{ "cells": [ { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "import os\n", "import glob\n", "\n", "from experiments import ExperimentRunner\n", "import params" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "ex = ExperimentRunner(params.experiments, n_jobs=1)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Running param set: {'data_path_base': 'vtest_new2', 'conv_model_name': 'resnet', 'num_epochs': 20, '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 0/19\n", "----------\n", "train Loss: 0.6817 F1: 0.5897 Acc: 0.5949\n", "val Loss: 0.7901 F1: 0.4043 Acc: 0.3412\n", "\n", "Epoch 1/19\n", "----------\n", "train Loss: 0.6104 F1: 0.7294 Acc: 0.7089\n", "val Loss: 0.6452 F1: 0.8125 Acc: 0.7176\n", "\n", "Epoch 2/19\n", "----------\n", "train Loss: 0.6116 F1: 0.7222 Acc: 0.6203\n", "val Loss: 0.6177 F1: 0.8217 Acc: 0.7294\n", "\n", "Epoch 3/19\n", "----------\n", "train Loss: 0.5695 F1: 0.7692 Acc: 0.6962\n", "val Loss: 0.6339 F1: 0.8095 Acc: 0.7176\n", "\n", "Epoch 4/19\n", "----------\n", "train Loss: 0.5504 F1: 0.7816 Acc: 0.7595\n", "val Loss: 0.6923 F1: 0.5743 Acc: 0.4941\n", "\n", "Epoch 5/19\n", "----------\n", "train Loss: 0.4833 F1: 0.8148 Acc: 0.8101\n", "val Loss: 0.6607 F1: 0.6789 Acc: 0.5882\n", "\n", "Epoch 6/19\n", "----------\n", "train Loss: 0.4592 F1: 0.8293 Acc: 0.8228\n", "val Loss: 0.6051 F1: 0.8130 Acc: 0.7294\n", "\n", "Epoch 7/19\n", "----------\n", "train Loss: 0.4290 F1: 0.8706 Acc: 0.8608\n", "val Loss: 0.5585 F1: 0.8722 Acc: 0.8000\n", "\n", "Epoch 8/19\n", "----------\n", "train Loss: 0.4170 F1: 0.8696 Acc: 0.8481\n", "val Loss: 0.5424 F1: 0.8722 Acc: 0.8000\n", "\n", "Epoch 9/19\n", "----------\n", "train Loss: 0.4004 F1: 0.8696 Acc: 0.8481\n", "val Loss: 0.5398 F1: 0.8788 Acc: 0.8118\n", "\n", "Epoch 10/19\n", "----------\n", "train Loss: 0.3825 F1: 0.9011 Acc: 0.8861\n", "val Loss: 0.5567 F1: 0.8594 Acc: 0.7882\n", "\n", "Epoch 11/19\n", "----------\n", "train Loss: 0.3598 F1: 0.9213 Acc: 0.9114\n", "val Loss: 0.5710 F1: 0.8571 Acc: 0.7882\n", "\n", "Epoch 12/19\n", "----------\n", "train Loss: 0.3281 F1: 0.9412 Acc: 0.9367\n", "val Loss: 0.5708 F1: 0.8480 Acc: 0.7765\n", "\n", "Epoch 13/19\n", "----------\n", "train Loss: 0.3187 F1: 0.9302 Acc: 0.9241\n", "val Loss: 0.5715 F1: 0.8226 Acc: 0.7412\n", "\n", "Epoch 14/19\n", "----------\n", "train Loss: 0.3049 F1: 0.9647 Acc: 0.9620\n", "val Loss: 0.5688 F1: 0.8033 Acc: 0.7176\n", "\n", "Epoch 15/19\n", "----------\n", "train Loss: 0.2929 F1: 0.9535 Acc: 0.9494\n", "val Loss: 0.5558 F1: 0.8320 Acc: 0.7529\n", "\n", "Epoch 16/19\n", "----------\n", "train Loss: 0.2833 F1: 0.9425 Acc: 0.9367\n", "val Loss: 0.5366 F1: 0.8504 Acc: 0.7765\n", "\n", "Epoch 17/19\n", "----------\n", "train Loss: 0.2662 F1: 0.9647 Acc: 0.9620\n", "val Loss: 0.5275 F1: 0.8594 Acc: 0.7882\n", "\n", "Epoch 18/19\n", "----------\n", "train Loss: 0.2576 F1: 0.9535 Acc: 0.9494\n", "val Loss: 0.5512 F1: 0.8293 Acc: 0.7529\n", "\n", "Epoch 19/19\n", "----------\n", "train Loss: 0.2463 F1: 0.9535 Acc: 0.9494\n", "val Loss: 0.5820 F1: 0.7863 Acc: 0.7059\n", "\n", "Training complete in 2m 51s\n", "Best val F1 : 0.878788\n", "Best val Acc : 0.811765\n" ] } ], "source": [ "ex.run()" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 89, "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": 90, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 aziai staff 4.6K Jun 2 23:21 results/results_20190602232142.csv\r\n", "-rw-r--r--@ 1 aziai staff 2.1K Jun 2 20:25 results/results_20190602195657.csv\r\n", "-rw-r--r--@ 1 aziai staff 1.4K Jun 2 19:48 results/results_20190602194822.csv\r\n", "-rw-r--r--@ 1 aziai staff 478B Jun 2 19:45 results/results_20190602194401.csv\r\n", "-rw-r--r--@ 1 aziai staff 846B Jun 2 19:28 results/results_20191402191416.csv\r\n" ] } ], "source": [ "!ls -lht results/*.csv" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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