3, 2000] loss: 2.332\n",
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"[3, 4000] loss: 1.165\n",
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"[3, 6000] loss: 0.777\n",
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"[3, 8000] loss: 0.582\n",
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"[3, 10000] loss: 0.467\n",
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"[3, 12000] loss: 0.389\n",
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"[3, 14000] loss: 0.333\n",
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"[3, 16000] loss: 0.292\n",
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"[3, 18000] loss: 0.259\n",
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"[4, 2000] loss: 2.334\n",
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"[4, 4000] loss: 1.165\n",
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"[4, 6000] loss: 0.778\n",
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"[4, 8000] loss: 0.583\n",
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"[4, 10000] loss: 0.466\n",
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"[4, 12000] loss: 0.389\n",
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"[4, 14000] loss: 0.333\n",
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"[4, 16000] loss: 0.291\n",
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"[4, 18000] loss: 0.259\n",
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"[5, 2000] loss: 2.327\n",
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"[5, 4000] loss: 1.169\n",
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"[5, 6000] loss: 0.778\n",
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"[5, 8000] loss: 0.583\n",
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"[5, 10000] loss: 0.466\n",
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"[5, 12000] loss: 0.389\n",
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"[5, 14000] loss: 0.333\n",
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"[5, 16000] loss: 0.291\n",
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"[5, 18000] loss: 0.259\n",
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"[6, 2000] loss: 2.335\n",
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"[6, 4000] loss: 1.166\n",
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"[6, 6000] loss: 0.778\n",
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"[6, 8000] loss: 0.582\n",
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"[6, 10000] loss: 0.466\n",
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"[6, 12000] loss: 0.389\n",
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"[6, 14000] loss: 0.333\n",
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"[6, 16000] loss: 0.292\n",
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"[6, 18000] loss: 0.259\n",
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"[7, 2000] loss: 2.330\n",
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"[7, 4000] loss: 1.166\n",
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"[7, 6000] loss: 0.776\n",
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"[7, 8000] loss: 0.582\n",
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"[7, 10000] loss: 0.466\n",
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"[7, 12000] loss: 0.389\n",
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"[7, 14000] loss: 0.333\n",
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"[7, 16000] loss: 0.291\n",
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"[7, 18000] loss: 0.259\n",
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"[8, 2000] loss: 2.327\n",
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"[8, 4000] loss: 1.167\n",
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"[8, 6000] loss: 0.777\n",
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"[8, 8000] loss: 0.582\n",
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"[8, 10000] loss: 0.466\n",
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"[8, 12000] loss: 0.389\n",
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"[8, 14000] loss: 0.334\n",
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"[8, 16000] loss: 0.292\n",
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"[8, 18000] loss: 0.259\n"
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]
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}
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],
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"source": [
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"spot_torch = spot.Spot(fun=fun,\n",
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" lower = lower,\n",

‎pyproject.toml‎

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[project]
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name = "spotPython"
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version = "0.0.51"
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version = "0.0.52"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

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