{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "qcOwXI31eN0Y" }, "source": [ "Let's make sure we are using tensorflow v2+" ] }, { "cell_type": "markdown", "metadata": { "id": "JHEt8HwIH6_x" }, "source": [ "# Training and inference with colab\n", "[Google colab](https://colab.research.google.com) is a free server for running notebooks with GPU/TPU support - this is a great way to use _DAS_ if you do not have a computer with a GPU.\n", "\n", "This notebook demonstrates \n", "- how to setup _DAS_ \n", "- how to load your own datasets\n", "- how to train a network and then use that network to label a new audio recording.\n", "\n", "Open and edit this notebook in colab by clicking this badge:\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/janclemenslab/das/blob/master/colab/colab.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "f1TSU8apeH9x" }, "outputs": [], "source": [ "%tensorflow_version 2.x" ] }, { "cell_type": "markdown", "metadata": { "id": "M_HarM2deUY2" }, "source": [ "Install _DAS_:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "xfOlezxOHGHu", "outputId": "01161dc3-d318-46e5-df45-6ce1d875c94a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting das\n", " Downloading das-0.22.1-py3-none-any.whl (77 kB)\n", "\u001b[K |████████████████████████████████| 77 kB 3.0 MB/s eta 0:00:011\n", "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from das) (1.4.1)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from das) (1.19.5)\n", "Collecting zarr\n", " Downloading zarr-2.10.0-py3-none-any.whl (146 kB)\n", "\u001b[K |████████████████████████████████| 146 kB 7.1 MB/s \n", "\u001b[?25hRequirement already satisfied: librosa in 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\u001b[?25l\u001b[?25hdone\n", " Created wheel for asciitree: filename=asciitree-0.3.3-py3-none-any.whl size=5051 sha256=8b134871e2a3d88db6eeeb1d6938976d2892be448eff2ecc74944e9eca70293f\n", " Stored in directory: /root/.cache/pip/wheels/12/1c/38/0def51e15add93bff3f4bf9c248b94db0839b980b8535e72a0\n", "Successfully built defopt asciitree\n", "Installing collected packages: pockets, mypy-extensions, typing-inspect, sphinxcontrib-napoleon, numcodecs, fasteners, asciitree, zarr, peakutils, matplotlib-scalebar, flammkuchen, defopt, das\n", "Successfully installed asciitree-0.3.3 das-0.22.1 defopt-6.1.0 fasteners-0.16.3 flammkuchen-0.9.2 matplotlib-scalebar-0.7.2 mypy-extensions-0.4.3 numcodecs-0.9.1 peakutils-1.3.3 pockets-0.9.1 sphinxcontrib-napoleon-0.7 typing-inspect-0.7.1 zarr-2.10.0\n" ] }, { "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "sphinxcontrib" ] } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "!pip install das" ] }, { "cell_type": "markdown", "metadata": { "id": "GupBBiSXFWjf" }, "source": [ "Import all the things:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Rm-lDKcqFWjf" }, "outputs": [], "source": [ "import das.train, das.predict, das.utils, das.npy_dir\n", "import matplotlib.pyplot as plt\n", "import flammkuchen\n", "import logging\n", "logging.basicConfig(level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": { "id": "FevZwGYzeg5k" }, "source": [ "Mount your google drive so you can access your own datasets - this will ask for authentication." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gnBEpxg02dQ4", "outputId": "5afc17f2-e111-4f1d-ecdb-4a825f11cb51" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "markdown", "metadata": { "id": "4R29gIh-ex-P" }, "source": [ "## Train the model\n", "Adjust the variable `path_to_data` to point to the dataset on your own google drive." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-jMWYYfmFWjf", "outputId": "fecc66d2-96f7-4103-f456-0c380128da4f", "tags": [ "outputPrepend" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:Loading data from /content/drive/MyDrive/Dmoj.wrigleyi.npy.\n", "INFO:root:Version of the data:\n", "INFO:root: MD5 hash of /content/drive/MyDrive/Dmoj.wrigleyi.npy is\n", "INFO:root: 5381c36663f3b7286b0a5c42c0e3e463\n", "INFO:root:Parameters:\n", "INFO:root:{'data_dir': '/content/drive/MyDrive/Dmoj.wrigleyi.npy', 'y_suffix': '', 'save_dir': 'res', 'save_prefix': '', 'model_name': 'tcn', 'nb_filters': 32, 'kernel_size': 32, 'nb_conv': 4, 'use_separable': [True, True, False, False], 'nb_hist': 1024, 'ignore_boundaries': True, 'batch_norm': True, 'nb_pre_conv': 0, 'pre_nb_dft': 64, 'pre_kernel_size': 3, 'pre_nb_filters': 16, 'pre_nb_conv': 2, 'nb_lstm_units': 0, 'verbose': 2, 'batch_size': 32, 'nb_epoch': 1000, 'learning_rate': 0.0005, 'reduce_lr': False, 'reduce_lr_patience': 5, 'fraction_data': None, 'seed': None, 'batch_level_subsampling': False, 'tensorboard': False, 'neptune_api_token': None, 'neptune_project': None, 'log_messages': False, 'nb_stacks': 2, 'with_y_hist': True, 'x_suffix': '', 'balance': False, 'version_data': True, 'sample_weight_mode': 'temporal', 'data_padding': 128, 'return_sequences': True, 'stride': 768, 'y_offset': 0, 'output_stride': 1, 'class_names': ['noise', 'pulse'], 'class_types': ['segment', 'event'], 'filename_endsample_test': [], 'filename_endsample_train': [], 'filename_endsample_val': [], 'filename_startsample_test': [], 'filename_startsample_train': [], 'filename_startsample_val': [], 'filename_train': [], 'filename_val': [], 'samplerate_x_Hz': 10000.0, 'samplerate_y_Hz': 10000.0, 'filename_test': [], 'data_hash': '5381c36663f3b7286b0a5c42c0e3e463', 'nb_freq': 16, 'nb_channels': 16, 'nb_classes': 2, 'first_sample_train': 0, 'last_sample_train': None, 'first_sample_val': 0, 'last_sample_val': None}\n", "INFO:root:Preparing data\n", "INFO:root:Training data:\n", "INFO:root: AudioSequence with 47 batches each with 32 items.