mirror of
https://git.gfz-potsdam.de/naaice/model-training.git
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953 lines
878 KiB
Plaintext
953 lines
878 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## General Information"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook is used to train a simple neural network model to predict the chemistry in the barite benchmark (50x50 grid). The training data is stored in the repository using **git large file storage** and can be downloaded after the installation of git lfs using the `git lfs pull` command.\n",
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"\n",
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"It is then recommended to create a Python environment using miniconda. The necessary dependencies are contained in `environment.yml` and can be installed using `conda env create -f environment.yml`.\n",
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"\n",
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"The data set is divided into a design and result part and consists of the iterations of a reference simulation. The design part of the data set contains the chemical concentrations at time $t$ and the result part at time $t+1$, which are to be learned by the model."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Chemical problem"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the Barite benchmark, the system is initially in equilibrium. In iteration 0, barium is added to the system at various points, causing celestite to dissolve and barium to react with the sulphates to form barite, which precipitates.\n",
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"\n",
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"\\begin{equation}\n",
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"\t\\underbrace{Ba^{2+}_{}}_{\\text{Barium}} + \\underbrace{SrSO_4}_{\\text{Celestite}} \\rightarrow \\underbrace{BaSO_4^{2-}}_{\\text{Barite}} + \\underbrace{Sr^{2+}_{}}_{\\text{Strontium}}\n",
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"\\end{equation}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup Libraries"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"All necessary preprocessing steps are outsourced to `preprocessing.py` in order to keep the notebook as compact as possible."
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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": 1,
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"metadata": {},
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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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"2025-02-26 17:33:56.161717: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
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"2025-02-26 17:33:56.182881: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
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"To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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}
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],
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"source": [
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"from preprocessing import *\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.colors as mcolors\n",
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"import matplotlib.patches as mpatches"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Read data from `.h5` file and convert it to a `pandas.DataFrame`"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Various data sets are available for training, each of which contains the PHREEQC simulation data. The data simulates the Barite benchmark, but under different starting conditions.\n",
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"\n",
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"- `Barite_50_Data_training.h5`: Simulates a total of 1000 iterations, with barium being abandoned at one corner. The data set also contains all 1000 iterations\n",
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"- `barite_50_4_corner.h5`: Simulates a total of 5000 iterations, with barium being abandoned at all 4 corners. Contains every 20th iteration.\n",
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"- `barite_50_4_corner_20k.h5`: Simulates a total of 20000 iterations, whereby barium is abandoned at all 4 corners and also contains all 20000 iterations (not in the LFS, as too large for git)\n",
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"\n",
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"The datasets are located under `./datasets` (must first be pulsed with git lfs)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"| Dataset | # cells | non-reactive portion | reactive portion |\n",
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"|---------|--------------|----------------------|---------------|\n",
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"| 1-corner 1k Iterations | 2.502.500 | 0,988 | 0,012 |\n",
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"| 4-corner 5k Iterations | 630.000 | 0,952 | 0,048 |\n",
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"| 4-corner 20k Iterations | 50.000.000 | 0,867 | 0,133 |\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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# load data and differentiate between design and results (before and after a simulation step)\n",
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"data_file = h5py.File(\"../datasets/barite_50_4_corner.h5\")\n",
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"\n",
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"design = data_file[\"design\"]\n",
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"results = data_file[\"result\"]\n",
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"\n",
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"df_design = pd.DataFrame(\n",
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" np.array(design[\"data\"]).transpose(), columns=np.array(design[\"names\"].asstr())\n",
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")\n",
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"df_results = pd.DataFrame(\n",
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" np.array(results[\"data\"]).transpose(), columns=np.array(results[\"names\"].asstr())\n",
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")\n",
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"\n",
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"data_file.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Preprocess Data\n",
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"\n",
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"The data are preprocessed in the following way:\n",
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"\n",
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"1. Label data points in the `design` dataset with `reactive` and `non-reactive` labels using kmeans clustering\n",
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"2. **Optional:** Transform `design` and `results` data set into log-scaled data.\n",
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"3. Split data into training and test sets.\n",
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"4. Learn scaler (MinMax-Scaling or standardization) on training data for `design` and `results` together (option `global`) or individual (option `individual`).\n",
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"5. Transform training and test data with the learned scaler.\n",
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"6. Split training data into training and validation dataset."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The dataset contains more chemical species than we need (i.e. charge or ionized forms). That's why we need to select the species we want to train on."
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"species_columns = [\"H\", \"O\", \"Ba\", \"Cl\", \"S\", \"Sr\", \"Barite\", \"Celestite\"]"
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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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"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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"/home/signer/bin/miniconda3/envs/training/lib/python3.11/site-packages/sklearn/base.py:1473: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
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" return fit_method(estimator, *args, **kwargs)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Amount class 0 before: 0.9521309523809524\n",
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"Amount class 1 before: 0.04786904761904762\n",
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"No sampling selected. Output equals input.\n"
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]
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}
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],
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"source": [
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"preprocess = preprocessing() #np.log1p, np.expm1\n",
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"X, y = preprocess.cluster(df_design[species_columns], df_results[species_columns])\n",
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"\n",
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"# optional: perform log transformation\n",
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"# X, y = preprocess.funcTranform(X, y)\n",
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"\n",
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"X_train, X_test, y_train, y_test = preprocess.split(X, y, ratio=0.2)\n",
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"X_train_origin = X_train.copy()\n",
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"X_train, y_train = preprocess.balancer(X_train, y_train, strategy=\"off\")\n",
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"X_train, y_train = preprocess.class_selection(X_train, y_train, class_label=0)\n",
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"preprocess.scale_fit(X_train, y_train, scaling=\"global\", type=\"standard\")\n",
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"X_train, X_test, y_train, y_test = preprocess.scale_transform(\n",
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" X_train, X_test, y_train, y_test\n",
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")\n",
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"X_train, X_val, y_train, y_val = preprocess.split(X_train, y_train, ratio=0.1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For a better understanding, we can first visualise the barite distribution for different iterations."
