{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" }, "colab": { "name": "Exercise_08_2_solution.ipynb", "provenance": [], "include_colab_link": true }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "zhevd7m4MEoT" }, "source": [ "# Air-shower reconstruction using a convolutional neural network\n", "## Cosmic-ray observatory\n", "In this example, we will train a neural network to reconstruct air-shower properties measured by a hypothetic cosmic-ray observatory located at a height of 1400 m. The observatory features a cartesian array of 14 x 14 particle detectors with a distance of 750 m. \n", "Thus, the measured data contain the simulated detector responses from secondary particles of the cosmic-ray induced air shower that interact with the ground-based observatory. \n", "\n", "Each particle detector measures two quantities that are stored in the form of a cartesian image (2D array with 14 x 14 pixels)- arrival time `T`: arrival time of the first particles\n", "- signal `S`: integrated signal\n", "\n", "In this task, we are interested in reconstructing the cosmic-ray energy in EeV (exaelectronvolt = $10^{18}~\\mathrm{eV}$) using the measured particle footprint and a convolutional neural network.\n", "\n", "\n", "# Preparation:\n", "**Enable a GPU for this task.** \n", "Therefore, click:\n", "*Edit* -> *Notebook settings* -> *select GPU* as *Hardware accelerator*\n", "\n", "# References\n", "The tutorial is inspired by the simulation and the study performed in https://doi.org/10.1016/j.astropartphys.2017.10.006. \n" ] }, { "cell_type": "code", "metadata": { "id": "dc1W4fDQMEoZ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "81888f7b-9249-4925-f3d6-c33102d135ae" }, "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "import numpy as np\n", "import seaborn as sns\n", "from matplotlib import pyplot as plt\n", "\n", "layers = keras.layers\n", "\n", "print(\"tf\", tf.__version__)\n" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "keras 2.6.0\n", "tf 2.6.0\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "qlKbYg4eMEod" }, "source": [ "---\n", "# Data\n", "### Download data\n", "As a first step, we have to download the simulated air shower data." ] }, { "cell_type": "code", "metadata": { "id": "afCf7h-HMEoe" }, "source": [ "import os\n", "import gdown\n", "url = \"https://drive.google.com/u/0/uc?export=download&confirm=HgGH&id=19gOSVQ0mhdjMkm5i2u5NsGrWNBdlhR4F\"\n", "output = 'airshowers_mnist.npz'\n", "\n", "if os.path.exists(output) == False:\n", " gdown.download(url, output, quiet=False)\n", "\n", "f = np.load(output)" ], "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lpTcoB3xMEof" }, "source": [ "### Input 1: Arrival times\n", "Our first input is the arrival times of the first shower particles, measured at the various stations. We normalize the arrival times with respect to the mean and the standard deviation of the arrival time distribution. \n", "Remember that each input has the shape of 14 x 14, as each station measures a specific arrival time.\n" ] }, { "cell_type": "code", "metadata": { "id": "Im1FDnqBMEog", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3131f349-46df-463d-e698-8697cb34b2e3" }, "source": [ "# time map\n", "T = f['time']\n", "T -= np.nanmean(T)\n", "T /= np.nanstd(T)\n", "T[np.isnan(T)] = 0\n", "\n", "print(T.shape)" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(100000, 14, 14)\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "3p35M05lMEoh" }, "source": [ "#### Plot four example maps\n", "To visualize a map of arrival times, we can plot a random example event. Here, the dark blue indicates an early trigger and a bright yellow a late trigger. \n", "With this information, one can directly get an impression of the shower axis (arrival direction) of the arriving particle shower." ] }, { "cell_type": "code", "metadata": { "id": "FRw8E9H7MEoi", "colab": { "base_uri": "https://localhost:8080/", "height": 513 }, "outputId": "602c962f-a208-4871-d309-a6545efafb9d" }, "source": [ "nsamples=len(T)\n", "random_samples = np.random.choice(nsamples, 4)\n", "\n", "def rectangular_array(n=14):\n", " \"\"\" Return x,y coordinates for rectangular array with n^2 stations. \"\"\"\n", " n0 = (n - 1) / 2\n", " return (np.mgrid[0:n, 0:n].astype(float) - n0)\n", "\n", "plt.figure(figsize=(9,7))\n", "\n", "for i,j in enumerate(random_samples):\n", " plt.subplot(2,2,i+1)\n", " footprint=T[j,...]