\n", " Total of 1158000 samples with\n", " each x=(16,) and\n", " each y=(2,)\n", "INFO:root:Validation data:\n", "INFO:root: AudioSequence with 15 batches each with 32 items.\n", " Total of 386000 samples with\n", " each x=(16,) and\n", " each y=(2,)\n", "INFO:root:building network\n", "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", " \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n", "INFO:root:None\n", "INFO:root:Will save to res/20210925_132436.\n", "INFO:root:start training\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model: \"TCN\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_1 (InputLayer) [(None, 1024, 16)] 0 \n", "__________________________________________________________________________________________________\n", "conv1d (Conv1D) (None, 1024, 32) 544 input_1[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d (SeparableConv (None, 1024, 32) 8224 conv1d[0][0] \n", "__________________________________________________________________________________________________\n", "activation (Activation) (None, 1024, 32) 0 separable_conv1d[0][0] \n", "__________________________________________________________________________________________________\n", "lambda (Lambda) (None, 1024, 32) 0 activation[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d (SpatialDropo (None, 1024, 32) 0 lambda[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d[0][0] \n", "__________________________________________________________________________________________________\n", "add (Add) (None, 1024, 32) 0 conv1d[0][0] \n", " conv1d_1[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_1 (SeparableCo (None, 1024, 32) 8224 add[0][0] \n", "__________________________________________________________________________________________________\n", "activation_1 (Activation) (None, 1024, 32) 0 separable_conv1d_1[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_1 (Lambda) (None, 1024, 32) 0 activation_1[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_1 (SpatialDro (None, 1024, 32) 0 lambda_1[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_1[0][0] \n", "__________________________________________________________________________________________________\n", "add_1 (Add) (None, 1024, 32) 0 add[0][0] \n", " conv1d_2[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_2 (SeparableCo (None, 1024, 32) 8224 add_1[0][0] \n", "__________________________________________________________________________________________________\n", "activation_2 (Activation) (None, 1024, 32) 0 separable_conv1d_2[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_2 (Lambda) (None, 1024, 32) 0 activation_2[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_2 (SpatialDro (None, 1024, 32) 0 lambda_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_2[0][0] \n", "__________________________________________________________________________________________________\n", "add_2 (Add) (None, 1024, 32) 0 add_1[0][0] \n", " conv1d_3[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_3 (SeparableCo (None, 1024, 32) 8224 add_2[0][0] \n", "__________________________________________________________________________________________________\n", "activation_3 (Activation) (None, 1024, 32) 0 separable_conv1d_3[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_3 (Lambda) (None, 1024, 32) 0 activation_3[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_3 (SpatialDro (None, 1024, 32) 0 lambda_3[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_4 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_3[0][0] \n", "__________________________________________________________________________________________________\n", "add_3 (Add) (None, 1024, 32) 0 add_2[0][0] \n", " conv1d_4[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_4 (SeparableCo (None, 1024, 32) 8224 add_3[0][0] \n", "__________________________________________________________________________________________________\n", "activation_4 (Activation) (None, 1024, 32) 0 separable_conv1d_4[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_4 (Lambda) (None, 1024, 32) 0 activation_4[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_4 (SpatialDro (None, 1024, 32) 0 lambda_4[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_5 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_4[0][0] \n", "__________________________________________________________________________________________________\n", "add_4 (Add) (None, 1024, 32) 0 add_3[0][0] \n", " conv1d_5[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_5 (SeparableCo (None, 1024, 32) 8224 add_4[0][0] \n", "__________________________________________________________________________________________________\n", "activation_5 (Activation) (None, 1024, 32) 0 separable_conv1d_5[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_5 (Lambda) (None, 1024, 32) 0 activation_5[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_5 (SpatialDro (None, 1024, 32) 0 lambda_5[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_6 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_5[0][0] \n", "__________________________________________________________________________________________________\n", "add_5 (Add) (None, 1024, 32) 0 add_4[0][0] \n", " conv1d_6[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_6 (SeparableCo (None, 1024, 32) 8224 add_5[0][0] \n", "__________________________________________________________________________________________________\n", "activation_6 (Activation) (None, 1024, 32) 0 separable_conv1d_6[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_6 (Lambda) (None, 1024, 32) 0 activation_6[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_6 (SpatialDro (None, 1024, 32) 0 lambda_6[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_7 