{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "import os\n", "import glob\n", "\n", "from experiments import ExperimentRunner\n", "import params" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "ex = ExperimentRunner(params.experiment_vggish_only, n_jobs=1)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Running param set: {'data_path_base': '/Users/aziai/Downloads/vtest_new2', 'conv_model_name': None, 'num_epochs': 10, 'feature_extract': False, 'batch_size': 64, 'lr': 0.001, 'use_vggish': True, 'momentum': 0.9}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Updating ALL params\n", "Epoch 0/9\n", "----------\n", "train Loss: 0.8801 F1: 0.6833 Acc: 0.5190\n", "val Loss: 0.5289 F1: 0.8742 Acc: 0.7765\n", "\n", "Epoch 1/9\n", "----------\n", "train Loss: 0.8135 F1: 0.6833 Acc: 0.5190\n", "val Loss: 0.5584 F1: 0.8742 Acc: 0.7765\n", "\n", "Epoch 2/9\n", "----------\n", "train Loss: 0.7179 F1: 0.6833 Acc: 0.5190\n", "val Loss: 0.6482 F1: 0.8667 Acc: 0.7647\n", "\n", "Epoch 3/9\n", "----------\n", "train Loss: 0.6749 F1: 0.6833 Acc: 0.5190\n", "val Loss: 0.6608 F1: 0.8725 Acc: 0.7765\n", "\n", "Epoch 4/9\n", "----------\n", "train Loss: 0.6832 F1: 0.6842 Acc: 0.5443\n", "val Loss: 0.6556 F1: 0.8467 Acc: 0.7529\n", "\n", "Epoch 5/9\n", "----------\n", "train Loss: 0.6969 F1: 0.6667 Acc: 0.5823\n", "val Loss: 0.6542 F1: 0.8413 Acc: 0.7647\n", "\n", "Epoch 6/9\n", "----------\n", "train Loss: 0.6960 F1: 0.5060 Acc: 0.4810\n", "val Loss: 0.6501 F1: 0.8788 Acc: 0.8118\n", "\n", "Epoch 7/9\n", "----------\n", "train Loss: 0.6896 F1: 0.5882 Acc: 0.5570\n", "val Loss: 0.6488 F1: 0.8837 Acc: 0.8235\n", "\n", "Epoch 8/9\n", "----------\n", "train Loss: 0.6764 F1: 0.6667 Acc: 0.6203\n", "val Loss: 0.6576 F1: 0.8976 Acc: 0.8471\n", "\n", "Epoch 9/9\n", "----------\n", "train Loss: 0.6710 F1: 0.7253 Acc: 0.6835\n", "val Loss: 0.6590 F1: 0.8710 Acc: 0.8118\n", "\n", "Training complete in 0m 56s\n", "Best val F1 : 0.897638\n", "Best val Acc : 0.847059\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Running param set: {'data_path_base': '/Users/aziai/Downloads/vtest_new2', 'conv_model_name': None, 'num_epochs': 10, 'feature_extract': True, 'batch_size': 64, 'lr': 0.001, 'use_vggish': True, 'momentum': 0.9}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Params to update\n", "* combined.weight\n", "* combined.bias\n", "Epoch 0/9\n", "----------\n", "train Loss: 0.7826 F1: 0.6833 Acc: 0.5190\n", "val Loss: 0.5453 F1: 0.8742 Acc: 0.7765\n", "\n", "Epoch 1/9\n", "----------\n", "train Loss: 0.7525 F1: 0.6833 Acc: 0.5190\n", "val Loss: 0.5790 F1: 0.8742 Acc: 0.7765\n", "\n", "Epoch 2/9\n", "----------\n", "train Loss: 0.7158 F1: 0.6833 Acc: 0.5190\n", "val Loss: 0.6449 F1: 0.8742 Acc: 0.7765\n", "\n", "Epoch 3/9\n", "----------\n", "train Loss: 0.6936 F1: 0.6838 Acc: 0.5316\n", "val Loss: 0.7350 F1: 0.0870 Acc: 0.2588\n", "\n", "Epoch 4/9\n", "----------\n", "train Loss: 0.7142 F1: nan Acc: 0.4430\n", "val Loss: 0.8224 F1: nan Acc: 0.2235\n", "\n", "Epoch 5/9\n", "----------\n", "train Loss: 0.7382 F1: nan Acc: 0.4810\n", "val Loss: 0.8526 F1: nan Acc: 0.2235\n", "\n", "Epoch 6/9\n", "----------\n", "train Loss: 0.7477 F1: nan Acc: 0.4810\n", "val Loss: 0.8238 F1: nan Acc: 0.2235\n", "\n", "Epoch 7/9\n", "----------\n", "train Loss: 0.7361 F1: nan Acc: 0.4810\n", "val Loss: 0.7583 F1: 0.0299 Acc: 0.2353\n", "\n", "Epoch 8/9\n", "----------\n", "train Loss: 0.7142 F1: nan Acc: 0.4304\n", "val Loss: 0.6900 F1: 0.6491 Acc: 0.5294\n", "\n", "Epoch 9/9\n", "----------\n", "train Loss: 0.6995 F1: 0.5161 Acc: 0.4304\n", "val Loss: 0.6337 F1: 0.8742 Acc: 0.7765\n", "\n", "Training complete in 0m 26s\n", "Best val F1 : 0.874172\n", "Best val Acc : 0.776471\n" ] } ], "source": [ "ex.run()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "paths = glob.glob('results/*.csv') # * means all if need specific format then *.csv\n", "latest = max(paths, key=os.path.getctime)\n", "df = pd.read_csv(latest)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
batch_sizeconv_model_namedata_path_baseexperiment_uuidfeature_extractlrmomentumnum_epochsrunner_uuiduse_vggishval_accval_f1
164resnet/Users/aziai/Downloads/vtest_new220191402191416False0.0010.910b3290017112d2116e4bdbb9c8dbf15a8e75adacb942afb...True0.8941180.935252
064resnet/Users/aziai/Downloads/vtest_new220191402191416False0.0010.9100e57debc92afdd0dc7a209584b4d97860c9dba98f3aed4...False0.8235290.878049
364resnet/Users/aziai/Downloads/vtest_new220191402191416True0.0010.9108e644bc291a463725bf0bcb11825a196383a4860eeecd7...True0.7294120.824427
264resnet/Users/aziai/Downloads/vtest_new220191402191416True0.0010.910e07e5119b07164f06098d1adba9e4c43ad0344716a0746...False0.5647060.626263
\n", "
" ], "text/plain": [ " batch_size conv_model_name data_path_base ... use_vggish val_acc val_f1\n", "1 64 resnet /Users/aziai/Downloads/vtest_new2 ... True 0.894118 0.935252\n", "0 64 resnet /Users/aziai/Downloads/vtest_new2 ... False 0.823529 0.878049\n", "3 64 resnet /Users/aziai/Downloads/vtest_new2 ... True 0.729412 0.824427\n", "2 64 resnet /Users/aziai/Downloads/vtest_new2 ... False 0.564706 0.626263\n", "\n", "[4 rows x 12 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(by='val_f1', ascending=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }