{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## General Information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "\n", "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", "\n", "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-01-15 14:03:45.613137: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1736946225.710416 5544 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1736946225.737063 5544 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2025-01-15 14:03:45.952881: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running Keras in version 3.7.0\n" ] } ], "source": [ "import keras\n", "print(\"Running Keras in version {}\".format(keras.__version__))\n", "\n", "import h5py\n", "import numpy as np\n", "import pandas as pd\n", "import time\n", "import sklearn.model_selection as sk\n", "import matplotlib.pyplot as plt\n", "from sklearn.cluster import KMeans\n", "from imblearn.over_sampling import SMOTE\n", "from imblearn.under_sampling import RandomUnderSampler\n", "from imblearn.over_sampling import RandomOverSampler\n", "from collections import Counter\n", "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "I0000 00:00:1736946248.679800 5544 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13393 MB memory: -> device: 0, name: NVIDIA A2, pci bus id: 0000:82:00.0, compute capability: 8.6\n" ] } ], "source": [ "dtype = \"float32\"\n", "activation = \"relu\"\n", "\n", "lr = 0.001\n", "batch_size = 512\n", "epochs = 50 # default 400 epochs\n", "\n", "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n", " initial_learning_rate=lr,\n", " decay_steps=2000,\n", " decay_rate=0.9,\n", " staircase=True\n", ")\n", "\n", "optimizer = keras.optimizers.Adam(learning_rate=lr_schedule)\n", "loss = keras.losses.Huber()\n", "\n", "sample_fraction = 0.8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup the model" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Model: \"sequential\"\n",
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       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (Dense)                   │ (None, 128)            │         1,664 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (Dense)                 │ (None, 128)            │        16,512 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (Dense)                 │ (None, 12)             │         1,548 │\n",
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\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,724\u001b[0m (77.05 KB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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Model: \"sequential_1\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_3 (Dense)                 │ (None, 512)            │         6,656 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (Dense)                 │ (None, 1024)           │       525,312 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (Dense)                 │ (None, 512)            │       524,800 │\n",
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\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,062,924\u001b[0m (4.05 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model_large = keras.Sequential(\n", " [keras.layers.Input(shape=(12,), dtype=dtype),\n", " keras.layers.Dense(512, activation='relu', dtype=dtype),\n", " keras.layers.Dense(1024, activation='relu', dtype=dtype),\n", " keras.layers.Dense(512, activation='relu', dtype=dtype),\n", " keras.layers.Dense(12, dtype=dtype)\n", " ])\n", "\n", "model_large.compile(optimizer=optimizer, loss = loss)\n", "model_large.summary()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define some functions and helper classes" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def Safelog(val):\n", " # get range of vector\n", " if val > 0:\n", " return np.log10(val)\n", " elif val < 0:\n", " return -np.log10(-val)\n", " else:\n", " return 0\n", "\n", "def Safeexp(val):\n", " if val > 0:\n", " return -10 ** -val\n", " elif val < 0:\n", " return 10 ** val\n", " else:\n", " return 0\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# ? Why does the charge is using another logarithm than the other species\n", "\n", "func_dict_in = {\n", " \"H\" : np.log1p,\n", " \"O\" : np.log1p,\n", " \"Charge\" : Safelog,\n", " \"H_0_\" : np.log1p,\n", " \"O_0_\" : np.log1p,\n", " \"Ba\" : np.log1p,\n", " \"Cl\" : np.log1p,\n", " \"S_2_\" : np.log1p,\n", " \"S_6_\" : np.log1p,\n", " \"Sr\" : np.log1p,\n", " \"Barite\" : np.log1p,\n", " \"Celestite\" : np.log1p,\n", "}\n", "\n", "func_dict_out = {\n", " \"H\" : np.expm1,\n", " \"O\" : np.expm1,\n", " \"Charge\" : Safeexp,\n", " \"H_0_\" : np.expm1,\n", " \"O_0_\" : np.expm1,\n", " \"Ba\" : np.expm1,\n", " \"Cl\" : np.expm1,\n", " \"S_2_\" : np.expm1,\n", " \"S_6_\" : np.expm1,\n", " \"Sr\" : np.expm1,\n", " \"Barite\" : np.expm1,\n", " \"Celestite\" : np.expm1,\n", "}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read data from `.h5` file and convert it to a `pandas.DataFrame`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n", "data_file = h5py.File(\"Barite_50_Data_training.h5\")\n", "\n", "design = data_file[\"design\"]\n", "results = data_file[\"result\"]\n", "\n", "df_design = pd.DataFrame(np.array(design[\"data\"]).transpose(), columns = design[\"names\"].asstr())\n", "df_results = pd.DataFrame(np.array(results[\"data\"]).transpose(), columns = results[\"names\"].asstr())\n", "\n", "data_file.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classify each cell with kmeans" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/mnt/beegfs/home/signer/.conda/envs/ai/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", " return fit_method(estimator, *args, **kwargs)\n" ] } ], "source": [ "# widget with slider for the index\n", "\n", "class_label_design = np.array([])\n", "class_label_result = np.array([])\n", "\n", "\n", "i = 1000\n", "for i in range(0,1001):\n", " field_design = np.array(df_design['Barite'][(i*2500):(i*2500+2500)]).reshape(50,50)\n", " field_result = np.array(df_results['Barite'][(i*2500):(i*2500+2500)]).reshape(50,50)\n", " \n", " kmeans_design = KMeans(n_clusters=2, random_state=0).fit(field_design.reshape(-1,1))\n", " kmeans_result = KMeans(n_clusters=2, random_state=0).fit(field_result.reshape(-1,1))\n", " \n", " class_label_design = np.append(class_label_design.astype(int), kmeans_design.labels_)\n", " class_label_result = np.append(class_label_result.astype(int), kmeans_result.labels_)\n", " \n", "\n", "\n", "class_label_design = pd.DataFrame(class_label_design, columns = [\"Class\"])\n", "class_label_result = pd.DataFrame(class_label_result, columns = [\"Class\"])\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "if(\"Class\" in df_design.columns and \"Class\" in df_results.columns):\n", " print(\"Class column already exists\")\n", "else:\n", " df_design = pd.concat([df_design, class_label_design], axis=1)\n", " df_results = pd.concat([df_results, class_label_design], axis=1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amount class 0: 0.9879380619380619\n", "Amount class 1: 0.012061938061938062\n" ] } ], "source": [ "counter = Counter(df_design.iloc[:,-1])\n", "print(\"Amount class 0:\", counter[0] / (counter[0] + counter[1]) )\n", "print(\"Amount class 1:\", counter[1] / (counter[0] + counter[1]) )\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "i=800\n", "\n", "plt.imshow(np.array(df_results['Barite'][(i*2500):(i*2500+2500)]).reshape(50,50), interpolation='bicubic', origin='lower')\n", "plt.contour(np.array(df_results['Class'][(i*2500):(i*2500+2500)]).reshape(50,50), levels=[0.1], colors='red', origin='lower')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split into Training and Testing datsets" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = sk.train_test_split(df_design, df_results, test_size = 0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perform Over and Under Sampling on dataset to balance classes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sampling_strategy = 'none'\n", "\n", "Counter(df_design['Class'])\n", "\n", "if sampling_strategy == 'smote':\n", " print(\"Using SMOTE strategy\")\n", " over = SMOTE(sampling_strategy=0.1)\n", " design_resampled, design_classes_resampled = over.fit_resample(X_train.iloc[:, :-1], X_train.iloc[:, -1])\n", " target_resampled, target_classes_resampled = over.fit_resample(y_train.iloc[:, :-1], y_train.iloc[:, -1])\n", " \n", "elif sampling_strategy == 'over':\n", " print(\"Using Oversampling\")\n", " over = RandomOverSampler()\n", " design_resampled, design_classes_resampled = over.fit_resample(X_train.iloc[:, :-1], X_train.iloc[:, -1])\n", " target_resampled, target_classes_resampled = over.fit_resample(y_train.iloc[:, :-1], y_train.iloc[:, -1])\n", " \n", "elif sampling_strategy == 'over/under':\n", " pass\n", "elif sampling_strategy == 'none':\n", " pass\n", "\n", "if(sampling_strategy != 'none'):\n", " X_train = pd.concat([design_resampled, design_classes_resampled], axis=1)\n", " y_train = pd.concat([target_resampled, target_classes_resampled], axis=1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amount class 0: 0.9879670329670329\n", "Amount class 1: 0.012032967032967032\n" ] } ], "source": [ "counter = Counter(X_train.iloc[:,-1])\n", "print(\"Amount class 0:\", counter[0] / (counter[0] + counter[1]) )\n", "print(\"Amount class 1:\", counter[1] / (counter[0] + counter[1]) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Scaling and Normalization Functions" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def log_scale(df_design, df_result, func_dict):\n", " \n", " df_design = df_design.copy()\n", " df_result = df_result.copy()\n", " \n", " for key in df_design.keys():\n", " if key != \"Class\":\n", " df_design[key] = np.vectorize(func_dict[key])(df_design[key])\n", " df_result[key] = np.vectorize(func_dict[key])(df_result[key])\n", " \n", " return