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_6[0][0] \n", "__________________________________________________________________________________________________\n", "add_6 (Add) (None, 1024, 32) 0 add_5[0][0] \n", " conv1d_7[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_7 (SeparableCo (None, 1024, 32) 8224 add_6[0][0] \n", "__________________________________________________________________________________________________\n", "activation_7 (Activation) (None, 1024, 32) 0 separable_conv1d_7[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_7 (Lambda) (None, 1024, 32) 0 activation_7[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_7 (SpatialDro (None, 1024, 32) 0 lambda_7[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_8 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_7[0][0] \n", "__________________________________________________________________________________________________\n", "add_7 (Add) (None, 1024, 32) 0 add_6[0][0] \n", " conv1d_8[0][0] \n", "__________________________________________________________________________________________________\n", "separable_conv1d_8 (SeparableCo (None, 1024, 32) 8224 add_7[0][0] \n", "__________________________________________________________________________________________________\n", "activation_8 (Activation) (None, 1024, 32) 0 separable_conv1d_8[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_8 (Lambda) (None, 1024, 32) 0 activation_8[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_8 (SpatialDro (None, 1024, 32) 0 lambda_8[0][0] \n", 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(SpatialDro (None, 1024, 32) 0 lambda_9[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_10 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_9[0][0] \n", "__________________________________________________________________________________________________\n", "add_9 (Add) (None, 1024, 32) 0 add_8[0][0] \n", " conv1d_10[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_11 (Conv1D) (None, 1024, 32) 32800 add_9[0][0] \n", "__________________________________________________________________________________________________\n", "activation_10 (Activation) (None, 1024, 32) 0 conv1d_11[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_10 (Lambda) (None, 1024, 32) 0 activation_10[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_10 (SpatialDr (None, 1024, 32) 0 lambda_10[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_12 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_10[0][0] \n", "__________________________________________________________________________________________________\n", "add_10 (Add) (None, 1024, 32) 0 add_9[0][0] \n", " conv1d_12[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_13 (Conv1D) (None, 1024, 32) 32800 add_10[0][0] \n", "__________________________________________________________________________________________________\n", "activation_11 (Activation) (None, 1024, 32) 0 conv1d_13[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_11 (Lambda) (None, 1024, 32) 0 activation_11[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_11 (SpatialDr (None, 1024, 32) 0 lambda_11[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_14 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_11[0][0] \n", "__________________________________________________________________________________________________\n", "add_11 (Add) (None, 1024, 32) 0 add_10[0][0] \n", " conv1d_14[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_15 (Conv1D) (None, 1024, 32) 32800 add_11[0][0] \n", "__________________________________________________________________________________________________\n", "activation_12 (Activation) (None, 1024, 32) 0 conv1d_15[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_12 (Lambda) (None, 1024, 32) 0 activation_12[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_12 (SpatialDr (None, 1024, 32) 0 lambda_12[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_16 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_12[0][0] \n", "__________________________________________________________________________________________________\n", "add_12 (Add) (None, 1024, 32) 0 add_11[0][0] \n", " conv1d_16[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_17 (Conv1D) (None, 1024, 32) 32800 add_12[0][0] \n", "__________________________________________________________________________________________________\n", "activation_13 (Activation) (None, 1024, 32) 0 conv1d_17[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_13 (Lambda) (None, 1024, 32) 0 activation_13[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_13 (SpatialDr (None, 1024, 32) 0 lambda_13[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_18 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_13[0][0] \n", "__________________________________________________________________________________________________\n", "add_13 (Add) (None, 1024, 32) 0 add_12[0][0] \n", " conv1d_18[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_19 (Conv1D) (None, 1024, 32) 32800 add_13[0][0] \n", "__________________________________________________________________________________________________\n", "activation_14 (Activation) (None, 1024, 32) 0 conv1d_19[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_14 (Lambda) (None, 1024, 32) 0 activation_14[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_14 (SpatialDr (None, 1024, 32) 0 lambda_14[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_20 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_14[0][0] \n", "__________________________________________________________________________________________________\n", "add_14 (Add) (None, 1024, 32) 0 add_13[0][0] \n", " conv1d_20[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_21 (Conv1D) (None, 1024, 32) 32800 