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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": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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AAAAAAAAAAJSCIgcAAAAAAAAAoBQUOQAAAAAAAAAApaDIAQAAAAAAAAAoBUUOAAAAAAAAAEAp/F/obpiJtbOkkwAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 3000x600 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"timesteps = [1, 20, 30, 40, 251]\n",
|
||
"fig, axes = plt.subplots(1, 5, figsize=(20, 4), dpi=150)\n",
|
||
"\n",
|
||
"for i, timestep in enumerate(timesteps):\n",
|
||
" ax = axes[i]\n",
|
||
" im = ax.imshow(\n",
|
||
" np.array(X[\"Barite\"][(timestep * 2500) : (timestep * 2500 + 2500)]).reshape(\n",
|
||
" 50, 50\n",
|
||
" ),\n",
|
||
" origin=\"lower\",\n",
|
||
" cmap=\"viridis\",\n",
|
||
" )\n",
|
||
" ax.set_title(f\"Iteration {timestep}\")\n",
|
||
" ax.axis(\"off\")\n",
|
||
"\n",
|
||
"fig.colorbar(im, ax=axes.ravel().tolist(), label=\"Barite Concentration\")\n",
|
||
"plt.suptitle(\"Barite Distribution for Different Timesteps\", fontsize=14)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Train the model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Tensorflow has problems processing DataFrames in custom loss function, so that the elements cannot be called with `df.loc[:, ‘Barite’]`, but only via indices. We therefore have to extract the indices of the chemical species into a dictionary."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"column_dict = {}\n",
|
||
"for i in species_columns:\n",
|
||
" column_dict[i] = X.columns.get_loc(i)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"In the following we define all the necessary functions and parameters that are required for training."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def model_training(model, batch_size=512, epochs=100):\n",
|
||
" start = time.time()\n",
|
||
" callback = keras.callbacks.EarlyStopping(monitor=\"loss\", patience=30)\n",
|
||
" history = model.fit(\n",
|
||
" X_train.loc[:, X_train.columns != \"Class\"],\n",
|
||
" y_train.loc[:, y_train.columns != \"Class\"],\n",
|
||
" batch_size=batch_size,\n",
|
||
" epochs=epochs,\n",
|
||
" validation_data=(\n",
|
||
" X_val.loc[:, X_val.columns != \"Class\"],\n",
|
||
" y_val.loc[:, y_val.columns != \"Class\"],\n",
|
||
" ),\n",
|
||
" callbacks=[callback],\n",
|
||
" )\n",
|
||
"\n",
|
||
" end = time.time()\n",
|
||
"\n",
|
||
" print(\"Training took {} seconds\".format(end - start))\n",
|
||
"\n",
|
||
" return history"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# select model architecture\n",
|
||
"model = model_definition(\"large\")\n",
|
||
"\n",
|
||
"# define learning rate adaptation\n",
|
||
"lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n",
|
||
" initial_learning_rate=0.001, decay_steps=2000, decay_rate=0.9, staircase=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# hyperparameters that are determined by hyperparameter optimization\n",
|
||
"h1 = 0.16726490480995826\n",
|
||
"h2 = 0.5283208497548787\n",
|
||
"h3 = 0.5099528144902471\n",
|
||
"\n",
|
||
"\n",
|
||
"scaler_type = \"standard\"\n",
|
||
"loss_variant = \"huber_mass_balance\"\n",
|
||
"delta = 1.7642791340966357\n",
|
||
"\n",
|
||
"\n",
|
||
"optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)\n",
|
||
"model.compile(\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss=custom_loss(preprocess, column_dict, h1, h2, h3, scaler_type, loss_variant, 1),\n",
|
||
" metrics=[\n",
|
||
" huber_metric(delta),\n",
|
||
" mass_balance_metric(preprocess, column_dict, scaler_type),\n",
|
||
" ],\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 0.0159 - loss: 0.0082 - mass_balance: 0.0116 - val_huber: 2.1637e-04 - val_loss: 0.0023 - val_mass_balance: 0.0043\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.7016e-04 - loss: 0.0014 - mass_balance: 0.0027 - val_huber: 1.7522e-04 - val_loss: 0.0013 - val_mass_balance: 0.0025\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.0684e-04 - loss: 0.0012 - mass_balance: 0.0022 - val_huber: 3.5211e-04 - val_loss: 0.0022 - val_mass_balance: 0.0042\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 2.1493e-04 - loss: 0.0011 - mass_balance: 0.0020 - val_huber: 2.2204e-04 - val_loss: 0.0010 - val_mass_balance: 0.0019\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 15ms/step - huber: 1.3008e-04 - loss: 7.9274e-04 - mass_balance: 0.0015 - val_huber: 4.4501e-05 - val_loss: 3.6538e-04 - val_mass_balance: 6.8946e-04\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.1257e-04 - loss: 6.6159e-04 - mass_balance: 0.0012 - val_huber: 1.0521e-04 - val_loss: 6.6072e-04 - val_mass_balance: 0.0012\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.1593e-04 - loss: 6.9253e-04 - mass_balance: 0.0013 - val_huber: 4.6279e-05 - val_loss: 3.6958e-04 - val_mass_balance: 6.9983e-04\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 9.9661e-05 - loss: 5.9260e-04 - mass_balance: 0.0011 - val_huber: 7.7692e-05 - val_loss: 3.9290e-04 - val_mass_balance: 7.3339e-04\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 7.3264e-05 - loss: 4.7501e-04 - mass_balance: 8.9288e-04 - val_huber: 9.2113e-05 - val_loss: 8.4957e-04 - val_mass_balance: 0.0016\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 8.6489e-05 - loss: 5.8290e-04 - mass_balance: 0.0011 - val_huber: 3.1637e-05 - val_loss: 5.8915e-04 - val_mass_balance: 0.0011\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 5.9110e-05 - loss: 4.3897e-04 - mass_balance: 8.2805e-04 - val_huber: 3.6716e-05 - val_loss: 3.1306e-04 - val_mass_balance: 5.9171e-04\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 27ms/step - huber: 5.8969e-05 - loss: 4.6788e-04 - mass_balance: 8.8358e-04 - val_huber: 7.4076e-05 - val_loss: 7.7939e-04 - val_mass_balance: 0.0015\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 5.9244e-05 - loss: 3.9023e-04 - mass_balance: 7.3465e-04 - val_huber: 5.9634e-05 - val_loss: 3.9370e-04 - val_mass_balance: 7.3867e-04\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 20ms/step - huber: 5.8721e-05 - loss: 3.8836e-04 - mass_balance: 7.3155e-04 - val_huber: 3.3829e-05 - val_loss: 2.5489e-04 - val_mass_balance: 4.8263e-04\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 4.8019e-05 - loss: 3.4845e-04 - mass_balance: 6.5770e-04 - val_huber: 3.0502e-05 - val_loss: 2.2406e-04 - val_mass_balance: 4.2018e-04\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 4.8281e-05 - loss: 3.4026e-04 - mass_balance: 6.4149e-04 - val_huber: 5.5124e-05 - val_loss: 4.3510e-04 - val_mass_balance: 8.2050e-04\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 3.8080e-05 - loss: 3.1863e-04 - mass_balance: 6.0295e-04 - val_huber: 3.5506e-05 - val_loss: 6.1022e-04 - val_mass_balance: 0.0012\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 3.6879e-05 - loss: 2.8393e-04 - mass_balance: 5.3626e-04 - val_huber: 3.7765e-05 - val_loss: 4.1528e-04 - val_mass_balance: 7.8552e-04\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 15ms/step - huber: 3.2695e-05 - loss: 2.8065e-04 - mass_balance: 5.3144e-04 - val_huber: 1.8064e-05 - val_loss: 1.9093e-04 - val_mass_balance: 3.6273e-04\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - huber: 3.1337e-05 - loss: 2.5379e-04 - mass_balance: 4.7992e-04 - val_huber: 2.5578e-05 - val_loss: 2.9832e-04 - val_mass_balance: 5.6782e-04\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 3.7086e-05 - loss: 2.7006e-04 - mass_balance: 5.0938e-04 - val_huber: 2.2522e-05 - val_loss: 2.2766e-04 - val_mass_balance: 4.3007e-04\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 21ms/step - huber: 2.8414e-05 - loss: 2.2388e-04 - mass_balance: 4.2329e-04 - val_huber: 1.7697e-05 - val_loss: 1.7123e-04 - val_mass_balance: 3.2254e-04\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 16ms/step - huber: 3.0425e-05 - loss: 2.3615e-04 - mass_balance: 4.4640e-04 - val_huber: 1.9530e-05 - val_loss: 1.9458e-04 - val_mass_balance: 3.6947e-04\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.5037e-05 - loss: 1.9369e-04 - mass_balance: 3.6621e-04 - val_huber: 1.9878e-05 - val_loss: 2.9097e-04 - val_mass_balance: 5.5204e-04\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.6450e-05 - loss: 2.2114e-04 - mass_balance: 4.1811e-04 - val_huber: 3.3484e-05 - val_loss: 5.1009e-04 - val_mass_balance: 9.7122e-04\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 2.8268e-05 - loss: 1.9281e-04 - mass_balance: 3.6375e-04 - val_huber: 1.4985e-05 - val_loss: 1.1141e-04 - val_mass_balance: 2.0968e-04\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 2.8420e-05 - loss: 1.9706e-04 - mass_balance: 3.7174e-04 - val_huber: 2.0493e-05 - val_loss: 1.5578e-04 - val_mass_balance: 2.9476e-04\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 2.1046e-05 - loss: 1.6831e-04 - mass_balance: 3.1860e-04 - val_huber: 2.5317e-05 - val_loss: 1.6482e-04 - val_mass_balance: 3.1080e-04\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 2.2267e-05 - loss: 1.7674e-04 - mass_balance: 3.3441e-04 - val_huber: 9.3458e-06 - val_loss: 1.1187e-04 - val_mass_balance: 2.1313e-04\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 1.9771e-05 - loss: 1.5978e-04 - mass_balance: 3.0248e-04 - val_huber: 1.1929e-05 - val_loss: 1.1887e-04 - val_mass_balance: 2.2595e-04\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 2.2051e-05 - loss: 1.6235e-04 - mass_balance: 3.0656e-04 - val_huber: 1.7063e-05 - val_loss: 1.8251e-04 - val_mass_balance: 3.4744e-04\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 1.8538e-05 - loss: 1.7150e-04 - mass_balance: 3.2539e-04 - val_huber: 1.3060e-05 - val_loss: 1.0948e-04 - val_mass_balance: 2.0754e-04\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 2.3532e-05 - loss: 1.4467e-04 - mass_balance: 2.7262e-04 - val_huber: 7.6551e-06 - val_loss: 8.5257e-05 - val_mass_balance: 1.6184e-04\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.7583e-05 - loss: 1.5367e-04 - mass_balance: 2.9137e-04 - val_huber: 8.2259e-06 - val_loss: 9.4663e-05 - val_mass_balance: 1.7979e-04\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 1.8602e-05 - loss: 1.3293e-04 - mass_balance: 2.5101e-04 - val_huber: 8.5681e-06 - val_loss: 1.1647e-04 - val_mass_balance: 2.2283e-04\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 25ms/step - huber: 1.5507e-05 - loss: 1.3234e-04 - mass_balance: 2.5097e-04 - val_huber: 1.2965e-05 - val_loss: 2.4758e-04 - val_mass_balance: 4.7178e-04\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 2.7496e-05 - loss: 1.3054e-04 - mass_balance: 2.4429e-04 - val_huber: 1.4207e-05 - val_loss: 1.0057e-04 - val_mass_balance: 1.9007e-04\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 1.7117e-05 - loss: 1.1822e-04 - mass_balance: 2.2331e-04 - val_huber: 8.3411e-06 - val_loss: 1.0676e-04 - val_mass_balance: 2.0202e-04\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 1.9310e-05 - loss: 1.1455e-04 - mass_balance: 2.1555e-04 - val_huber: 9.0852e-06 - val_loss: 1.2996e-04 - val_mass_balance: 2.4633e-04\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 15ms/step - huber: 2.3680e-05 - loss: 1.1200e-04 - mass_balance: 2.0969e-04 - val_huber: 7.7604e-06 - val_loss: 8.3377e-05 - val_mass_balance: 1.5779e-04\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.3581e-05 - loss: 1.0753e-04 - mass_balance: 2.0348e-04 - val_huber: 6.4747e-06 - val_loss: 7.6034e-05 - val_mass_balance: 1.4468e-04\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step - huber: 1.4963e-05 - loss: 9.3135e-05 - mass_balance: 1.7541e-04 - val_huber: 5.8801e-06 - val_loss: 5.5339e-05 - val_mass_balance: 1.0464e-04\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 1.4002e-05 - loss: 1.0034e-04 - mass_balance: 1.8957e-04 - val_huber: 6.0165e-06 - val_loss: 5.2093e-05 - val_mass_balance: 9.8668e-05\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 21ms/step - huber: 1.3720e-05 - loss: 8.8099e-05 - mass_balance: 1.6604e-04 - val_huber: 6.4475e-06 - val_loss: 1.4406e-04 - val_mass_balance: 2.7444e-04\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 2.0150e-05 - loss: 9.2090e-05 - mass_balance: 1.7219e-04 - val_huber: 4.7389e-06 - val_loss: 5.7199e-05 - val_mass_balance: 1.0862e-04\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 1.1187e-05 - loss: 7.6198e-05 - mass_balance: 1.4389e-04 - val_huber: 5.7339e-06 - val_loss: 1.1307e-04 - val_mass_balance: 2.1630e-04\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.1631e-05 - loss: 7.7719e-05 - mass_balance: 1.4674e-04 - val_huber: 5.1228e-06 - val_loss: 9.2185e-05 - val_mass_balance: 1.7662e-04\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 8.6178e-06 - loss: 7.7370e-05 - mass_balance: 1.4671e-04 - val_huber: 4.4743e-06 - val_loss: 8.0649e-05 - val_mass_balance: 1.5478e-04\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 1.2652e-05 - loss: 6.9353e-05 - mass_balance: 1.3021e-04 - val_huber: 4.3246e-06 - val_loss: 6.8228e-05 - val_mass_balance: 1.3036e-04\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 1.8886e-05 - loss: 7.5731e-05 - mass_balance: 1.4089e-04 - val_huber: 4.6933e-06 - val_loss: 5.2718e-05 - val_mass_balance: 1.0036e-04\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 8.6954e-06 - loss: 6.2883e-05 - mass_balance: 1.1879e-04 - val_huber: 5.7759e-06 - val_loss: 7.6331e-05 - val_mass_balance: 1.4537e-04\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 21ms/step - huber: 9.6971e-06 - loss: 6.1626e-05 - mass_balance: 1.1608e-04 - val_huber: 3.5665e-06 - val_loss: 5.3019e-05 - val_mass_balance: 1.0099e-04\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 8.4618e-06 - loss: 5.6582e-05 - mass_balance: 1.0672e-04 - val_huber: 4.0161e-06 - val_loss: 1.1739e-04 - val_mass_balance: 2.2385e-04\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 8.6070e-06 - loss: 6.1078e-05 - mass_balance: 1.1538e-04 - val_huber: 3.9764e-06 - val_loss: 7.8183e-05 - val_mass_balance: 1.5002e-04\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 8.3744e-06 - loss: 5.2668e-05 - mass_balance: 9.9188e-05 - val_huber: 4.8194e-06 - val_loss: 6.0945e-05 - val_mass_balance: 1.1645e-04\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 8.0430e-06 - loss: 5.5005e-05 - mass_balance: 1.0381e-04 - val_huber: 4.2099e-06 - val_loss: 4.2308e-05 - val_mass_balance: 8.0290e-05\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 9.8632e-06 - loss: 4.6309e-05 - mass_balance: 8.6417e-05 - val_huber: 3.7704e-06 - val_loss: 5.2539e-05 - val_mass_balance: 1.0076e-04\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 1.1049e-05 - loss: 4.8602e-05 - mass_balance: 9.0530e-05 - val_huber: 4.5764e-06 - val_loss: 4.2829e-05 - val_mass_balance: 8.1037e-05\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 7.8099e-06 - loss: 4.4020e-05 - mass_balance: 8.2638e-05 - val_huber: 3.7273e-06 - val_loss: 3.4894e-05 - val_mass_balance: 6.5669e-05\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 7.3985e-06 - loss: 4.2755e-05 - mass_balance: 8.0336e-05 - val_huber: 3.5656e-06 - val_loss: 5.2735e-05 - val_mass_balance: 1.0131e-04\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 5.7896e-06 - loss: 4.4404e-05 - mass_balance: 8.3940e-05 - val_huber: 3.3344e-06 - val_loss: 4.1619e-05 - val_mass_balance: 7.9748e-05\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 7.6573e-06 - loss: 4.2019e-05 - mass_balance: 7.8827e-05 - val_huber: 2.9571e-06 - val_loss: 3.8318e-05 - val_mass_balance: 7.3062e-05\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 7.0246e-06 - loss: 4.1996e-05 - mass_balance: 7.8925e-05 - val_huber: 3.2846e-06 - val_loss: 4.7290e-05 - val_mass_balance: 9.0100e-05\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 7.1768e-06 - loss: 4.0670e-05 - mass_balance: 7.6273e-05 - val_huber: 3.2443e-06 - val_loss: 3.0751e-05 - val_mass_balance: 5.8160e-05\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - huber: 8.2322e-06 - loss: 3.9525e-05 - mass_balance: 7.3858e-05 - val_huber: 3.6928e-06 - val_loss: 4.9247e-05 - val_mass_balance: 9.3333e-05\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 21ms/step - huber: 7.3951e-06 - loss: 3.3935e-05 - mass_balance: 6.3241e-05 - val_huber: 2.6322e-06 - val_loss: 2.9614e-05 - val_mass_balance: 5.6361e-05\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 7.9427e-06 - loss: 3.1778e-05 - mass_balance: 5.8965e-05 - val_huber: 3.0384e-06 - val_loss: 3.5186e-05 - val_mass_balance: 6.6310e-05\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 9.2122e-06 - loss: 3.3397e-05 - mass_balance: 6.1652e-05 - val_huber: 2.6337e-06 - val_loss: 3.0151e-05 - val_mass_balance: 5.7459e-05\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 20ms/step - huber: 7.5230e-06 - loss: 3.1762e-05 - mass_balance: 5.9018e-05 - val_huber: 3.2956e-06 - val_loss: 7.7759e-05 - val_mass_balance: 1.4897e-04\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 5.1066e-06 - loss: 3.0846e-05 - mass_balance: 5.7952e-05 - val_huber: 2.6449e-06 - val_loss: 2.7740e-05 - val_mass_balance: 5.2617e-05\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 13ms/step - huber: 5.9822e-06 - loss: 2.8337e-05 - mass_balance: 5.2830e-05 - val_huber: 3.2052e-06 - val_loss: 