df_design, df_result\n", "\n", "# Get minimum and maximum values for each column\n", "def get_min_max(df_design, df_result):\n", " \n", " min_vals_des = df_design.min()\n", " max_vals_des = df_design.max()\n", " \n", " min_vals_res = df_result.min()\n", " max_vals_res = df_result.max()\n", "\n", " # minimum of input and output data to get global minimum/maximum\n", " data_min = np.minimum(min_vals_des, min_vals_res).to_dict()\n", " data_max = np.maximum(max_vals_des, max_vals_res).to_dict()\n", "\n", " return data_min, data_max\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "df_design_log, df_results_log = log_scale(df_design, df_results, func_dict_in)\n", "data_min_log, data_max_log = get_min_max(df_design_log, df_results_log)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "X_train_log, y_train_log = log_scale(X_train, y_train, func_dict_in)\n", "X_test_log, y_test_log = log_scale(X_test, y_test, func_dict_in)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "train_min_log, train_max_log = get_min_max(X_train_log, y_train_log)\n", "test_min_log, test_max_log = get_min_max(X_test_log, y_test_log)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def preprocess(data, func_dict, data_min, data_max):\n", " data = data.copy()\n", " for key in data.keys():\n", " if key != \"Class\":\n", " data[key] = (data[key] - data_min[key]) / (data_max[key] - data_min[key])\n", "\n", " return data\n", "\n", "def postprocess(data, func_dict, data_min, data_max):\n", " data = data.copy()\n", " for key in data.keys():\n", " if key != \"Class\":\n", " data[key] = data[key] * (data_max[key] - data_min[key]) + data_min[key]\n", " data[key] = np.vectorize(func_dict[key])(data[key])\n", " return data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess the data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "pp_design = preprocess(df_design_log, func_dict_in, data_min_log, data_max_log)\n", "pp_results = preprocess(df_results_log, func_dict_in, data_min_log, data_max_log)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "X_train_preprocess = preprocess(X_train_log, func_dict_in, train_min_log, train_max_log)\n", "y_train_preprocess = preprocess(y_train_log, func_dict_in, train_min_log, train_max_log)\n", "\n", "X_test_preprocess = preprocess(X_test_log, func_dict_in, test_min_log, test_max_log)\n", "y_test_preprocess = preprocess(y_test_log, func_dict_in, test_min_log, test_max_log)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample the data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "X_train, X_val, y_train, y_val = sk.train_test_split(X_train_preprocess, y_train_preprocess, test_size = 0.1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom Loss function" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def custom_loss_H20(df_design_log, df_result_log, data_min_log, data_max_log, func_dict_out, postprocess):\n", " df_result = postprocess(df_result_log, func_dict_out, data_min_log, data_max_log) \n", " return keras.losses.Huber + np.sum(((df_result['H'] / df_result['O']) - 2)**2)\n", "\n", "def loss_wrapper(data_min_log, data_max_log, func_dict_out, postprocess):\n", " def loss(df_design_log, df_result_log):\n", " return custom_loss_H20(df_design_log, df_result_log, data_min_log, data_max_log, func_dict_out, postprocess)\n", " return loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1736946361.112525 5608 service.cc:148] XLA service 0x7f55280081d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1736946361.112832 5608 service.cc:156] StreamExecutor device (0): NVIDIA A2, Compute Capability 8.6\n", "2025-01-15 14:06:01.159888: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", "I0000 00:00:1736946361.348701 5608 cuda_dnn.cc:529] Loaded cuDNN version 90300\n", "2025-01-15 14:06:01.387004: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 182 configs for 6 fusions on a single thread.\n", "2025-01-15 14:06:03.621770: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 760 bytes spill stores, 708 bytes spill loads\n", "\n", "2025-01-15 14:06:10.257275: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 188 bytes spill stores, 188 bytes spill loads\n", "\n", "2025-01-15 14:06:12.491063: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170_0', 776 bytes spill stores, 724 bytes spill loads\n", "\n", "2025-01-15 14:06:14.767081: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 220 bytes spill stores, 220 bytes spill loads\n", "\n", "2025-01-15 14:06:19.678342: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 172 bytes spill stores, 384 bytes spill loads\n", "\n", "2025-01-15 14:06:28.467581: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 664 bytes spill stores, 636 bytes spill loads\n", "\n", "2025-01-15 14:06:35.654758: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 28 bytes spill stores, 28 bytes spill loads\n", "\n", "2025-01-15 14:06:37.946914: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 664 bytes spill stores, 636 bytes spill loads\n", "\n", "2025-01-15 14:06:45.130159: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 28 bytes spill stores, 28 bytes spill loads\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m 59/3520\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - loss: 0.0151 " ] }, { "name": "stderr", "output_type": "stream", "text": [ "I0000 00:00:1736946413.659915 5608 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m3510/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.7823e-04" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2025-01-15 14:07:02.606589: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 170 configs for 6 fusions on a single thread.