mirror of
https://github.com/wassname/attentive-neural-processes.git
synced 2026-08-15 12:14:57 +08:00
misc
This commit is contained in:
wassname
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@@ -20,8 +20,8 @@
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:13.478035Z",
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"start_time": "2020-01-27T09:16:11.443192Z"
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"end_time": "2020-02-01T12:51:52.095766Z",
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"start_time": "2020-02-01T12:51:47.852973Z"
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}
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"outputs": [],
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@@ -50,8 +50,8 @@
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:13.518944Z",
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"start_time": "2020-02-01T12:51:52.117820Z"
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}
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},
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"outputs": [],
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@@ -66,8 +66,8 @@
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:13.559666Z",
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"start_time": "2020-01-27T09:16:13.521864Z"
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"end_time": "2020-02-01T12:51:52.320060Z",
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"start_time": "2020-02-01T12:51:52.232182Z"
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}
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},
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"outputs": [],
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@@ -82,16 +82,16 @@
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:13.663063Z",
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"start_time": "2020-01-27T09:16:13.562115Z"
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"end_time": "2020-02-01T12:51:52.631797Z",
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"start_time": "2020-02-01T12:51:52.323591Z"
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}
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},
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"outputs": [],
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"source": [
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"from src.models.model import LatentModel\n",
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"from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n",
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"from src.plot import plot_from_loader\n",
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"from src.models.lstm import SequenceDfDataSet, LSTM_PL\n",
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"# from src.plot import plot_from_loader\n",
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"from src.models.lstm import SequenceDfDataSet, LSTM_PL, plot_from_loader\n",
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"from src.dict_logger import DictLogger"
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]
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},
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@@ -100,8 +100,8 @@
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:13.720549Z",
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"start_time": "2020-01-27T09:16:13.665200Z"
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"end_time": "2020-02-01T12:51:52.688293Z",
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"start_time": "2020-02-01T12:51:52.634674Z"
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}
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},
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"outputs": [],
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@@ -123,8 +123,8 @@
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"execution_count": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:22.429851Z",
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"start_time": "2020-01-27T09:16:13.723294Z"
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"end_time": "2020-02-01T12:52:07.575506Z",
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"start_time": "2020-02-01T12:51:52.692536Z"
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}
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},
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"outputs": [],
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@@ -137,15 +137,15 @@
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"execution_count": 7,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:23.088727Z",
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"start_time": "2020-01-27T09:16:22.432605Z"
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"end_time": "2020-02-01T12:52:08.620008Z",
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"start_time": "2020-02-01T12:52:07.579719Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.legend.Legend at 0x7f7b849030b8>"
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"<matplotlib.legend.Legend at 0x7f10d7ef9b70>"
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]
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},
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"execution_count": 7,
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@@ -197,8 +197,8 @@
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"execution_count": 8,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:23.132107Z",
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"start_time": "2020-01-27T09:16:23.092355Z"
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"end_time": "2020-02-01T12:52:08.712221Z",
