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v0.056 train test val
1 parent 43197f4 commit 23a1762

7 files changed

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‎notebooks/11_spot_hpt_torch_fashion_mnist.ipynb‎

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"fun = HyperTorch(seed=123, log_level=50).fun_torch\n",
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"weights = 1.0\n",
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"shuffle = True\n",
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"eval = \"train_hold_out\"\n",
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"\n",
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"fun_control.update({\n",
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" \"data_dir\": None,\n",
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" \"metric\": None,\n",
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" \"metric_sklearn\": None,\n",
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" \"shuffle\": shuffle,\n",
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" \"eval\": eval\n",
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" })"
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]
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},

‎notebooks/12_spot_hpt_torch_cifar10.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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'12-torch_p040025_1min_5init_2023-05-07_10-00-44'"
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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 0.0.56\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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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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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": 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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},
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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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},
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"cell_type": "code",
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"execution_count": 11,
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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": 12,
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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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"Files already downloaded and verified\n",
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"Files already downloaded and verified\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"((50000, 32, 32, 3), (10000, 32, 32, 3))"
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]
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},
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"execution_count": 12,
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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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},
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},
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{
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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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"source": [
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"fun_control"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"fun = HyperTorch(seed=123, log_level=50).fun_torch\n",
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"weights = 1.0\n",
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"shuffle = True\n",
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"eval = \"train_hold_out\"\n",
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"\n",
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"fun_control.update({\n",
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" \"data_dir\": None,\n",
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" \"metric\": None,\n",
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" \"metric_sklearn\": None,\n",
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" \"shuffle\": shuffle,\n",
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" \"eval\": eval\n",
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" })"
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]
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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": 19,
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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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{
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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 | 1e-05 | 0.01 | 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": 21,
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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": 21,
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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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{
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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: 2.306\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",
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"min(spot_torch.y), max(spot_torch.y)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"trainset = fun_control[\"train\"]"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"model_default.evaluate_hold_out(dataset=trainset, shuffle=False, test_dataset=testset)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"model_spot.evaluate_hold_out(dataset=trainset, shuffle=False, test_dataset=testset)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",

‎notebooks/13_spot_hpt_torch_cv_fashion_mnist.ipynb‎

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"fun = HyperTorch(seed=123, log_level=50).fun_torch\n",
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"weights = -1.0\n",
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"shuffle = True\n",
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"eval=\"cv\"\n",
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"eval=\"train_cv\"\n",
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"\n",
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"fun_control.update({\n",
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" \"data_dir\": None,\n",

‎pyproject.toml‎

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[project]
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name = "spotPython"
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version = "0.0.55"
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version = "0.0.56"
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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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