updated docs

docs/jupyter_execute/tutorials/continue_training.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import das.utils, das.data, das.train, das.io, das.evaluate, das.predict\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "import flammkuchen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the model\n",
+    "Change `model_savename` - if the model is saved at `res/20220601_103543_model.h5`, then `model_savename=\"res/20220601_103543\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_savename = 'res/20220601_103543'\n",
+    "model, params = das.utils.load_model_and_params(model_savename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = das.io.load(params['data_dir'], x_suffix=params['x_suffix'], y_suffix=params['y_suffix'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prep the data for tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gen = das.data.AudioSequence(data['train']['x'],\n",
+    "                                  data['train']['y'],\n",
+    "                                  shuffle=True,\n",
+    "                                  **params)\n",
+    "val_gen = das.data.AudioSequence(data['val']['x'],\n",
+    "                             data['val']['y'],\n",
+    "                             shuffle=False,\n",
+    "                             **params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prep callbacks\n",
+    "The new model will be saved to `model_savename + \"_continued_model.h5\"`, in our example \"res/20220601_103543_continued_model.h5\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "das.utils.save_params(params, model_savename + \"_continued\")\n",
+    "\n",
+    "checkpoint_save_name = model_savename + \"_continued_model.h5\"  # this will overwrite intermediates from previous epochs\n",
+    "callbacks = [\n",
+    "    ModelCheckpoint(checkpoint_save_name, save_best_only=True, save_weights_only=False, monitor='val_loss', verbose=1),\n",
+    "    EarlyStopping(monitor='val_loss', patience=20, verbose=1),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_hist = model.fit(\n",
+    "    data_gen,\n",
+    "    epochs=params['nb_epoch'],\n",
+    "    steps_per_epoch=min(len(data_gen), 100),\n",
+    "    verbose=params['verbose'],\n",
+    "    validation_data=val_gen,\n",
+    "    callbacks=callbacks,\n",
+    "    class_weight=params['class_weights'],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f'   Re-loading last best model from {checkpoint_save_name}.')\n",
+    "model.load_weights(checkpoint_save_name)\n",
+    "\n",
+    "print('   Predicting.')\n",
+    "x_test, y_test, y_pred = das.evaluate.evaluate_probabilities(x=data['test']['x'], y=data['test']['y'], model=model, params=params)\n",
+    "\n",
+    "labels_test = das.predict.labels_from_probabilities(y_test)\n",
+    "labels_pred = das.predict.labels_from_probabilities(y_pred)\n",
+    "\n",
+    "print('   Evaluating.')\n",
+    "conf_mat, report = das.evaluate.evaluate_segments(labels_test, labels_pred, params['class_names'], report_as_dict=True)\n",
+    "print(conf_mat)\n",
+    "print(report)\n",
+    "\n",
+    "save_filename = \"{0}_results.h5\".format(model_savename + \"_continued\")\n",
+    "print(f'   Saving to {save_filename}.')\n",
+    "ddd = {\n",
+    "    # 'fit_hist': fit_hist.history,\n",
+    "    'confusion_matrix': conf_mat,\n",
+    "    'classification_report': report,\n",
+    "    'x_test': x_test,\n",
+    "    'y_test': y_test,\n",
+    "    'y_pred': y_pred,\n",
+    "    'labels_test': labels_test,\n",
+    "    'labels_pred': labels_pred,\n",
+    "    'params': params,\n",
+    "}\n",
+    "flammkuchen.save(save_filename, ddd)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "7ea0ec616133ead53c1908c8f6539f5c0cb9b2f78368e2bb6ab3f847e89ca400"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.9 ('base')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+ "nbformat": 4,
+ "nbformat_minor": 2
+}