\n", "2025-01-15 14:07:04.595726: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163_0', 756 bytes spill stores, 444 bytes spill loads\n", "\n", "2025-01-15 14:07:11.174922: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163_0', 56 bytes spill stores, 68 bytes spill loads\n", "\n", "2025-01-15 14:07:11.599286: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 12 bytes spill stores, 12 bytes spill loads\n", "\n", "2025-01-15 14:07:12.141013: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_163', 204 bytes spill stores, 204 bytes spill loads\n", "\n", "2025-01-15 14:07:14.292978: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170_0', 756 bytes spill stores, 444 bytes spill loads\n", "\n", "2025-01-15 14:07:15.902316: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 4 bytes spill stores, 4 bytes spill loads\n", "\n", "2025-01-15 14:07:16.549503: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 236 bytes spill stores, 236 bytes spill loads\n", "\n", "2025-01-15 14:07:21.435252: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 12 bytes spill stores, 12 bytes spill loads\n", "\n", "2025-01-15 14:07:22.296584: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_170', 428 bytes spill stores, 704 bytes spill loads\n", "\n", "2025-01-15 14:07:30.646925: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 776 bytes spill stores, 792 bytes spill loads\n", "\n", "2025-01-15 14:07:34.111635: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 92 bytes spill stores, 92 bytes spill loads\n", "\n", "2025-01-15 14:07:34.831349: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 88 bytes spill stores, 88 bytes spill loads\n", "\n", "2025-01-15 14:07:36.594341: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 20 bytes spill stores, 28 bytes spill loads\n", "\n", "2025-01-15 14:07:37.378454: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 32 bytes spill stores, 56 bytes spill loads\n", "\n", "2025-01-15 14:07:39.469100: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_343', 52 bytes spill stores, 72 bytes spill loads\n", "\n", "2025-01-15 14:07:42.877171: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 776 bytes spill stores, 792 bytes spill loads\n", "\n", "2025-01-15 14:07:46.407965: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 92 bytes spill stores, 92 bytes spill loads\n", "\n", "2025-01-15 14:07:47.130611: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 88 bytes spill stores, 88 bytes spill loads\n", "\n", "2025-01-15 14:07:48.883431: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 20 bytes spill stores, 28 bytes spill loads\n", "\n", "2025-01-15 14:07:49.652582: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 32 bytes spill stores, 56 bytes spill loads\n", "\n", "2025-01-15 14:07:51.729864: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_345', 52 bytes spill stores, 72 bytes spill loads\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 5.7683e-04" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2025-01-15 14:08:00.788132: W external/local_xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:868] Compiling 72 configs for 3 fusions on a single thread.\n", "2025-01-15 14:08:07.801469: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:397] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_31', 220 bytes spill stores, 220 bytes spill loads\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 25ms/step - loss: 5.7669e-04 - val_loss: 2.6423e-06\n", "Epoch 2/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - loss: 4.9671e-06 - val_loss: 2.3180e-06\n", "Epoch 3/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 3.2911e-06 - val_loss: 4.5144e-06\n", "Epoch 4/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 2.4528e-06 - val_loss: 1.0435e-06\n", "Epoch 5/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.7198e-06 - val_loss: 8.9310e-07\n", "Epoch 6/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.5762e-06 - val_loss: 6.4216e-07\n", "Epoch 7/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.1924e-06 - val_loss: 6.5215e-07\n", "Epoch 8/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.0509e-06 - val_loss: 7.6646e-07\n", "Epoch 9/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 9.9888e-07 - val_loss: 6.1071e-07\n", "Epoch 10/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 1.0026e-06 - val_loss: 5.3742e-07\n", "Epoch 11/50\n", "\u001b[1m3520/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - loss: 8.9634e-07 - val_loss: 6.5116e-07\n", "Epoch 12/50\n", "\u001b[1m2968/3520\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 9.6697e-07" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[21], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# measure time\u001b[39;00m\n\u001b[1;32m 2\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 4\u001b[0m history \u001b[38;5;241m=\u001b[39m model_large\u001b[38;5;241m.