add_14[0][0] \n", "__________________________________________________________________________________________________\n", "activation_15 (Activation) (None, 1024, 32) 0 conv1d_21[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_15 (Lambda) (None, 1024, 32) 0 activation_15[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_15 (SpatialDr (None, 1024, 32) 0 lambda_15[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_22 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_15[0][0] \n", "__________________________________________________________________________________________________\n", "add_15 (Add) (None, 1024, 32) 0 add_14[0][0] \n", " conv1d_22[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_23 (Conv1D) (None, 1024, 32) 32800 add_15[0][0] \n", "__________________________________________________________________________________________________\n", "activation_16 (Activation) (None, 1024, 32) 0 conv1d_23[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_16 (Lambda) (None, 1024, 32) 0 activation_16[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_16 (SpatialDr (None, 1024, 32) 0 lambda_16[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_24 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_16[0][0] \n", "__________________________________________________________________________________________________\n", "add_16 (Add) (None, 1024, 32) 0 add_15[0][0] \n", " conv1d_24[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_25 (Conv1D) (None, 1024, 32) 32800 add_16[0][0] \n", "__________________________________________________________________________________________________\n", "activation_17 (Activation) (None, 1024, 32) 0 conv1d_25[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_17 (Lambda) (None, 1024, 32) 0 activation_17[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_17 (SpatialDr (None, 1024, 32) 0 lambda_17[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_26 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_17[0][0] \n", "__________________________________________________________________________________________________\n", "add_17 (Add) (None, 1024, 32) 0 add_16[0][0] \n", " conv1d_26[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_27 (Conv1D) (None, 1024, 32) 32800 add_17[0][0] \n", "__________________________________________________________________________________________________\n", "activation_18 (Activation) (None, 1024, 32) 0 conv1d_27[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_18 (Lambda) (None, 1024, 32) 0 activation_18[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_18 (SpatialDr (None, 1024, 32) 0 lambda_18[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_28 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_18[0][0] \n", "__________________________________________________________________________________________________\n", "add_18 (Add) (None, 1024, 32) 0 add_17[0][0] \n", " conv1d_28[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_29 (Conv1D) (None, 1024, 32) 32800 add_18[0][0] \n", "__________________________________________________________________________________________________\n", "activation_19 (Activation) (None, 1024, 32) 0 conv1d_29[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_19 (Lambda) (None, 1024, 32) 0 activation_19[0][0] \n", "__________________________________________________________________________________________________\n", "spatial_dropout1d_19 (SpatialDr (None, 1024, 32) 0 lambda_19[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_30 (Conv1D) (None, 1024, 32) 1056 spatial_dropout1d_19[0][0] \n", "__________________________________________________________________________________________________\n", "add_20 (Add) (None, 1024, 32) 0 conv1d_1[0][0] \n", " conv1d_2[0][0] \n", " conv1d_3[0][0] \n", " conv1d_4[0][0] \n", " conv1d_5[0][0] \n", " conv1d_6[0][0] \n", " conv1d_7[0][0] \n", " conv1d_8[0][0] \n", " conv1d_9[0][0] \n", " conv1d_10[0][0] \n", " conv1d_12[0][0] \n", " conv1d_14[0][0] \n", " conv1d_16[0][0] \n", " conv1d_18[0][0] \n", " conv1d_20[0][0] \n", " conv1d_22[0][0] \n", " conv1d_24[0][0] \n", " conv1d_26[0][0] \n", " conv1d_28[0][0] \n", " conv1d_30[0][0] \n", "__________________________________________________________________________________________________\n", "activation_20 (Activation) (None, 1024, 32) 0 add_20[0][0] \n", "__________________________________________________________________________________________________\n", "dense (Dense) (None, 1024, 2) 66 activation_20[0][0] \n", "__________________________________________________________________________________________________\n", "activation_21 (Activation) (None, 1024, 2) 0 dense[0][0] \n", "==================================================================================================\n", "Total params: 431,970\n", "Trainable params: 431,970\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n", "Epoch 1/1000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2470: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n", " warnings.warn('`Model.state_updates` will be removed in a future version. '\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Epoch 00001: val_loss improved from inf to 0.04569, saving model to res/20210925_132436_model.h5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n", " category=CustomMaskWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "47/47 - 68s - loss: 0.0796 - val_loss: 0.0457\n", "Epoch 2/1000\n", "\n", "Epoch 00002: val_loss improved from 0.04569 to 0.04298, saving model to res/20210925_132436_model.h5\n", "47/47 - 21s - loss: 0.0333 - val_loss: 0.0430\n", "Epoch 3/1000\n", "\n", "Epoch 00003: val_loss improved from 0.04298 to 0.04224, saving model to res/20210925_132436_model.h5\n", "47/47 - 21s - loss: 0.0325 - val_loss: 0.0422\n", "Epoch 4/1000\n", "\n", "Epoch 00004: val_loss improved from 0.04224 to 0.04108, saving model to res/20210925_132436_model.h5\n", "47/47 - 21s - loss: 0.0317 - val_loss: 0.0411\n", "Epoch 5/1000\n", "\n", "Epoch 00005: val_loss did not improve from 0.04108\n", "47/47 - 21s - loss: 0.0328 - val_loss: 0.0416\n", "Epoch 6/1000\n", "\n", "Epoch 00006: val_loss improved from 0.04108 to 0.04089, saving model to res/20210925_132436_model.h5\n", "47/47 - 21s - loss: 0.0301 - val_loss: 0.0409\n", "Epoch 7/1000\n", "\n", "Epoch 00007: val_loss did not improve from 0.04089\n", "47/47 - 21s - loss: 0.0303 - val_loss: 0.0410\n", "Epoch 8/1000\n", "\n", "Epoch 00008: val_loss did not improve from 0.04089\n", "47/47 - 21s - loss: 0.0289 - val_loss: 0.0410\n", "Epoch 9/1000\n", "\n", "Epoch 00009: val_loss did not improve from 0.04089\n", "47/47 - 21s - loss: 0.0302 - val_loss: 0.0413\n", "Epoch 10/1000\n", "\n", "Epoch 00010: val_loss improved from 0.04089 to 0.04059, saving model to res/20210925_132436_model.h5\n", "47/47 - 21s - loss: 0.0283 - val_loss: 0.0406\n", "Epoch 11/1000\n", "\n", "Epoch 00011: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0269 - val_loss: 0.0409\n", "Epoch 12/1000\n", "\n", "Epoch 00012: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0279 - val_loss: 0.0410\n", "Epoch 13/1000\n", "\n", "Epoch 00013: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0278 - val_loss: 0.0410\n", "Epoch 14/1000\n", "\n", "Epoch 00014: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0267 - val_loss: 0.0417\n", "Epoch 15/1000\n", "\n", "Epoch 00015: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0267 - val_loss: 0.0409\n", "Epoch 16/1000\n", "\n", "Epoch 00016: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0265 - val_loss: 0.0408\n", "Epoch 17/1000\n", "\n", "Epoch 00017: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0266 - val_loss: 0.0415\n", "Epoch 18/1000\n", "\n", "Epoch 00018: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0271 - val_loss: 0.0424\n", "Epoch 19/1000\n", "\n", "Epoch 00019: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0287 - val_loss: 0.0429\n", "Epoch 20/1000\n", "\n", "Epoch 00020: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0268 - val_loss: 0.0412\n", "Epoch 21/1000\n", "\n", "Epoch 00021: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0272 - val_loss: 0.0423\n", "Epoch 22/1000\n", "\n", "Epoch 00022: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0276 - val_loss: 0.0417\n", "Epoch 23/1000\n", "\n", "Epoch 00023: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0268 - val_loss: 0.0417\n", "Epoch 24/1000\n", "\n", "Epoch 00024: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0271 - val_loss: 0.0418\n", "Epoch 25/1000\n", "\n", "Epoch 00025: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0269 - val_loss: 0.0421\n", "Epoch 26/1000\n", "\n", "Epoch 00026: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0258 - val_loss: 0.0420\n", "Epoch 27/1000\n", "\n", "Epoch 00027: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0259 - val_loss: 0.0421\n", "Epoch 28/1000\n", "\n", "Epoch 00028: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0256 - val_loss: 0.0424\n", "Epoch 29/1000\n", "\n", "Epoch 00029: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0281 - val_loss: 0.0438\n", "Epoch 30/1000\n", "\n", "Epoch 00030: val_loss did not improve from 0.04059\n", "47/47 - 21s - loss: 0.0269 - val_loss: 0.0424\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:re-loading last best model\n", "INFO:root:predicting\n", "INFO:root:evaluating\n", "INFO:root:[[354461 1856]\n", " [ 1718 10605]]\n", "INFO:root:{'noise': {'precision': 0.9951765825610157, 'recall': 0.9947911550669769, 'f1-score': 0.9949838314881768, 'support': 356317}, 'pulse': {'precision': 0.8510552925126394, 'recall': 0.8605858962914875, 'f1-score': 0.8557940606843125, 'support': 12323}, 'accuracy': 0.9903049045138889, 'macro avg': {'precision': 0.9231159375368276, 'recall': 0.9276885256792322, 'f1-score': 0.9253889460862447, 'support': 368640}, 'weighted avg': {'precision': 0.9903588561686921, 'recall': 0.9903049045138889, 'f1-score': 0.9903309572867444, 'support': 368640}}\n", "INFO:root:saving to res/20210925_132436_results.h5.\n", "/usr/local/lib/python3.7/dist-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: 'f1-score'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", " NaturalNameWarning)\n", "/usr/local/lib/python3.7/dist-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: 'macro avg'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", " NaturalNameWarning)\n", "/usr/local/lib/python3.7/dist-packages/tables/path.py:112: NaturalNameWarning: object name is not a valid Python identifier: 'weighted avg'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though\n", " NaturalNameWarning)\n", "INFO:root:DONE.