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"start_time": "2020-02-01T12:52:08.628358Z"
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}
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},
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"outputs": [],
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@@ -207,6 +207,7 @@
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"EPOCHS = 2\n",
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"DIR = Path(os.getcwd())\n",
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"MODEL_DIR = DIR/ 'optuna_result'/ 'lstm'\n",
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"name = \"lstm1\"\n",
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"MODEL_DIR.mkdir(parents=True, exist_ok=True)"
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]
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},
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@@ -215,8 +216,8 @@
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"execution_count": 9,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:23.175803Z",
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"start_time": "2020-01-27T09:16:23.134684Z"
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"end_time": "2020-02-01T12:52:08.844311Z",
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"start_time": "2020-02-01T12:52:08.720012Z"
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}
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},
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"outputs": [
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@@ -232,55 +233,21 @@
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"print(f\"now run `tensorboard --logdir {MODEL_DIR}\")"
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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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"ExecuteTime": {
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"end_time": "2020-01-27T04:59:44.555994Z",
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"start_time": "2020-01-27T04:59:44.486359Z"
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}
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},
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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": 10,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-01-27T09:16:23.236356Z",
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"start_time": "2020-01-27T09:16:23.179477Z"
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"end_time": "2020-02-01T12:52:08.936273Z",
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"start_time": "2020-02-01T12:52:08.846579Z"
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}
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},
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"outputs": [],
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"source": [
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"\n",
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"def objective(trial):\n",
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" # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n",
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" \n",
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" trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n",
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" trial.suggest_uniform(\"lstm_dropout\", 0, 0.75)\n",
|
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" trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128]) \n",
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" trial.suggest_categorical(\"lstm_layers\", [1, 2, 4, 8, 16, 32, 64, 128]) \n",
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" trial.suggest_categorical(\"bidirectional\", [False, True]) \n",
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" \n",
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" # constants\n",
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" trial.suggest_int(\"window_length\", 24 * 2, 24 * 2)\n",
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" trial.suggest_int(\"target_length\", 24, 24)\n",
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" trial.suggest_int(\"max_nb_epochs\", 20, 20)\n",
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" trial.suggest_int(\"num_workers\", 4, 4)\n",
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" trial.suggest_int(\"grad_clip\", 40, 40)\n",
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" trial.suggest_int(\"vis_i\", 670, 670)\n",
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" trial.suggest_int(\"input_size\", 17, 17)\n",
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" trial.suggest_int(\"batch_size\", 16, 16) \n",
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" \n",
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" print('trial', trial.number, 'params', trial.params)\n",
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" \n",
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" \n",
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"def main(trial, train=True):\n",
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" # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n",
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" # filenames match. Therefore, the filenames for each trial must be made unique.\n",
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" name = \"lstm\"\n",
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" \n",
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" checkpoint_callback = pl.callbacks.ModelCheckpoint(\n",
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" os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode='min')\n",