\u001b[39mfit(X_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m 5\u001b[0m y_train\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m 6\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m batch_size, \n\u001b[1;32m 7\u001b[0m epochs \u001b[38;5;241m=\u001b[39m epochs, \n\u001b[1;32m 8\u001b[0m validation_data \u001b[38;5;241m=\u001b[39m (X_val\u001b[38;5;241m.\u001b[39miloc[:,:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], y_val\u001b[38;5;241m.\u001b[39miloc[:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 11\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining took \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m seconds\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end \u001b[38;5;241m-\u001b[39m start))\n", "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:368\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[1;32m 367\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 368\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m 369\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m 370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n", "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:217\u001b[0m, in \u001b[0;36mTensorFlowTrainer._make_function..function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m 214\u001b[0m iterator, (tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mIterator, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedIterator)\n\u001b[1;32m 215\u001b[0m ):\n\u001b[1;32m 216\u001b[0m opt_outputs \u001b[38;5;241m=\u001b[39m multi_step_on_iterator(iterator)\n\u001b[0;32m--> 217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mhas_value():\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mget_value()\n", "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/tensorflow/python/data/ops/optional_ops.py:176\u001b[0m, in \u001b[0;36m_OptionalImpl.has_value\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhas_value\u001b[39m(\u001b[38;5;28mself\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mcolocate_with(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor):\n\u001b[0;32m--> 176\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gen_optional_ops\u001b[38;5;241m.\u001b[39moptional_has_value(\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor, name\u001b[38;5;241m=\u001b[39mname\n\u001b[1;32m 178\u001b[0m )\n", "File \u001b[0;32m~/.conda/envs/ai/lib/python3.11/site-packages/tensorflow/python/ops/gen_optional_ops.py:172\u001b[0m, in \u001b[0;36moptional_has_value\u001b[0;34m(optional, name)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tld\u001b[38;5;241m.\u001b[39mis_eager:\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m _result \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_FastPathExecute(\n\u001b[1;32m 173\u001b[0m _ctx, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOptionalHasValue\u001b[39m\u001b[38;5;124m\"\u001b[39m, name, optional)\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# measure time\n", "start = time.time()\n", "\n", "history = model_large.fit(X_train.iloc[:, :-1], \n", " y_train.iloc[:, :-1], \n", " batch_size = batch_size, \n", " epochs = epochs, \n", " validation_data = (X_val.iloc[:,:-1], y_val.iloc[:, :-1])\n", ")\n", "\n", "end = time.time()\n", "\n", "print(\"Training took {} seconds\".format(end - start))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(history.history[\"loss\"], \"o-\", label = \"Training Loss\")\n", "plt.xlabel(\"Epoch\")\n", "# plt.yscale('log')\n", "plt.ylabel(\"Loss (Huber)\")\n", "plt.grid('on')\n", "\n", "plt.savefig(\"loss_all.png\", dpi=300)\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(history.history[\"loss\"][1:], \"o-\", label = \"Training Loss\")\n", "plt.xlabel(\"Epoch\")\n", "# plt.yscale('log')\n", "plt.ylabel(\"Loss (Huber)\")\n", "plt.grid('on')\n", "plt.savefig(\"loss_1_to_end.png\", dpi=300)\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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HOChargeH_0_O_0_BaClS_2_S_6_SrBariteCelestiteClass
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4111.01243455.508699-1.216643e-095.350528e-312.777485e-154.049176e-078.098352e-070.000000e+000.0006200.0006200.0011.0000000
..........................................