\n" ] }, { "data": { "text/plain": [ "(,\n", " {'balance': False,\n", " 'batch_level_subsampling': False,\n", " 'batch_norm': True,\n", " 'batch_size': 32,\n", " 'class_names': ['noise', 'pulse'],\n", " 'class_types': ['segment', 'event'],\n", " 'class_weights': None,\n", " 'data_dir': '/content/drive/MyDrive/Dmoj.wrigleyi.npy',\n", " 'data_hash': '5381c36663f3b7286b0a5c42c0e3e463',\n", " 'data_padding': 128,\n", " 'filename_endsample_test': [],\n", " 'filename_endsample_train': [],\n", " 'filename_endsample_val': [],\n", " 'filename_startsample_test': [],\n", " 'filename_startsample_train': [],\n", " 'filename_startsample_val': [],\n", " 'filename_test': [],\n", " 'filename_train': [],\n", " 'filename_val': [],\n", " 'first_sample_train': 0,\n", " 'first_sample_val': 0,\n", " 'fraction_data': None,\n", " 'ignore_boundaries': True,\n", " 'kernel_size': 32,\n", " 'last_sample_train': None,\n", " 'last_sample_val': None,\n", " 'learning_rate': 0.0005,\n", " 'log_messages': False,\n", " 'model_name': 'tcn',\n", " 'nb_channels': 16,\n", " 'nb_classes': 2,\n", " 'nb_conv': 4,\n", " 'nb_epoch': 1000,\n", " 'nb_filters': 32,\n", " 'nb_freq': 16,\n", " 'nb_hist': 1024,\n", " 'nb_lstm_units': 0,\n", " 'nb_pre_conv': 0,\n", " 'nb_stacks': 2,\n", " 'neptune_project': None,\n", " 'output_stride': 1,\n", " 'pre_kernel_size': 3,\n", " 'pre_nb_conv': 2,\n", " 'pre_nb_dft': 64,\n", " 'pre_nb_filters': 16,\n", " 'reduce_lr': False,\n", " 'reduce_lr_patience': 5,\n", " 'return_sequences': True,\n", " 'sample_weight_mode': 'temporal',\n", " 'samplerate_x_Hz': 10000.0,\n", " 'samplerate_y_Hz': 10000.0,\n", " 'save_dir': 'res',\n", " 'save_prefix': '',\n", " 'seed': None,\n", " 'stride': 768,\n", " 'tensorboard': False,\n", " 'use_separable': [True, True, False, False],\n", " 'verbose': 2,\n", " 'version_data': True,\n", " 'with_y_hist': True,\n", " 'x_suffix': '',\n", " 'y_offset': 0,\n", " 'y_suffix': ''})" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path_to_data = '/content/drive/MyDrive/Dmoj.wrigleyi.npy'\n", "\n", "das.train.train(model_name='tcn',\n", " data_dir=path_to_data,\n", " save_dir='res',\n", " nb_hist=1024,\n", " kernel_size=32,\n", " nb_filters=32,\n", " ignore_boundaries=True,\n", " verbose=2,\n", " nb_conv=4,\n", " learning_rate=0.0005,\n", " use_separable=[True, True, False, False],\n", " nb_epoch=1000)" ] }, { "cell_type": "markdown", "metadata": { "id": "8Vnn1RzqfAsR" }, "source": [ "Adjust the name to point to the results:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "qpKFtj3zFWjf" }, "outputs": [], "source": [ "res_name = '/content/res/20210925_132436'\n", "res = flammkuchen.load(f'{res_name}_results.h5')" ] }, { "cell_type": "markdown", "metadata": { "id": "EjvHmmXyfXLm" }, "source": [ "Inspect the history of the training and validation loss" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 279 }, "id": "zCH0jn00Bl1n", "outputId": "94b864a2-e135-4826-ea88-a5e1cdccdf8a" }, "outputs": [ { "data": { "image/png": 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E83XArX09wMwiwPeAi4EqYIWZLXb3tXGbfQyoc/dpZnYj8E1gUbhuo7vP6Wf5EqIgO8pb21WTEJHU1a+ahLu/7u6nAbOB2e5+OnDhYR62ANjg7pvcvQ14GLiq2zZXAQ+E048BF5mZ9bv0CRbUJBQSIpK6BnRlOnffF555DfC3h9l8AlAZN18VLutxG3fvAPYCReG6MjN7zcyeN7NzB1LOwVKQnU5Dawex2HF93qCISK/629zUk0T+4t8OTHb3WjObB/zGzE6JC6igAGa3AbcBTJ48edALUZAVxR0a2jr2D9MhIpJKjuYa14f7eb0NmBQ3PzFc1uM2ZhYFRgC17t7q7rUA7r4S2AiceEgB3H/k7vPdfX5JScmR7UUf9o/fpCOcRCRF9RkSZlZvZvt6uNUD4w/z3CuA6WZWZmYZwI3A4m7bLOZAB/h1wDPu7mZWEnZ8E167YjqwaYD7dtQ0EqyIpLo+m5vcPf9In9jdO8zsU8DTBFexuy+8YNFdQLm7LwZ+AjxoZhuA3QRBAnAecJeZtQMx4HZ3332kZTlSGglWRFLd0fRJHJa7LwGWdFv2lbjpFuD6Hh73OPB4IsvWHxoJVkRS3dH0SRz3DtQk1NwkIqlJIdGHA30SqkmISGpSSPQhLzMICY0EKyKpSiHRh2gkjdyMiDquRSRlKSQOQyPBikgqU0gchsZvEpFUppA4jILsqE6mE5GUpZA4DNUkRCSVKSQOoyBbISEiqUsh0dEGz34DWht6XF2QFdUhsCKSshQSVa/AC9+CB6+G5rpDVudnBUc3ueuaEiKSehQSpQvhhgdg++vw0yuhofqg1QXZUWIOjW2dSSqgiEjyKCQATr4SbnoYajfA/ZfC3qr9q3RNCRFJZQqJLtMugluegIZdcN9lULsRiBsJVp3XIpKCFBLxppwNty6Gtga4/zLYuTauJqHOaxFJPQqJ7safDh95CjD46eWMaVgLqLlJRFKTQqIno2fAR38HmflMXXITZ9jb1LcqJETkGNm9GV59EBprk10ShUSvRpXBR35HLH8cP8u4m7zK55NdIhE53u3eDE9+Ev5rHiz+FPzHLPjDV6GxJmlFUkj0ZcQEYrf+lk0+jgtf+zSsfTLZJRKR41F8OLzxK1jw8aDZe8blsOyeICx+/w/BgTXHmB0vJ4nNnz/fy8vLE/LcC/7xcX5d8B0mNq2F934ZZlwJJSeBWUJeTyShWuuhqRYKS5NdEtm9GZZ+C1Y9BGlRmP8ROOezUDDuwDY164MTflc/CpHMcJvPQP7YQSmCma109/m9rldIHN6Z3/gj75uaxz+1fws2/CFYmDcGys4Lb+dD4ZSEvLbIEWtrgpp1sOtt2LUWqt8OpvduDdafeClc8g0omprccqai3Zth6bfh9YfAIj2HQ3e1G8PHPBwEyry/hIWfhYLxR1UUhcQguPg7zzNtdB4/+NA8qKuAzS8cuDXsDDYaOeVAYJSdB/ljElIWkR61N8PGZ2FbOex6K7jVVQDh5zuSAcUnwuiToWQGeAz+/J/Q2Qbv+TSc+7eQkZvMPTi+dbRBYzXUb4eVPx1YOHS3exMs/U74HGkw98Ow8HMwYuIRFU0hMQiu/cEystMj/Pyvzjx4hTtUrwsD43moWAote4N1JSfDaYtgzocgryQh5ZIU19YI638PaxcH920NwS/MomlBEIyeGRypN3omFJZBJHrw4+t3wB++Am88AgUT4ZKvw8yrB9aM2tEG65+G134efA5KToJJZ8KkBcH9EX5xJYR78PlsCo8YMgu+ZPu6xTqDII21Q2d7MN0ZTsfi5tuboakmGNansRoadwWdzQ27gvmWPQfKsb/JaIDh0F1dBbz47/DaL2DqhXDzo0f0NAqJQfCX97/C7sY2Fn9qYd8bxjphxxvBh+Wdp2HLnyEtHWa+H+Z/FKaco34MOTote4P/rbVPwoY/QUcz5BTDyX8BJ78/GIssmjmw59yyHJ76AuxYDaXnwuX/FtQ4+rLrrSAYXn84+HLMGwsnvg9qN8G2lUG5APLHHwiMSQtg7GyIZhzZvneJxSDW0e3WGXwR128Pwq+3+46Wo3vt/sguhNwSyB0NucWQNzqcD28T5w9afwIAeyqD/SqefkQPV0gMgk8/9BpvVO3huS+8d2APrH4HVt4Pq34RfLiLTwzC4rQbg3+k/oh1Bh1XO94I2jHHnAKl5/T/8XJkOjvg3deg4gWoeDFoGjjh/KA5ccypkHYMDwxs2g3rlgQ1hk3PBr9e88YGPz5Ofj9MeQ+kRY7uNWKdwf/qn/4p6Ng+86/hgjsga8SBbVr2wppfB+GwrTyotZx0GZx+C0y96EBNpbMddq6Byleg8mWoXHGgHySaBePmBL+gO9qgsxU6WoN96u0+1nlwINDP76z03OB18scFX8p5Y4Lp3OKgluCxPm4evG5aBCLpQXNdJCPY567pSNx0NDMI65yiow/BY0whMQj+4TerWbJ6B6/+48VH9gRtTbD2N1B+H1StCD4op14bBMaEeQdqF+3NsHMt7Hgdtr8RBMPOtQd+le1nMO40KDs3+NKafDZk5h3VPh6VWCzom6mrgLrNQRV73Gkw+SxIz05euQYi1hn8kt78QtBsuGU5tNUH60afEnw51awL5nOKgr/7CRcEt8E+aKGtEba+FJRj89IgrLwTRkwKQmHmVTDxjMQEVWMtPHMXrHwg+DL9P18L9u/VB4PaS0dz0JQ69xaYdUP/m1L3bQ+G5a8Mby17gmaXSHrwBdv1Rbv/PjP4so1kBLXxtEjwBb3/1sN81oggDLpCITN/8P8+xyGFxCD419+9zY9e2MT6f74MO9rmou2vQ/n98Maj0N4IY2cF7cc7VkPNO8GvGAj+4cfODm7jZgfbFZbCu6vCL48Xgg9brD34kEyYFzQVlJ0XVOu7fznHOqG9CdpbgvuOrvvWsP01En7wIuF09ND5lj1hEIS33ZuD+z1beq7GRzKDspxwQXAbN+fQdvHB0NkR/C3bGoNAjsWNs7X//bJD59sagi/jzS/AlhcP9CcVnxj+Lc8N7nOLg+X73oVNz8Om54Jbw45geWHZgX0sXRiEyED+T9qbg1/cm5cG7+22lcE+pEVh/NzgPZ1xeTB9rJor330Nlnwh+FEDkFkQ/LA5/RaYcAzLIQmnkBgEP3x+I3c/9TZr77qEnIxB+pJr2QerfxUc6dBUG4TA/kCYDSMnH/6D2NYUfrmER1p1/eKMZAbV7PaW4Auoozmotg+mjHwYVRoE10G3sqAprKo86Mzf9DzsXB08JrMg+BItOz9ouimZcWAf3YOLPjXsCmoljdXBfcOu4NZUE1w9sD0MgvamIBTam45+3wpLw1A4PyhffzoTuw5a6AqMihcP1DzS0iF7JGSN7Pt+z9YgGLaVB/tgkWDssNKFQUBNOiv5NcS3FgfNRzOugIyc5JVFEiapIWFmlwL/CUSAH7v73d3WZwI/A+YBtcAid6+IWz8ZWAvc6e7f6uu1EhkSv3x5K19+YjUvfekixo7ISshrDIqWfbB1+YFDc9OzIT0nuI9mh/Nxt2h22MkZtr/GOoOQ6eoI7D6fmR+EQGEp5Izq/6/JxpqgTJueC4KjriJY3tVG3HUESKyH8bHS0sOOv+IgmDJygn3KyD1wv386J2iHjqSHDw7/t7v/j3fNR8Ia2MjJA/oz96izPQjpypeD0G/eE9S8mvcE4dc13bL3QLksLWiWKw1rLJPPgqyCoy+LyAAcLiQSUPff/8IR4HvAxUAVsMLMFrv72rjNPgbUufs0M7sR+CawKG79d4CnElXG/irIDv5M9S3tQzsksgrgxEuC21CSWwynXhPcAOq2HKhltOwJOuNzS4LQyBsdhkJ4n104PJo2IunhUTwL+t4uFoPWfcF+Zxce3DEsMgQlLCSABcAGd98EYGYPA1cR1Ay6XAXcGU4/BnzXzMzd3cyuBjYDjQksY7/kZ+nCQ4OqcAoUfjg4CSjVpKUFTU3ZI5NdEpF+SeRxfBOAyrj5qnBZj9u4ewewFygyszzgi8DX+noBM7vNzMrNrLy6urqvTY9KQVaQpbrwkIikmqE6CuydwL+7e0NfG7n7j9x9vrvPLylJ3FnNuoSpiKSqRDY3bQMmxc1PDJf1tE2VmUWBEQQd2GcC15nZvwIjgZiZtbj7dxNY3l4duISpQkJEUksiQ2IFMN3MygjC4Ebgg922WQzcCiwHrgOe8eBwq3O7NjCzO4GGZAUEQH5Xc1OLmptEJLUkLCTcvcPMPgU8TXAI7H3u/qaZ3QWUu/ti4CfAg2a2AdhNECRDTlZ6hMxommoSIpJyElmTwN2XAEu6LftK3HQLcP1hnuPOhBRugAqy01WTEJGUM1Q7roec/KyoOq5FJOUoJPqpICtdzU0iknIUEv2k5iYRSUUKiX4qyIpSr5qEiKQYhUQ/BTUJhYSIpBaFRD8FfRJqbhKR1KKQ6KeC7ChtnTFa2juTXRQRkWNGIdFPGglWRFKRQqKfNBKsiKQihUQ/aSRYEUlFCol+0kiwIpKKFBL9NCJbI8GKSOpRSPSTahIikooUEv3U1SdRr5qEiKQQhUQ/ZUbTSI+YOq5FJKUoJPrJzDQSrIikHIXEAGgkWBFJNQqJASjIiqomISIpRSExACNzMnhpUy2f+PlKflVeSXV9a7KLJCKSUAm9xvXx5ouXzuDBl7bwzNs7eWrNDgBOmziCC2eM4cIZozllfAFpaZaw129u6yQ7I5Kw5xcR6c7cPdllGBTz58/38vLyY/Ja7s7a7ft45q1dPLNuF6sq9+AOJfmZXHjSaN47YzQLpxeTl3n0GbyvpZ0lb2znsZVVrNxax+ffdxL/94KpmCUujEQkdZjZSnef3+t6hcTRq21o5bl11TyzbhcvrKumvrWD9Igxd3Ih504v5pxpxcyeOJJIP2sZnTFn2cYaHltZxdNv7qClPca00XmMG5HF0vU1fGxhGf/v8pMTWmsRkdSgkDjG2jtjlFfU8dy6XSxdX8Pa7fuAoNP77KlFLJxewsJpxZQW5RxSG9hY3cDjK6t44rVtbN/bQkFWlPfPGc918yZx2sQRuMNd/7uWny6r4JrTJ/DN62aTHlG3kogcOYVEktU2tPLnjbX8eX0NL26oYdueZgAmjMxm4bRiFk4vpr6lg8dWVvLq1j2kGZx/YgnXzZvERSePJiv94D4Id+e7z2zg2394hwtnjOZ7H5yrfgoROWIKiSHE3amobeLF9dW8uKGGZRtr9w/zceKYPK6bN5Gr50xgdEHWYZ/r5y9t4R+fXMPcyYXcd+sZjMhJT3TxReQ4pJAYwjo6Y6x5dx/RNOOU8QUD7oxesno7n314FWXFuTzw0QWMHXH4cBERiXe4kFCDdhJFI2nMmTSSUyeMOKKjlS6fNY77P3IGVXVNXPuDZWyqbkhAKUUklSkkhrlzphXz0G1n0dzeyfU/XM7qqr3JLpKIHEcSGhJmdqmZrTOzDWZ2Rw/rM83skXD9y2ZWGi5fYGarwtvrZvaBRJZzuJs9cSSP3X42WekRbrr3JZZtqEl2kUTkOJGwPgkziwDvABcDVcAK4CZ3Xxu3zf8FZrv77WZ2I/ABd19kZjlAm7t3mNk44HVgvLv3OrrecOyTGGw79rbw4ftepqKmiVvfM4U0M5rbO2lp76SlPRY3Hcy3tHfiwFWnjefDZ5cOSuf36qq9LN1QzVVzJjBhZPbR75SIJFTSOq7N7GzgTne/JJz/EoC7/0vcNk+H2yw3syiwAyjxuEKZWRnwEjBBIXF4e5rauP3nK3lp024yo2lkZ0TIikbIzogcMp+VnsaepnaWbawlNyPCTQsm87Fzyxg3YmBf7p0x5w9rd3DfixW8UrEbgLzMKP9wxcksOmOSzg4XGcIOFxKJHLtpAlAZN18FnNnbNmGtYS9QBNSY2ZnAfcAU4JaeAsLMbgNuA5g8efKg78BwNDIng4dvO5tYzPt9RvbbO/bx389v4v5lFTywvIIPnD6B286byrTReX0+rr6lnUdWVPLTZRVU1TUzaVQ2//gXM3nP1CLu+p+13PHr1fx29Xbuvna2ahUiw1QiaxLXAZe6+1+F87cAZ7r7p+K2WRNuUxXObwy3qYnb5mTgAeA8d2/p7fVUkzh6lbub+PHSTTy8opK2zhiXzBQJx7sAAA8VSURBVBzL7RdMZc6kkQdtt7W2ifuXbeZX5VU0tHawoHQUH11YxsUzx+wfeiQWc37xylb+ZclbpJnx/644mRtVqxAZcpJZk9gGTIqbnxgu62mbqrC5aQRQG7+Bu79lZg3AqYBSIIEmjcrha1edyt9cNJ0HllXwwLIKfvfmDs4+oYhPXDCVzGgaP3lxM394aycRM648bTwfPaeMWRNHHPJcaWnGLWdN4YITS/j7x97gS79ezRLVKkSGnUTWJKIEHdcXEYTBCuCD7v5m3DafBGbFdVxf4+43hP0QlWET1BRgOUEHd6+H7agmMfgaWjt4+JWt3Lt0Ezv3BdfOKMxJ5+Yzp3DL2VMY048zw+HQWsWXLz+ZmxaoVpEsbR0xVm/by+mTRmqQSEnuGddmdjnwH0AEuM/d/9nM7gLK3X2xmWUBDwKnA7uBG919U9g0dQfQDsSAu9z9N329lkIicVo7OvntG9vpjDl/MXv8EY8VVbm7iS8+/gbLNtaycFoxd187i4mFOYNcWunLs2/v4p/+dy2baho5+4QivnXDaarZpTgNyyFDSnytwoDPXXwiN585RYMUJtjG6ga+/r9reXZdNWXFuVw5exw/eXEzaWZ89f2ncO3cCarZpSiFhAxJlbub+PITq1m6voZRuRncenYpHz57CoW5GUf1vM1tndQ1tZEZTSMzPTjsN5pmKfsFuK+lnXv+uJ6fLqsgKz3CZy6azq3vKSUjmsbW2iY+/6vXeaViN++bOYZvXDOL4rzMZBdZgEfLK/n+sxv4PyeP4aYzJzO1pO8jDY+GQkKGtBUVu/nhcxv509u7yOk6V2NhGeMH0ARS39LOM2/vYsnq7Ty3rprWjthB69MMMqMRMtPTgvCIBuGRnxXl4pljuXbeBEbnH1+DI3bGnF+VV/JvT69jd1MbN8ybxOcvOYmS/MxDtvvJi5v41tPvkJ8V5V+umcX7Thmb0LJ1feekanD3xd359u/f4bvPbuCE4ly27m6iI+acdcIoPnjmFC45ZQyZ0cGtdSskZFhYt6Oe/35+I0++/i4GXH36BG4//wSmjc7vcfu9ze386a2dLFm9gxfWV9PWEWNMQSaXnTqOGWPzaeuM0doeo7Wjk9aOWHALzzTvWvbu3hZer9xDJM1470mjWXTGJC44qWTYX8jplc27+dr/vMmb7+7jjNJCvnrlKZw64dAj0OKt21HP5x5Zxdrt+7hu3kS+cuVMCrKO/Az8prYOKnc3s3V3E1t3N1EZ3m+pbaSyrpmR2emcO72E804sZuG0YopUg6G1o5O/f+wNnlz1LjeeMYl/uvpU6pra+FV5FQ+v2Erl7mZG5WZw/byJ3LhgMmXFuYPyugoJGVaq6pr48dLNPLxiKy3tMS6eOYZPXDCVuZML2dPUxu/X7uSp1dt5cUMN7Z3OuBFZXHbqOK6YPZbTJxUO+GidjdUNPFpeyeMrt1HT0EpJfibXzJ3ADfMnJbSKP9haOzrZuKuR7z+3gf99YzvjR2TxpctP5i9mj+v3L/a2jhj/9cx6vvfsBsaNyObfrp/Ne6YW97p9fUs7FTVNbKppoKKmiYraxv2hUF3fetC2eZlRJo/KYfKoHCaNyubdvS38eUMNe5raATh1QgHnTS/h3OklzJtSSEa0f0Ht7uxpamdnfQudMacgK52CrHTysqL9vlzwUFDX2MZfP7iSVyp28/eXnsQnzj/4OvaxmLN0Qw2/fHkLf3xrF50x5z1Ti/jgmZN538yx/f579UQhIcPS7sY2frqsgp8tr2BPUzvTRudRUdNIR8yZWJjN5bPGcdmpYzlt4uAcxtneGeO5ddU8sqKSZ9cFH8L5Uwq54YxJXDFrHLmZ/TulyN0T2oyyu7GNTdUNbKxuYGN1Ixt3BdOVdc10xpzMaBq3nz+V28+fesQHA7y6tY6/e/R1Ntc08pFzSrl+3iS21DayubaRzdWNVNQ2srmmkZqGtv2PMYPxI7L3B8HkohwmhdNTRuUwMif9kL9LZ8xZvW0vS9+pZun6Gl7dWkdHzMnJiHD2CUWcd2IQGPUtHeyqb2HH3hZ27mtlZ30LO/e2BPf7Wmnr1rzYJTcjQn5WOgXZUfKz0snPCu6L8zK49JSxLCgbNSSavLbUNvKR+1dQVdfMt284jStPG9/n9rv2tfBoeSUPvVLJtj3NFOdl8FfnnsDt5089otdXSMiw1tjawcMrKnn6zR3MnVzI5bPGMusIr7/RX7v2tfDr17bx6IpKNtU0kpsRYca4Ajo6g2ar9s4Y7Z1Oe2eMto4YbZ2x/dMxD84lKc7LpDgvk6K8DIrzMinJz6Q4L4Oi3EyKw+n0SBoNrR00tnbQ2NoZ3LcdmO5at7e5nc01jWysbqAu/OUNkBFN44TiXKaOzmNqeH9mWdGgXHyqqa2Dbz71Ng8s33LQ8pL8TMqKcykryqW0ODeYLs5lSlHOIZfaHaj6lnaWb6zlhfVBaGypbTpkm5yMCGMLshhdkMmYgqxwOosxBZlE04x9LR3Ut3Swr7md+pYO6lvC+9au+Q527G2hub2TyaNyuHbuRK6ZO4FJo5JzKPbKLXV8/GflxNy598PzOaN0VL8f2xlzlq6v5pcvb+XEMfl8/pKTjqgMCgmRI+TurNxSx6/Kq6isayIjmkZ6JI2MSFo4bcF8NFiWHkkjzWB3Uxs19W3UNrZS09BGTX0r9a29jk3Zp+z0CPlZUUqLc5laksfUkiAMppXkMX5kdsKbVF7dWse2umbKioNQyOtnjWowbKltZPW2vRTmZDAmDIK8zOhR/0Bobuvkd29u57GVVSzbWIs7vGdqEdfNm8ilp44lJ+PY7OOS1dv53COrGDsii59+ZMFR9TEcTQ1WISEyBLS0d1LbGARGTUNw64g5eZlRcjOi5GRGgulwPjczQk7G8GpXH46q6pp44tVtPPZqFVtqm8jNiHDF7HFcN28SZ5QWJqTG6u7cu3QT31jyNnMnj+TeD89Pase9QkJE5DDcnRUVdTy2spLfvrGdxrZOphTlcOr4EbR1xuiIa2Js74zREXPaOoL79s4Y7lCcl8Ho/ANNYSX5wf3o/ExG52dSmJNBzJ07/+dNfv7SVq6YNY5v33DaUTfTHS2FhIjIADS1dfC7NTv49avb2L63mfSwKbGrebFrOho2PUYjQW2jpqGVXfta2bmvhX0thzYvpkeMvMwodU3t/PX5J/DFS2YMibGzkjkKrIjIsJOTEeWauRO5Zu7EI36OlvZOdu1rZVd4BFbXfXV9KwunF/GB04/8uY81hYSIyCDLSo8wuSg4FHi4G96nloqISEIpJEREpFcKCRER6ZVCQkREeqWQEBGRXikkRESkVwoJERHplUJCRER6ddwMy2Fm1cCWw27Yu2KgZpCKMxRof4a+422fjrf9geNvn3ranynuXtLbA46bkDhaZlbe1/glw432Z+g73vbpeNsfOP726Uj2R81NIiLSK4WEiIj0SiFxwI+SXYBBpv0Z+o63fTre9geOv30a8P6oT0JERHqlmoSIiPRKISEiIr1K+ZAws0vNbJ2ZbTCzO5JdnsFgZhVmttrMVpnZsLumq5ndZ2a7zGxN3LJRZvYHM1sf3hcms4wD1cs+3Wlm28L3aZWZXZ7MMg6EmU0ys2fNbK2ZvWlmnwmXD8v3qY/9Gc7vUZaZvWJmr4f79LVweZmZvRx+5z1iZhl9Pk8q90mYWQR4B7gYqAJWADe5+9qkFuwomVkFMN/dh+VJQGZ2HtAA/MzdTw2X/Suw293vDsO80N2/mMxyDkQv+3Qn0ODu30pm2Y6EmY0Dxrn7q2aWD6wErgb+kmH4PvWxPzcwfN8jA3LdvcHM0oEXgc8Afwv82t0fNrMfAq+7+w96e55Ur0ksADa4+yZ3bwMeBq5KcplSnru/AOzutvgq4IFw+gGCD/Cw0cs+DVvuvt3dXw2n64G3gAkM0/epj/0ZtjzQEM6mhzcHLgQeC5cf9j1K9ZCYAFTGzVcxzP8xQg783sxWmtltyS7MIBnj7tvD6R3AmGQWZhB9yszeCJujhkXTTHdmVgqcDrzMcfA+ddsfGMbvkZlFzGwVsAv4A7AR2OPuHeEmh/3OS/WQOF4tdPe5wGXAJ8OmjuOGB22kx0M76Q+AqcAcYDvw7eQWZ+DMLA94HPisu++LXzcc36ce9mdYv0fu3unuc4CJBC0nMwb6HKkeEtuASXHzE8Nlw5q7bwvvdwFPEPxzDHc7w3bjrvbjXUkuz1Fz953hhzgG3Mswe5/Cdu7HgV+4+6/DxcP2feppf4b7e9TF3fcAzwJnAyPNLBquOux3XqqHxApgetjbnwHcCCxOcpmOipnlhh1vmFku8D5gTd+PGhYWA7eG07cCTyaxLIOi68s09AGG0fsUdor+BHjL3b8Tt2pYvk+97c8wf49KzGxkOJ1NcIDOWwRhcV242WHfo5Q+ugkgPKTtP4AIcJ+7/3OSi3RUzOwEgtoDQBT45XDbJzN7CLiAYFjjncBXgd8AjwKTCYaEv8Hdh01HcC/7dAFBM4YDFcBfx7XnD2lmthBYCqwGYuHiLxO04w+796mP/bmJ4fsezSbomI4QVAgedfe7wu+Ih4FRwGvAh9y9tdfnSfWQEBGR3qV6c5OIiPRBISEiIr1SSIiISK8UEiIi0iuFhIiI9EohITIAZtYZNyLoqsEcOdjMSuNHiRUZCqKH30RE4jSHwxyIpATVJEQGQXgNj38Nr+PxiplNC5eXmtkz4QBxfzKzyeHyMWb2RDjW/+tm9p7wqSJmdm84/v/vwzNlRZJGISEyMNndmpsWxa3b6+6zgO8SnMUP8F/AA+4+G/gFcE+4/B7geXc/DZgLvBkunw58z91PAfYA1yZ4f0T6pDOuRQbAzBrcPa+H5RXAhe6+KRwoboe7F5lZDcHFbNrD5dvdvdjMqoGJ8cMhhENU/8Hdp4fzXwTS3f3rid8zkZ6pJiEyeLyX6YGIH0OnE/UbSpIpJEQGz6K4++Xh9DKC0YUBbiYYRA7gT8AnYP+FYUYcq0KKDIR+pYgMTHZ4pa8uv3P3rsNgC83sDYLawE3hsr8B7jezLwDVwEfC5Z8BfmRmHyOoMXyC4KI2IkOK+iREBkHYJzHf3WuSXRaRwaTmJhER6ZVqEiIi0ivVJEREpFcKCRER6ZVCQkREeqWQEBGRXikkRESkV/8f9Q/+PCJ7+joAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(res['fit_hist']['loss'], label='train')\n", "plt.plot(res['fit_hist']['val_loss'], label='val')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Loss')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "uGJ9Xkx_fe-I" }, "source": [ "Plot the test results:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 223 }, "id": "rTuFVEKLA_iM", "outputId": "05a3be24-e44d-4043-8467-d73a91714d41" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# t0, t1 = 1_020_000, 1_040_000 # dmel tutorial dataset\n", "t0, t1 = 40_000, 60_000\n", "\n", "plt.figure(figsize=(40, 8))\n", "plt.subplot(311)\n", "plt.plot(res['x_test'][t0:t1], 'k')\n", "plt.ylabel('Audio')\n", "\n", "plt.subplot(312)\n", "plt.plot(res['y_test'][t0:t1, 1:])\n", "plt.ylabel('Prediction targets')\n", "\n", "plt.subplot(313)\n", "plt.plot(res['y_pred'][t0:t1, 1:])\n", "plt.ylabel('Predictions')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "i3eXlLRsg891" }, "source": [ "You can download the model results via the file tab on the left, from `/contest/res`" ] }, { "cell_type": "markdown", "metadata": { "id": "YaBJ-gzZfqKo" }, "source": [ "## Predict on new data\n", "Load a new recording for prediction" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9MouG9Z8B8nl", "outputId": "7a48dd7d-1895-4e87-a3cd-38a04b772a92" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data:\n", " test:\n", " x: (386000, 16)\n", " y: (386000, 2)\n", " val:\n", " y: (386000, 2)\n", " x: (386000, 16)\n", " train:\n", " y: (1158000, 2)\n", " x: (1158000, 16)\n", "\n", "Attributes:\n", " class_names: ['noise', 'pulse']\n", " class_types: ['segment', 'event']\n", " filename_endsample_test: []\n", " filename_endsample_train: []\n", " filename_endsample_val: []\n", " filename_startsample_test: []\n", " filename_startsample_train: []\n", " filename_startsample_val: []\n", " filename_train: []\n", " filename_val: []\n", " samplerate_x_Hz: 10000.0\n", " samplerate_y_Hz: 10000.0\n", " filename_test: []\n", "\n" ] } ], "source": [ "model, params = das.utils.load_model_and_params(res_name) # load the model and runtime parameters\n", "ds = das.npy_dir.load('/content/drive/MyDrive/Dmoj.wrigleyi.npy', memmap_dirs=['train','val']) # load the new data\n", "print(ds)\n", "x = ds['test']['x']" ] }, { "cell_type": "markdown", "metadata": { "id": "U0CTfLSQgMxV" }, "source": [ "Run inference - this will calculate the confidence score and extract segment boundaries and event times." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AA1xaQkFDi9W", "outputId": "88fc4fe6-67a1-43ba-a388-9fc8196305b1" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2470: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n", " warnings.warn('`Model.state_updates` will be removed in a future version. '\n" ] } ], "source": [ "events, segments, class_probabilities, _ = das.predict.predict(x, model=model, params=params, verbose=1)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 420 }, "id": "mx3ejHmMDzPO", "outputId": "4af7de93-2356-4aee-9af7-357e1b7778ef" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t0, t1 = 42_000, 48_000\n", "plt.figure(figsize=(20, 8))\n", "plt.plot(x[t0:t1, 0], alpha=0.25, c='k', label='Audio')\n", "plt.plot(class_probabilities[t0:t1, 1:], label='Prediction')\n", "plt.xlim(0, t1-t0)\n", "plt.legend(['Audio', 'Prediction'])\n", "plt.show()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "colab.ipynb", "provenance": [], "toc_visible": true }, "file_extension": ".py", "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": "3.8.11" }, "mimetype": "text/x-python", "name": "python", "npconvert_exporter": "python", "pygments_lexer": "ipython3", "version": 3 }, "nbformat": 4, "nbformat_minor": 4 }