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"\n",
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@@ -300,15 +267,102 @@
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" early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n",
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" )\n",
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" model = LSTM_PL(trial.params)\n",
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" trainer.fit(model)\n",
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" if train:\n",
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" trainer.fit(model)\n",
|
||||
" return model, trainer"
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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": 44,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-02-01T13:06:23.208888Z",
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"start_time": "2020-02-01T13:06:23.092666Z"
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}
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},
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"outputs": [],
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"source": [
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"def add_suggest(trial):\n",
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" trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n",
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||||
" trial.suggest_uniform(\"lstm_dropout\", 0, 0.75)\n",
|
||||
" trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128]) \n",
|
||||
" trial.suggest_categorical(\"lstm_layers\", [1, 2, 4, 8]) \n",
|
||||
" trial.suggest_categorical(\"bidirectional\", [False, True]) \n",
|
||||
" \n",
|
||||
" # constants\n",
|
||||
" trial.suggest_int(\"window_length\", 24 * 2, 24 * 2)\n",
|
||||
" trial.suggest_int(\"target_length\", 24, 24)\n",
|
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" trial.suggest_int(\"max_nb_epochs\", 20, 20)\n",
|
||||
" trial.suggest_int(\"num_workers\", 4, 4)\n",
|
||||
" trial.suggest_int(\"grad_clip\", 40, 40)\n",
|
||||
" trial.suggest_int(\"vis_i\", 670, 670)\n",
|
||||
" trial.suggest_int(\"input_size\", 17, 17)\n",
|
||||
" trial.suggest_int(\"batch_size\", 16, 16) \n",
|
||||
" return trial"
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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": 45,
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||||
"metadata": {
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||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-01T13:06:23.437364Z",
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||||
"start_time": "2020-02-01T13:06:23.311890Z"
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}
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},
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"outputs": [],
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"source": [
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"\n",
|
||||
"def objective(trial):\n",
|
||||
" # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n",
|
||||
" trial = add_suggest(trial)\n",
|
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"\n",
|
||||
" \n",
|
||||
" print('trial', trial.number, 'params', trial.params)\n",
|
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" \n",
|
||||
" model, trainer = main(trial)\n",
|
||||
" \n",
|
||||
" # also report to tensorboard & print\n",
|
||||
" print('logger.metrics', logger.metrics[-1:])\n",
|
||||
" print('logger.metrics', model.logger.metrics[-1:])\n",
|
||||
" model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])\n",
|
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" logger.save()\n",
|
||||
" model.logger.save()\n",
|
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" \n",
|
||||
" return logger.metrics[-1]['val_loss']\n"
|
||||
" return model.logger.metrics[-1]['val_loss']\n"
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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": 12,
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"metadata": {
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||||
"ExecuteTime": {
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"end_time": "2020-02-01T12:52:09.197594Z",
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"start_time": "2020-02-01T12:52:09.072932Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[I 2020-02-01 20:52:09,183] Using an existing study with name 'no-name-b60e37fc-4ab6-4793-8a0a-c87a1b40c5c0' instead of creating a new one.\n"
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]
|
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}
|
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],
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"source": [
|
||||
"import argparse \n",
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"\n",
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"parser = argparse.ArgumentParser(description='PyTorch Lightning example.')\n",
|
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"parser.add_argument('--pruning', '-p', action='store_true',\n",
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" help='Activate the pruning feature. `MedianPruner` stops unpromising '\n",
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" 'trials at the early stages of training.')\n",
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"args = parser.parse_args(['-p'])\n",
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"\n",
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"pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n",
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"\n",
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||||