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2502500 rows × 13 columns

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" ], "text/plain": [ " H O Charge H_0_ O_0_ \\\n", "0 111.012434 55.508192 -7.779554e-09 2.697041e-26 2.210590e-15 \n", "1 111.012434 55.508427 -4.736083e-09 1.446346e-26 2.473481e-15 \n", "2 111.012434 55.508691 -1.311169e-09 3.889826e-28 2.769320e-15 \n", "3 111.012434 55.508698 -1.220023e-09 1.442658e-29 2.777193e-15 \n", "4 111.012434 55.508699 -1.216643e-09 5.350528e-31 2.777485e-15 \n", "... ... ... ... ... ... \n", "2502495 111.012434 55.507488 3.573728e-09 5.424062e-145 1.375204e-10 \n", "2502496 111.012434 55.507501 3.494007e-09 2.011675e-146 1.377139e-10 \n", "2502497 111.012434 55.507512 3.429764e-09 7.460897e-148 1.377819e-10 \n", "2502498 111.012434 55.507520 3.381745e-09 2.767237e-149 1.371144e-10 \n", "2502499 111.012434 55.507525 3.348864e-09 5.321610e-151 1.376026e-10 \n", "\n", " Ba Cl S_2_ S_6_ Sr \\\n", "0 2.041069e-02 4.082138e-02 0.000000e+00 0.000494 0.000494 \n", "1 1.094567e-02 2.189133e-02 0.000000e+00 0.000553 0.000553 \n", "2 2.943745e-04 5.887491e-04 0.000000e+00 0.000619 0.000619 \n", "3 1.091776e-05 2.183551e-05 0.000000e+00 0.000620 0.000620 \n", "4 4.049176e-07 8.098352e-07 0.000000e+00 0.000620 0.000620 \n", "... ... ... ... ... ... \n", "2502495 9.953520e-07 2.266555e-03 5.509534e-149 0.000318 0.001450 \n", "2502496 9.817216e-07 2.217997e-03 2.043375e-150 0.000321 0.001429 \n", "2502497 9.706451e-07 2.179066e-03 7.578467e-152 0.000324 0.001412 \n", "2502498 9.621074e-07 2.149820e-03 2.810844e-153 0.000326 0.001400 \n", "2502499 9.564401e-07 2.129912e-03 5.405468e-155 0.000327 0.001391 \n", "\n", " Barite Celestite Class \n", "0 0.001 1.000000 0 \n", "1 0.001 1.000000 0 \n", "2 0.001 1.000000 0 \n", "3 0.001 1.000000 0 \n", "4 0.001 1.000000 0 \n", "... ... ... ... \n", "2502495 0.001 1.000014 0 \n", "2502496 0.001 1.000010 0 \n", "2502497 0.001 1.000006 0 \n", "2502498 0.001 1.000004 0 \n", "2502499 0.001 1.000001 0 \n", "\n", "[2502500 rows x 13 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test the model" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m15641/15641\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 336us/step - loss: 6.6414e-07\n" ] }, { "data": { "text/plain": [ "8.585521982240607e-07" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test on all test data\n", "model.evaluate(X_test.iloc[:,:-1], y_test)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m15454/15454\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 331us/step - loss: 2.7927e-07\n" ] }, { "data": { "text/plain": [ "3.939527175589319e-07" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test on non-reactive data\n", "model.evaluate(X_test[X_test['Class'] == 0].iloc[:,:-1], y_test[X_test['Class'] == 0])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m188/188\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 400us/step - loss: 3.3173e-05\n" ] }, { "data": { "text/plain": [ "3.921399184037e-05" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test on reactive data\n", "model.evaluate(X_test[X_test['Class'] == 1].iloc[:,:-1], y_test[X_test['Class'] == 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save the model" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# Save the model\n", "model.save(\"Barite_50_Model_additional_species.keras\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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 }