2.3249e-05 - val_mass_balance: 4.3688e-05\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 6.0954e-06 - loss: 2.8623e-05 - mass_balance: 5.3352e-05 - val_huber: 2.6696e-06 - val_loss: 3.7623e-05 - val_mass_balance: 7.1514e-05\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 20ms/step - huber: 6.5221e-06 - loss: 2.8740e-05 - mass_balance: 5.3428e-05 - val_huber: 2.3854e-06 - val_loss: 2.0875e-05 - val_mass_balance: 3.9654e-05\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 23ms/step - huber: 5.3176e-06 - loss: 2.7184e-05 - mass_balance: 5.0835e-05 - val_huber: 2.4816e-06 - val_loss: 3.5760e-05 - val_mass_balance: 6.7804e-05\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 5.0432e-06 - loss: 2.4995e-05 - mass_balance: 4.6679e-05 - val_huber: 2.5025e-06 - val_loss: 1.9573e-05 - val_mass_balance: 3.6922e-05\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 4.8276e-06 - loss: 2.2201e-05 - mass_balance: 4.1353e-05 - val_huber: 2.4585e-06 - val_loss: 1.7460e-05 - val_mass_balance: 3.2908e-05\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 4.4530e-06 - loss: 2.2517e-05 - mass_balance: 4.2087e-05 - val_huber: 2.6383e-06 - val_loss: 2.1215e-05 - val_mass_balance: 4.0054e-05\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 4.7436e-06 - loss: 2.0644e-05 - mass_balance: 3.8389e-05 - val_huber: 2.7656e-06 - val_loss: 1.8618e-05 - val_mass_balance: 3.4989e-05\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 14ms/step - huber: 8.6071e-06 - loss: 2.1559e-05 - mass_balance: 3.8975e-05 - val_huber: 2.4785e-06 - val_loss: 1.9744e-05 - val_mass_balance: 3.7424e-05\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 4.8273e-06 - loss: 2.0366e-05 - mass_balance: 3.7813e-05 - val_huber: 2.3683e-06 - val_loss: 2.3192e-05 - val_mass_balance: 4.3858e-05\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 4.1429e-06 - loss: 1.9682e-05 - mass_balance: 3.6682e-05 - val_huber: 2.4579e-06 - val_loss: 2.3470e-05 - val_mass_balance: 4.4667e-05\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 4.3346e-06 - loss: 1.8738e-05 - mass_balance: 3.4823e-05 - val_huber: 2.2863e-06 - val_loss: 1.6274e-05 - val_mass_balance: 3.0652e-05\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 4.6412e-06 - loss: 1.7737e-05 - mass_balance: 3.2783e-05 - val_huber: 2.2080e-06 - val_loss: 1.5864e-05 - val_mass_balance: 2.9908e-05\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 17ms/step - huber: 6.0757e-06 - loss: 1.9005e-05 - mass_balance: 3.4790e-05 - val_huber: 2.2955e-06 - val_loss: 1.9878e-05 - val_mass_balance: 3.7724e-05\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 5.0746e-06 - loss: 1.8464e-05 - mass_balance: 3.4061e-05 - val_huber: 2.2392e-06 - val_loss: 1.8274e-05 - val_mass_balance: 3.4456e-05\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 6.1811e-06 - loss: 1.7876e-05 - mass_balance: 3.2582e-05 - val_huber: 2.2513e-06 - val_loss: 1.8889e-05 - val_mass_balance: 3.5718e-05\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 3.5479e-06 - loss: 1.5930e-05 - mass_balance: 2.9635e-05 - val_huber: 2.2526e-06 - val_loss: 1.6175e-05 - val_mass_balance: 3.0466e-05\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 18ms/step - huber: 7.3204e-06 - loss: 1.6691e-05 - mass_balance: 2.9917e-05 - val_huber: 2.1545e-06 - val_loss: 1.3727e-05 - val_mass_balance: 2.5782e-05\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 23ms/step - huber: 3.5428e-06 - loss: 1.4906e-05 - mass_balance: 2.7661e-05 - val_huber: 2.1938e-06 - val_loss: 1.4968e-05 - val_mass_balance: 2.8242e-05\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 4.8572e-06 - loss: 1.5157e-05 - mass_balance: 2.7730e-05 - val_huber: 2.1789e-06 - val_loss: 1.7663e-05 - val_mass_balance: 3.3284e-05\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 22ms/step - huber: 5.7642e-06 - loss: 1.5536e-05 - mass_balance: 2.8178e-05 - val_huber: 2.1638e-06 - val_loss: 1.2848e-05 - val_mass_balance: 2.4079e-05\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 21ms/step - huber: 5.3907e-06 - loss: 1.3979e-05 - mass_balance: 2.5303e-05 - val_huber: 2.1050e-06 - val_loss: 1.5735e-05 - val_mass_balance: 2.9686e-05\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 5.6190e-06 - loss: 1.4178e-05 - mass_balance: 2.5596e-05 - val_huber: 2.1393e-06 - val_loss: 1.3981e-05 - val_mass_balance: 2.6364e-05\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 6.4031e-06 - loss: 1.4058e-05 - mass_balance: 2.5123e-05 - val_huber: 2.1212e-06 - val_loss: 1.2008e-05 - val_mass_balance: 2.2475e-05\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 13ms/step - huber: 4.3502e-06 - loss: 1.4156e-05 - mass_balance: 2.5949e-05 - val_huber: 2.0968e-06 - val_loss: 1.0900e-05 - val_mass_balance: 2.0334e-05\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 15ms/step - huber: 3.4378e-06 - loss: 1.2550e-05 - mass_balance: 2.3135e-05 - val_huber: 2.1139e-06 - val_loss: 1.3909e-05 - val_mass_balance: 2.6132e-05\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 4.0237e-06 - loss: 1.3306e-05 - mass_balance: 2.4419e-05 - val_huber: 2.1032e-06 - val_loss: 1.0320e-05 - val_mass_balance: 1.9246e-05\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 22ms/step - huber: 4.7400e-06 - loss: 1.2574e-05 - mass_balance: 2.2770e-05 - val_huber: 2.1382e-06 - val_loss: 1.1723e-05 - val_mass_balance: 2.1962e-05\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 5.2539e-06 - loss: 1.2562e-05 - mass_balance: 2.2590e-05 - val_huber: 2.1173e-06 - val_loss: 1.4093e-05 - val_mass_balance: 2.6535e-05\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m886/886\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 24ms/step - huber: 4.6950e-06 - loss: 1.2635e-05 - mass_balance: 2.2899e-05 - val_huber: 2.0971e-06 - val_loss: 1.0790e-05 - val_mass_balance: 2.0159e-05\n",
|
||
"Training took 1548.7767686843872 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"history = model_training(model, epochs=100)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Plot trained models"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"As a first visualisation step, we can plot the metrics over the epochs to display the Huber loss and the mass balance. In general, we assume that a prediction is valid if the mass balance is less than $10^{-5}$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_metrics(*histories):\n",
|
||
" fig, axes = plt.subplots(1, 2, figsize=(20, 10), dpi=150)\n",
|
||
"\n",
|
||
" for history in histories:\n",
|
||
" axes[0].plot(history.history[\"val_mass_balance\"], label=\"Mass balance metric\")\n",
|
||
" axes[0].grid()\n",
|
||
" axes[0].legend(loc=\"upper right\", fontsize=18)\n",
|
||
" axes[0].set_xlabel(\"Epoch\", fontsize=18)\n",
|
||
" axes[0].set_ylabel(\"Mass balance metric\", fontsize=18)\n",
|
||
" axes[0].set_title(\"Mass balance metric over epochs\", fontsize=20, pad=15)\n",
|
||
" axes[0].tick_params(axis=\"both\", which=\"major\", labelsize=16)\n",
|
||
" axes[0].set_yscale(\"log\")\n",
|
||
"\n",
|
||
" for history in histories:\n",
|
||
" axes[1].plot(history.history[\"val_huber\"], label=\"Huber metric\")\n",
|
||
" axes[1].grid()\n",
|
||
" axes[1].legend(loc=\"upper right\", fontsize=18)\n",
|
||
" axes[1].set_xlabel(\"Epoch\", fontsize=18)\n",
|
||
" axes[1].set_ylabel(\"Huber metric\", fontsize=18)\n",
|
||
" axes[1].set_title(\"Huber metric over epochs\", fontsize=20, pad=15)\n",
|
||
" axes[1].tick_params(axis=\"both\", which=\"major\", labelsize=16)\n",
|
||
" axes[1].set_yscale(\"log\")\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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Dhg2z25a3t7f69OmjPn36SJLS09O1bt06ff/99/rqq6+UkJAg6WLi+5133mnOOu8ucXFxSk9Pt8wo7kh0dLT53NYPCmbPnm0eU29vby1btkyDBw92qh/uUpLngrtVqlTJ7EdkZGSR26tcubKl/L///a9Idyu43OQ+Pt27d9fvv//uwd6UrEqVKlnKZ86c8VBPAAAAAAAAAJQGtu4MW5DCzo4dFRXldN3c4xJS/mTnvHHw+fPnq1u3boXqF6zyHtu1a9eqRo0aHupNycsdRz979qwHe+J5jkfYAAAAAAAASonAwEDdeOONZjnnVnl169bV1VdfXeh2V65cKcMwJF0MpE6bNq3ApGRJ+WZBL4zatWtr0qRJ2rBhg959913L377++muX2vL391e/fv30wQcf6NChQ2ratKn5t61bt+rAgQNF7m9uWVlZ2rdvn9N19+/fb5YbN26cr86KFSvM5wMHDnQqQf3cuXNmMr47ePJcKKpWrVqZz7du3Vrk9mrWrKng4GCznPvOBZCaNWtmPs898/6VICwszDKYs337dg/2BgAAAAAAAICn5Z4129nk88Im7u7atcvpurknO5JkGTexVSYO7j65Y+jSlXdsc4/ZHDx48Iq+IylJ6gAAAAAA4JKRMwN5buPHjy/ULB05Tp06ZT6vUqWKatasWeA6aWlp+vvvvwu9TVv+85//qG3btma5KImvVatW1RtvvGFZVhyJtM7OHr169Wqlpqaa5Q4dOuSrk/t1yH0cHFm5cqVT9ZxVWs6Fwujdu7f5/OzZs1q3bl2R2vPx8VGvXr3M8k8//VSk9i43/fr1M5+fPHnS7YnaeW8pm52d7db2i8LLy0s9e/Y0yytWrCj0rEcAAAAAAAAALn25756aO87uSO67y7oiPDzc6bs7/vbbb+ZzPz8/tWvXzvL3qlWrqmXLlmaZOLj7dOjQQSEhIWa5OI6tr6+v+bw0xdAl65hNVlaWfv75Zw/2xrNIUgfcID0zW+dSsnU4Lku7T8V7ujsAAAAAcNnq1auXBg8erLZt25qP8ePHF6nNnJmzpYsJx8744osvFBcXV6Tt2pJ71o6MjAy3teWO9myZM2eOsrKyCqz36aefms99fX01aNCgfHVyvw65E9odyTv7fFGVpnPBVaNHj7YEZKdNm1bkNseOHWs+Dw8P16+//lrkNi8XgwcPtgy85P1RSFEFBgZayvHxpSvelPvciI+P1+zZsz3YGwC4yDAMnbvg3Oc3AAAAAABwn9yzZu/cudOpGP+cOXMKta3s7GzNmjWrwHrR0dFasmSJWR48eLDKlCmTr17uWOeyZcuuuBm/i4uPj49Gjx5tlufMmaOoqCi3biN3HL20xdAHDx5suSPp+++/79R42uWIJHXADb5Yf0xPrU3T65vTNWP5IU93BwAAAAAua7/99pt27NhhPurVq1ek9urUqWM+j4+P19q1ax3Wj4iI0OTJk51q+9ixY073wzAMy20q8+5XbGysEhMTnW5v586dlnJRj5Mt+/fv1zvvvOOwzsqVK/Xdd9+Z5WuvvVbVqlXLVy/36/Drr78WGKybMWOG1q9f72KPHSvOc6G41alTRzfeeKNZ/umnn/TJJ58Uqc2xY8eqQYMGZvnuu+/WiRMnXGrD2R8cXGqCgoL00EMPmeWFCxcWKlHb3vHJe73mvS2tp40ZM8Zyvbz22mvatm2bB3sE4EqXnW1ozMwN6vTqcr35235PdwcAAAAAgCtKly5dzOdJSUmaP3++w/qLFi3Sn3/+WejtTZs2TUePHnVY54knntCFCxfM8t13322z3oMPPqjg4GBJFxPgb775ZpcTni/XOHhRTZ48WT4+PpKkxMRE3XLLLS5PqOTo2OaOo5e2GHpAQIAmTZpklsPDw/Xcc895sEeeQ5I64AZVg//vV1ZRiXzoAAAAAMClpH///pYZqCdOnGg3uXzdunXq3bu34uLi5O1dcFhlwoQJ6t27txYsWKDk5GS79TIzM/XEE09o37595rKRI0da6oSHh6tOnTp6+umnC5zJY9euXXriiSfMclhYmDp16lRgf12Rs/+TJ0/WzJkzbdZZsWKFbrjhBnOGcn9/f7322ms26w4ePNh8fujQIT300EPKzMzMVy8jI0OvvfaaHnvsMUs/3KE4z4WSMH36dFWuXNks33///XrxxReVkpJid50DBw7o7rvvtpnw7+vrq88++8wMIp86dUpdunTRTz/9ZJl13pY9e/bomWeeUcOGDQu5N6Xf5MmT1apVK7N811136emnn1ZCQoLD9WJjYzVnzhx16tTJMpNPbi1atFBQUJBZnjJlis6ePeuWfruDr6+vZs6cKS8vL0lScnKy+vfv73DwyTAMrV69WgMHDiypbgK4guyNTNCWY+clSXPWHSvwcwoAAAAAALhP//79LZPTPPnkkwoPD7dZ99tvv9Vtt91mxhZd5e3trcTERA0ZMkR79+7N9/eMjAw9/vjj+vLLL81lvXr10rXXXmuzvdDQUL3//vtmedeuXerSpYv+/vtvh/0wDENbtmzRgw8+qJ49exZqXy53jRs31gsvvGCWV6xYoV69elkmbLIlKytLq1ev1u23364xY8bYrde1a1fz+YIFC7Ry5cqid9qNnn32WTVu3NgsT5s2Tffee6/Du/OeOHFCTz75pGXyp0udb8FVABSkalBZ83lUArcTBQAAAIBLSdWqVXXnnXeaidaHDx9Wy5YtNWbMGHXu3Flly5ZVZGSkli9frtWrV0uSatWqpWuvvVYff/yxw7YNw9DatWu1du1aBQQEqFu3burUqZNq1qyp4OBgJSUlaf/+/Vq6dKn+/fdfc702bdpowoQJ+dqLi4vTtGnTNG3aNDVp0kRdu3ZVy5YtVbFiRXl7e+vMmTNav369fvvtN8tM5G+//bbbE6lr166tPn366Msvv9SkSZP06aefauTIkQoLC1NsbKz+/PNP/fnnn5YksZdfflnNmze3mcR711136b///a+io6MlSR9//LGWL1+uG2+8UQ0bNlRaWpoOHjyoH374wTxW99xzj37//XfLsSuK4jwXSkJYWJi++eYbjRgxQmlpaTIMQ6+88opmzpyp4cOHq1WrVqpQoYISExN16NAhrVu3zpz9+tZbb7XZZv/+/TVjxgz95z//kWEYioyM1IgRI9SkSRMNGDBATZs2VXBwsFJSUhQTE6M9e/Zo06ZNioiIkCSbt0+9XAQEBOiHH35Q7969FRkZKcMwNG3aNH300UcaPHiwOnXqZP5oIC4uTkeOHNGOHTu0ZcsWmz/AyM3Pz0+33XabPvroI0nSqlWrVLNmTdWtW1dBQUHmAE7NmjX1yy+/FO+O2jFkyBBNnTpVU6ZMkXTx7gM333yzpk6dqmHDhqlBgwYqX768YmNjtXv3bq1cuVLHjx/3SF8BXP7OXfi/uHxKRpbOJ2eoYoC/B3sEAAAAAMCVw9fXV88//7x598lz586pc+fOGjt2rLp06aIyZcro33//1bJly7R9+3ZJ0iuvvGJJYHbW3XffrSVLlujQoUPq0KGDRo4cqe7du6t8+fI6cuSIFi5caJllPTQ0VJ999pnDpPjbb79du3fv1vTp0yVdnNylV69eateunfr3768GDRooMDBQFy5cUHR0tHbt2qWNGzfq9OnTkqSmTZu6vB9XihdffFH79u3TggULJEmbNm1SmzZt1L17d/Xu3Vt16tRR+fLllZCQoDNnzig8PFwbNmxQTEyMJOsER3lNmDBBM2fOlGEYSkpK0tVXX61KlSqpRo0a5uQ7kvT555+7fSInZwQGBmrhwoXq16+fmZj+6aefasGCBbrmmmvUoUMHVapUScnJyYqIiNDGjRu1fv16GYahOXPmlHh/iwtJ6oAbVMs1k3r0hTRlZRvy8S7cr70AAAAAACXv7bff1o4dO7Rp0yZJF2cFnjt3rubOnZuvbs2aNfXjjz9q6dKlLm0jKSlJy5cv1/Llyx3Wa9u2rX799Vf5+fk5rHfw4EEdPHjQYR0fHx+98847DmeaKIqPPvpIx44d019//aVt27aZCc+2PPnkk5o8ebLdvwcFBWnRokUaOnSoOev8oUOH9Prrr9usP2LECL3//vtq0qRJ0XYij5I4F4rToEGDtHz5cl1//fVmwn9UVJRmz55d6DYffPBB1apVS3fccYf5AwNnzj9JlkDw5ahRo0baunWrrr/+evOcSUxM1KJFi7Ro0aIC13d0fF5//XVt2rRJ//zzj6SLt5rNSf7P4WjGlZLw4osvqnz58nr22WfN27QeOHBABw4c8Gi/AFx54pKtt4o+HZdCkjoAAAAAACXo/vvv15o1a8y4aHp6ur788kvLjOY57rvvPj3//POFSlKvXr26Fi5cqOuuu05xcXFasGCBmQCdV3BwsH755RenksjffPNN1atXT48++qjS09MlSTt27NCOHTsKXPdyj4MXhZeXl+bNm6fGjRvr9ddfV3Z2tiRp/fr1Nu/wmpejY3vVVVfp5Zdf1osvvmhOmBQTE2MmuOe4cOFCEfagaNq1a6c//vhDY8aMMe/cGx8fr3nz5mnevHke61dJKh33IgYucVWD/28m9axsQzFJzKYOAAAAAJeS8uXLa/Xq1XriiSdUvnx5m3UCAgI0YcIEhYeHq0OHDk61++qrr+qBBx5wKpG6QYMGmj59urZs2aIaNWrk+3v79u317rvvavDgwQoMDHTYlr+/v0aNGqVt27aZM5cUh4CAAK1YsUIvvPCCKlWqZLNOo0aN9OOPP+rNN98ssL3evXtr8+bN6tevn906DRs21Mcff6wff/xR/v7uTz4rrnOhJPXs2VOHDh3Ss88+q6pVqzqs26RJE7388stq3769w3ojR47U0aNH9cwzz6h69eoO6/r5+alHjx6aNm2aU4nsl7qaNWtq/fr1mj9/vjp37lzgbWobNGig++67T2vXrtWNN95ot15ISIjWr1+vOXPmaMSIEapXr54CAgIKfRvc4nLPPfdo8+bNuvXWW+1eM9LFwYQ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|
||
"text/plain": [
|
||
"<Figure size 3000x1500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_metrics(history)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Test Mass Balance"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Valid and invalid region in the grid"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"To check how high the proportion of cells that fulfil the mass balance is, we first output the corresponding proportion. Furthermore, we differentiate between how many cells from the non-reactive area (class 0) and reactive area (class 1) fulfil the mass balance. It is to be expected that the model can predict cells in which not much happens, i.e. in which the reaction has not yet taken place, better than the complex chemistry."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1m14175/14175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 1ms/step\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'overall': 0.8228659611992946,\n",
|
||
" 'class_0': 0.8640359305004109,\n",
|
||
" 'class_1': 0.001662433618102055}"
|
||
]
|
||
},
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"results = mass_balance_evaluation(model, X_train, preprocess)\n",
|
||
"mass_balance_ratio(results, threshold=1e-5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Next, we want to visualise which cells fulfil the mass balance depending on the respective iteration. To do this, we must first take our original dataset and scale it (X_train cannot be used because it is also shuffled due to the division into training and testing) in order to then predict it with our model."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1m19688/19688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 1ms/step\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# scale our original data to use the trained model and predict the mass balance\n",
|
||
"input_scaled = preprocess.scaler_X.transform(X.iloc[:, X.columns != \"Class\"])\n",
|
||
"input_scaled = pd.concat([pd.DataFrame(input_scaled, columns=X.columns[X.columns != \"Class\"]), X[\"Class\"]], axis=1)\n",
|
||
"results_origin = mass_balance_evaluation(model, input_scaled, preprocess)\n",
|
||
"\n",
|
||
"# extract the mass balance for the grid per iteration\n",
|
||
"mass_balance_per_iteration = []\n",
|
||
"for i in range(0, int(len(results_origin) / 2500)):\n",
|
||
" mass_balance_per_iteration.append(\n",
|
||
" results_origin[\"mass_balance\"][(i * 2500) : ((i + 1) * 2500)]\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_mass_balance(X, iteration=80):\n",
|
||
" \n",
|
||
" bounds = [0, 1e-5, 1e-4]\n",
|
||
" colors = [\"green\", \"red\"]\n",
|
||
" cmap = mcolors.ListedColormap(colors)\n",
|
||
" norm = mcolors.BoundaryNorm(bounds, cmap.N)\n",
|
||
"\n",
|
||
" # Create plot\n",
|
||
" fig, ax = plt.subplots(figsize=(15, 10))\n",
|
||
" im = ax.imshow(\n",
|
||
" np.array(mass_balance_per_iteration[iteration]).reshape(50, 50),\n",
|
||
" cmap=cmap,\n",
|
||
" norm=norm,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Manual legend with colored boxes\n",
|
||
" legend_patches = [\n",
|
||
" mpatches.Patch(color=\"green\", label=\"valid\"),\n",
|
||
" mpatches.Patch(color=\"red\", label=\"invalid\"),\n",
|
||
" ]\n",
|
||
"\n",
|
||
" ax.legend(handles=legend_patches, loc=\"center\", fontsize=14)\n",
|
||
" ax.set_title(\n",
|
||
" f\"Valid and Invalid Areas by Mass Balance (Iteration {iteration})\", fontsize=18, pad=10\n",
|
||
" )\n",
|
||
"\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"plot_mass_balance(results_origin, iteration=5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"As expected, the reactive regions of the model do not fulfil the mass balance threshold of $10^{-5}$ and are therefore invalid."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Prediction vs Results in comparison with the mass balance"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Next, we look at how the prediction and the actual result behave for selected cells and compare this with the corresponding mass balance. To do this, we plot the above values for three different cells (1, 43 and 1250). Cell 1 is the cell where barium is added, cell 43 is a little further away and should rise with a corresponding delay and cell 1250 is relatively in the centre and should therefore hardly react."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_predictions_and_mass_balance(model, cell_indices, species=\"Ba\", iterations=250):\n",
|
||