"study = optuna.create_study(direction='minimize', pruner=pruner, storage=f'sqlite:///optuna_result/{name}.db', study_name='no-name-b60e37fc-4ab6-4793-8a0a-c87a1b40c5c0', load_if_exists=True)\n",
|
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"\n",
|
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"# shutil.rmtree(MODEL_DIR)"
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]
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},
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{
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@@ -316,7 +370,20 @@
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2020-01-27T09:16:11.600Z"
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"end_time": "2020-01-27T23:38:16.813496Z",
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"start_time": "2020-01-27T23:38:16.766674Z"
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}
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},
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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": 13,
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"metadata": {
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"ExecuteTime": {
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||||
"end_time": "2020-02-01T12:52:20.456952Z",
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"start_time": "2020-02-01T12:52:09.200082Z"
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}
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},
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"outputs": [
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@@ -324,9 +391,145 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"trial 0 params {'learning_rate': 0.00010146652064922795, 'lstm_dropout': 0.5524433594179708, 'hidden_size': 8, 'lstm_layers': 64, 'bidirectional': True, 'window_length': 48, 'target_length': 24, 'max_nb_epochs': 20, 'num_workers': 4, 'grad_clip': 40, 'vis_i': 670, 'input_size': 17, 'batch_size': 16}\n"
|
||||
"trial 10 params {'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 64, 'input_size': 17, 'learning_rate': 0.00010639577108177795, 'lstm_dropout': 0.5343276200876307, 'lstm_layers': 4, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48}\n"
|
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
|
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"INFO:root:gpu available: True, used: True\n",
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"INFO:root:VISIBLE GPUS: 0\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(self, func, catch, gc_after_trial)\u001b[0m\n\u001b[1;32m 568\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 570\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m<ipython-input-11-add053deef1c>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m<ipython-input-10-310bffe5fc1e>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(trial, train)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py\u001b[0m in \u001b[0;36msingle_gpu_train\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot_gpu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m 304\u001b[0m \"\"\"\n\u001b[0;32m--> 305\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRNNBase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 225\u001b[0m \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 304\u001b[0m \"\"\"\n\u001b[0;32m--> 305\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
|
||||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-13-6c748c8a0b77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of finished trials: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Best trial:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m self._optimize_sequential(func, n_trials, timeout, catch, callbacks,\n\u001b[0;32m--> 302\u001b[0;31m gc_after_trial, None)\n\u001b[0m\u001b[1;32m 303\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0mtime_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(self, func, n_trials, timeout, catch, callbacks, gc_after_trial, time_start)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial_and_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 539\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial_and_callbacks\u001b[0;34m(self, func, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;31m# type: (...) -> None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(self, func, catch, gc_after_trial)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[0;31m# https://github.com/optuna/optuna/pull/325.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 592\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"study.optimize(objective, n_trials=200, timeout=6000)\n",
|
||||
"\n",
|
||||
"print('Number of finished trials: {}'.format(len(study.trials)))\n",
|
||||
"\n",
|
||||
"print('Best trial:')\n",
|
||||
"trial = study.best_trial\n",
|
||||
"\n",
|
||||
"print(' Value: {}'.format(trial.value))\n",
|
||||
"\n",
|
||||
"print(' Params: ')\n",
|
||||
"for key, value in trial.params.items():\n",
|
||||
" print(' {}: {}'.format(key, value))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-01T12:52:20.462742Z",
|
||||
"start_time": "2020-02-01T12:51:48.200Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"study.optimize(objective, n_trials=200, timeout=6000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-01T12:52:20.473924Z",
|
||||
"start_time": "2020-02-01T12:51:48.200Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = study.trials_dataframe(attrs=('number', 'value', 'params', 'state'))\n",
|
||||
"df.sort_values('value')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[I 2020-01-29 08:42:54,398] Finished trial#8 resulted in value: 0.005635036621242762. Current best value is 0.005635036621242762 with parameters: {'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 128, 'input_size': 17, 'learning_rate': 0.00019134834148401144, 'lstm_dropout': 0.4080689425353674, 'lstm_layers': 8, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48}."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-01T12:52:22.492191Z",
|
||||
"start_time": "2020-02-01T12:52:22.416653Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"FrozenTrial(number=8, value=0.005635036621242762, datetime_start=datetime.datetime(2020, 1, 29, 7, 59, 34, 718923), datetime_complete=datetime.datetime(2020, 1, 29, 8, 42, 54, 291152), params={'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 128, 'input_size': 17, 'learning_rate': 0.00019134834148401144, 'lstm_dropout': 0.4080689425353674, 'lstm_layers': 8, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48}, distributions={'batch_size': IntUniformDistribution(high=16, low=16), 'bidirectional': CategoricalDistribution(choices=(False, True)), 'grad_clip': IntUniformDistribution(high=40, low=40), 'hidden_size': CategoricalDistribution(choices=(1, 2, 4, 8, 16, 32, 64, 128)), 'input_size': IntUniformDistribution(high=17, low=17), 'learning_rate': LogUniformDistribution(high=0.01, low=1e-05), 'lstm_dropout': UniformDistribution(high=0.75, low=0), 'lstm_layers': CategoricalDistribution(choices=(1, 2, 4, 8)), 'max_nb_epochs': IntUniformDistribution(high=20, low=20), 'num_workers': IntUniformDistribution(high=4, low=4), 'target_length': IntUniformDistribution(high=24, low=24), 'vis_i': IntUniformDistribution(high=670, low=670), 'window_length': IntUniformDistribution(high=48, low=48)}, user_attrs={}, system_attrs={'_number': 8}, intermediate_values={0: 0.008975990116596222, 1: 0.007493948098272085, 2: 0.00577270332723856, 3: 0.006248514633625746, 4: 0.005840662866830826, 5: 0.005921561270952225, 6: 0.005806191358715296, 7: 0.005521276965737343, 8: 0.005634930916130543, 9: 0.005419003777205944, 10: 0.005680651403963566, 11: 0.005305697675794363, 12: 0.0053964052349328995, 13: 0.005444809794425964, 14: 0.005449710413813591, 15: 0.005424626171588898, 16: 0.0055605689994990826, 17: 0.005586943589150906, 18: 0.0056319404393434525, 19: 0.005635036621242762}, trial_id=9, state=TrialState.COMPLETE)"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" trial = study.best_trial\n",
|
||||
"except ValueError as e:\n",
|
||||
" logging.warning(f\"Could not load best trial as: '{e}'\")\n",
|
||||
" trial = study.get_trials(1)[0]\n",
|
||||
"trial"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-02-01T13:16:23.050700Z",
|
||||
"start_time": "2020-02-01T13:12:23.739975Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
@@ -335,8 +538,8 @@
|
||||
"INFO:root:VISIBLE GPUS: 0\n",
|
||||
"INFO:root:\n",
|
||||
" Name Type Params\n",
|
||||
"0 _model LSTMNet 107 K\n",
|
||||
"1 _model.lstm1 LSTM 106 K\n",
|
||||
"0 _model LSTMNet 1 M\n",
|
||||
"1 _model.lstm1 LSTM 999 K\n",
|
||||
"2 _model.linear Linear 1 K\n"
|
||||
]
|
||||
},
|
||||
@@ -354,18 +557,33 @@
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "516c0dacaa1047fc9af021bc54e64f95",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"step 0, {'val_loss': '0.297493040561676'}\n",
|
||||
"\n",
|
||||
"step 0, {'val_loss': '0.23856914043426514'}\n",
|
||||
"\r"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "015bffde987142e7ad2e9d8ff3e11686",
|
||||
"model_id": "f5a24ba06eb142cab3bbd7dfb04f1d99",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
@@ -375,34 +593,35 @@
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-48-6157e0ff9134>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_suggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m103\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[0;32m<ipython-input-10-310bffe5fc1e>\u001b[0m in \u001b[0;36mmain\u001b[0;34m(trial, train)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLSTM_PL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;31m# ON CPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py\u001b[0m in \u001b[0;36msingle_gpu_train\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_pretrain_routine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdp_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mrun_pretrain_routine\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 840\u001b[0m \u001b[0;31m# CORE TRAINING LOOP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0;31m# RUN TNG EPOCH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0;31m# -----------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;31m# update LR schedulers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;31m# RUN TRAIN STEP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;31m# ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 389\u001b[0m \u001b[0mbatch_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_norm_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_step_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;31m# calculate loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer_closure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[0;31m# nan grads\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36moptimizer_closure\u001b[0;34m()\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[0;31m# backward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[0mmodel_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 494\u001b[0;31m \u001b[0mmodel_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_amp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 495\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# track metrics for callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/core/hooks.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, use_amp, loss, optimizer, optimizer_idx)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \"\"\"\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m 97\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 98\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m allow_unreachable=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import argparse \n",