" fig, axes = plt.subplots(2, len(cell_indices), figsize=(20, 10), dpi=150)\n",
|
||
"\n",
|
||
" for j, cell_offset in enumerate(cell_indices):\n",
|
||
" y_design = []\n",
|
||
" y_results = []\n",
|
||
"\n",
|
||
" df_design_transformed_scaled = preprocess.scaler_X.transform(df_design[species_columns])\n",
|
||
" df_results_transformed_scaled = preprocess.scaler_y.transform(df_results[species_columns])\n",
|
||
"\n",
|
||
" for i in range(0, iterations):\n",
|
||
" idx = i * 50 * 50 + cell_offset - 1\n",
|
||
" y_design.append(df_design_transformed_scaled.iloc[idx, :])\n",
|
||
" y_results.append(df_results_transformed_scaled.iloc[idx, :])\n",
|
||
"\n",
|
||
" y_design = pd.DataFrame(y_design, columns=species_columns)\n",
|
||
" y_results = pd.DataFrame(y_results, columns=species_columns)\n",
|
||
"\n",
|
||
" prediction = model.predict(y_design)\n",
|
||
" prediction = pd.DataFrame(prediction, columns=y_results.columns)\n",
|
||
"\n",
|
||
" y_design_back = pd.DataFrame(preprocess.scaler_y.inverse_transform(y_design), columns=species_columns)\n",
|
||
" y_results_back = pd.DataFrame(preprocess.scaler_y.inverse_transform(y_results), columns=species_columns)\n",
|
||
" prediction_back = pd.DataFrame(preprocess.scaler_X.inverse_transform(prediction), columns=species_columns)\n",
|
||
"\n",
|
||
" # Plot predictions and results\n",
|
||
" axes[0, j].plot(np.arange(0, iterations), y_results_back[species], label=\"Results\")\n",
|
||
" axes[0, j].plot(np.arange(0, iterations), prediction_back[species], label=\"Prediction\")\n",
|
||
" axes[0, j].legend()\n",
|
||
" axes[0, j].set_xlabel(\"Iteration\", fontsize=12, labelpad=10)\n",
|
||
" axes[0, j].set_ylabel(\"Concentration\", fontsize=12)\n",
|
||
" axes[0, j].set_title(f\"{species} concentration profile in cell {cell_offset}\", fontsize=14, pad=10)\n",
|
||
" axes[0, j].grid()\n",
|
||
" axes[0, j].legend(fontsize=12)\n",
|
||
"\n",
|
||
" # Calculate and plot mass balance\n",
|
||
" mass_balance = np.abs(\n",
|
||
" (prediction_back[\"Ba\"] + prediction_back[\"Barite\"]) - (y_results_back[\"Ba\"] + y_results_back[\"Barite\"])\n",
|
||
" ) + np.abs(\n",
|
||
" (prediction_back[\"Sr\"] + prediction_back[\"Celestite\"]) - (y_results_back[\"Sr\"] + y_results_back[\"Celestite\"])\n",
|
||
" )\n",
|
||
" axes[1, j].plot(np.arange(0, iterations), mass_balance)\n",
|
||
" axes[1, j].set_xlabel(\"Iteration\", fontsize=14, labelpad=10)\n",
|
||
" axes[1, j].set_title(f\"Mass Balance in cell {cell_offset}\", fontsize=14, pad=10)\n",
|
||
" axes[1, j].axhline(y=1e-5, color=\"r\", linestyle=\"--\", label=\"Threshold 1e-5\")\n",
|
||
" axes[1, j].set_yscale(\"log\")\n",
|
||
" axes[1, j].grid()\n",
|
||
" axes[1, j].legend(fontsize=12)\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
|
||
"\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 3000x1500 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_predictions_and_mass_balance(model, [1, 43, 1250], species=\"Ba\", iterations=250)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"The result is as expected. In cell 1, the barium concentration rises abruptly and remains relatively stable over time. In cell 43, barium only increases by diffusion after approx. 175 iterations and in cell 1250 the barium concentration remains de facto constant (note the scale!). Looking at the mass balance, it quickly becomes clear that in most cases this is also only fulfilled in the non-reactive cell 1250."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Test the model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Finally, we print out the respective metrics (total loss, Huber loss and mass balance) for the corresponding test data set (X_test) and again see that all loss metrics perform significantly better for the non-reactive case than for the reactive case."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def test_model(model, X_test, y_test):\n",
|
||
" \n",
|
||
" X_test.reset_index(inplace=True, drop=True)\n",
|
||
" y_test.reset_index(inplace=True, drop=True)\n",
|
||
" all = model.evaluate(X_test.loc[:, X_test.columns != \"Class\"], y_test.loc[:, y_test.columns != \"Class\"])\n",
|
||
" class_0 = model.evaluate(X_test[X_test[\"Class\"] == 0].iloc[:, X_test.columns != \"Class\"], y_test[X_test[\"Class\"] == 0].iloc[:, y_test.columns != \"Class\"])\n",
|
||
" class_1 = model.evaluate(\n",
|
||
" X_test[X_test[\"Class\"] == 1].iloc[:, :-1], y_test[X_test[\"Class\"] == 1].iloc[:, :-1])\n",
|
||
" \n",
|
||
" print(\"metric all data: \", all)\n",
|
||
" print(\"metric class 0: \", class_0)\n",
|
||
" print(\"metric class 1: \", class_1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 67,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1m3938/3938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 1ms/step - huber: 3.0615e-06 - loss: 1.0808e-05 - mass_balance: 1.9887e-05\n",
|
||
"\u001b[1m3747/3747\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1ms/step - huber: 2.1582e-06 - loss: 5.5914e-06 - mass_balance: 1.0110e-05\n",
|
||
"\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - huber: 2.0602e-05 - loss: 1.1211e-04 - mass_balance: 2.0973e-04\n",
|
||
"metric all data: [1.0805763849930372e-05, 3.288454536232166e-06, 1.980934939638246e-05]\n",
|
||
"metric class 0: [5.6999147091119085e-06, 2.409552052995423e-06, 1.0236796697427053e-05]\n",
|
||
"metric class 1: [0.00011092334898421541, 2.0596029571606778e-05, 0.00020741194020956755]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"test_model(model, X_test, y_test)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "training",
|
||
"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": "3.11.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|