|
||||
"\n",
|
||||
"parser = argparse.ArgumentParser(description='PyTorch Lightning example.')\n",
|
||||
"parser.add_argument('--pruning', '-p', action='store_true',\n",
|
||||
" help='Activate the pruning feature. `MedianPruner` stops unpromising '\n",
|
||||
" 'trials at the early stages of training.')\n",
|
||||
"args = parser.parse_args(['-p'])\n",
|
||||
"\n",
|
||||
"pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n",
|
||||
"\n",
|
||||
"study = optuna.create_study(direction='minimize', pruner=pruner)\n",
|
||||
"study.optimize(objective, n_trials=200, timeout=6000)\n",
|
||||
"\n",
|
||||
"print('Number of finished trials: {}'.format(len(study.trials)))\n",
|
||||
"\n",
|
||||
"print('Best trial:')\n",
|
||||
"trial = study.best_trial\n",
|
||||
"\n",
|
||||
"print(' Value: {}'.format(trial.value))\n",
|
||||
"\n",
|
||||
"print(' Params: ')\n",
|
||||
"for key, value in trial.params.items():\n",
|
||||
" print(' {}: {}'.format(key, value))\n",
|
||||
"\n",
|
||||
"# shutil.rmtree(MODEL_DIR)"
|
||||
"trial = optuna.trial.FixedTrial(params={'number':45, 'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 128, 'input_size': 17, 'learning_rate': 0.00019134834148401144, 'lstm_dropout': 0.4080689425353674, 'lstm_layers': 8, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48})\n",
|
||||
"trial = add_suggest(trial)\n",
|
||||
"trial.number = 103\n",
|
||||
"main(trial)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -410,24 +629,13 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-01-27T09:05:22.547568Z",
|
||||
"start_time": "2020-01-27T09:05:22.473558Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2020-01-27T09:16:11.600Z"
|
||||
"end_time": "2020-02-01T13:16:23.059314Z",
|
||||
"start_time": "2020-02-01T13:12:25.100Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# test"
|
||||
"# model, trainer = main(trial, train=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -435,13 +643,13 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2020-01-27T09:16:11.600Z"
|
||||
"end_time": "2020-02-01T13:16:23.063840Z",
|
||||
"start_time": "2020-02-01T13:12:25.300Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"trial = study.trials[0]\n",
|
||||
"model = LSTM_PL(trial.params)"
|
||||
"# model, trainer = main(trial, train=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -449,13 +657,17 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2020-01-27T09:16:11.600Z"
|
||||
"end_time": "2020-02-01T13:16:23.065715Z",
|
||||
"start_time": "2020-02-01T13:12:25.400Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot\n",
|
||||
"loader = model.val_dataloader()[0]\n",
|
||||
"dset_test = loader.dataset"
|
||||
"vis_i=640\n",
|
||||
"plot_from_loader(loader, model, vis_i=vis_i, n=5)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -463,38 +675,16 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2020-01-27T09:16:11.600Z"
|
||||
"end_time": "2020-02-01T13:16:23.067381Z",
|
||||
"start_time": "2020-02-01T13:12:25.500Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.cuda()"
|
||||
"# test\n",
|
||||
"trainer.test(model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-01-27T09:02:48.225225Z",
|
||||
"start_time": "2020-01-27T09:02:48.174287Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2020-01-27T09:03:20.413623Z",
|
||||
"start_time": "2020-01-27T09:03:20.313659Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -542,12 +732,17 @@
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": true,
|
||||
"sideBar": false,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_position": {
|
||||
"height": "calc(100% - 180px)",
|
||||
"left": "10px",
|
||||
"top": "150px",
|
||||
"width": "384px"
|
||||
},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": true
|
||||
},
|
||||
|
||||
+500
-2133
File diff suppressed because one or more lines are too long
@@ -5,8 +5,9 @@ from argparse import ArgumentParser
|
||||
from test_tube import Experiment, HyperOptArgumentParser
|
||||
from src.models.model import LatentModel
|
||||
from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df
|
||||
from src.plot import plot_from_loader_to_tensor
|
||||
from src.plot import plot_from_loader_to_tensor, plot_from_loader
|
||||
from src.utils import ObjectDict
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
|
||||
|
||||
@@ -51,13 +52,17 @@ class LatentModelPL(pl.LightningModule):
|
||||
return {"val_loss": loss, "log": tensorboard_logs}
|
||||
|
||||
def validation_end(self, outputs):
|
||||
if self.hparams["vis_i"] > 0:
|
||||
if int(self.hparams["vis_i"]) > 0:
|
||||
# https://github.com/PytorchLightning/pytorch-lightning/blob/f8d9f8f/pytorch_lightning/core/lightning.py#L293
|
||||
loader = self.val_dataloader()[0]
|
||||
vis_i = min(self.hparams["vis_i"], len(loader.dataset))
|
||||
vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset))
|
||||
# print('vis_i', vis_i)
|
||||
image = plot_from_loader_to_tensor(loader, self.model, i=vis_i)
|
||||
self.logger.experiment.add_image('val/image', image, self.trainer.global_step)
|
||||
if isinstance(self.hparams["vis_i"], str):
|
||||
image = plot_from_loader(loader, self.model, i=int(vis_i))
|
||||
plt.show()
|
||||
else:
|
||||
image = plot_from_loader_to_tensor(loader, self.model, i=vis_i)
|
||||
self.logger.experiment.add_image('val/image', image, self.trainer.global_step)
|
||||
|
||||
keys = outputs[0]["log"].keys()
|
||||
# tensorboard_logs = {}
|
||||
|
||||
Reference in New Issue
Block a user