\n", " xd, yd = rectangular_array()\n", " mask = footprint != 0\n", " mask[5, 5] = True\n", " marker_size = 50 * footprint[mask]\n", " plot = plt.scatter(xd, yd, c='grey', s=10, alpha=0.3, label=\"silent\")\n", " circles = plt.scatter(xd[mask], yd[mask], c=footprint[mask], \n", " cmap=\"viridis\", alpha=1, label=\"loud\",\n", " edgecolors=\"k\", linewidths=0.3)\n", " cbar = plt.colorbar(circles)\n", " cbar.set_label('normalized time [a.u.]')\n", " plt.xlabel(\"x / km\")\n", " plt.ylabel(\"y / km\")\n", " plt.grid(True)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "kA707hclMEoj" }, "source": [ "### Input 2: Measured signals\n", "In the following, we inspect the measured signals of the detectors. Again, we have 14 x 14 measurements (a few are zero as no signal was measured) that form a single event. We process the signal using a logarithmic re-scaling.\n" ] }, { "cell_type": "code", "metadata": { "id": "41_XzMnyMEok", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "18cb81db-f1e7-4f45-d478-44f173275fce" }, "source": [ "# signal map\n", "S = f['signal']\n", "S = np.log10(1 + S)\n", "S -= np.nanmin(S)\n", "S /= np.nanmax(S)\n", "S[np.isnan(S)] = 0\n", "\n", "print(S.shape)" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(100000, 14, 14)\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "8MH4JaVhM0AA" }, "source": [ "#### Plot four example events" ] }, { "cell_type": "code", "metadata": { "id": "AAS9bA1kMEom", "colab": { "base_uri": "https://localhost:8080/", "height": 513 }, "outputId": "3861c62c-4c6f-4686-c57d-4e0336cfc3bf" }, "source": [ "plt.figure(figsize=(9,7))\n", "\n", "for i,j in enumerate(random_samples):\n", " plt.subplot(2,2,i+1)\n", " footprint=S[j,...]\n", " xd, yd = rectangular_array()\n", " mask = footprint != 0\n", " mask[5, 5] = True\n", " marker_size = 130 * footprint[mask] + 20\n", " plot = plt.scatter(xd, yd, c='grey', s=10, alpha=0.4, label=\"silent\")\n", " circles = plt.scatter(xd[mask], yd[mask], c=footprint[mask], s=marker_size,\n", " cmap=\"autumn_r\", alpha=1, label=\"loud\",\n", " edgecolors=\"k\", linewidths=0.4)\n", " cbar = plt.colorbar(circles)\n", " cbar.set_label('normalized signals [a.u.]')\n", " plt.xlabel(\"x / km\")\n", " plt.ylabel(\"y / km\")\n", " plt.grid(True)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "5xk9pp_2MEon" }, "source": [ "### Labels\n", "Now, we can prepare the targets of our neural network training." ] }, { "cell_type": "code", "metadata": { "id": "8BbUO5g0MEon" }, "source": [ "axis = f['showeraxis']" ], "execution_count": 7, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "SELMHC0xMEoo" }, "source": [ "core = f['showercore'][:, 0:2]\n", "core /= 750" ], "execution_count": 8, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "t3TMaOzfMEoo", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5232447a-86d7-40ad-86ea-28df3bd56a9b" }, "source": [ "# energy - log10(E/eV) in range 3 - 100 EeV\n", "energy = f['energy']\n", "print(energy)" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[ 4.00461776 5.99159294 7.49367551 ... 10.81657107 5.07542798\n", " 43.32910954]\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "7AXjf80PRHnx" }, "source": [ "---\n", "#### Training and test data\n", "We further split the data into one large training set and a smaller test set. This test set is used for the final evaluation of the model and is only used once to ensure an unbiased performance measure.\n", "\n", "\n", "Before, we combine our input `X` by concatenating the maps of arrival times with the maps of signals." ] }, { "cell_type": "code", "metadata": { "id": "LRWu-MIBMEon" }, "source": [ "X = np.stack([T, S], axis=-1)" ], "execution_count": 10, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "djv28NqyMEop" }, "source": [ "X_train, X_test = np.split(X, [-20000])\n", "energy_train, energy_test = np.split(energy, [-20000])" ], "execution_count": 11, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hPuYYDR1MEoq" }, "source": [ "---\n", "## Define Model\n", "Now, we will set up a neural network to reconstruct the energy of the particle shower. In this **define step** we will set the architecture of the model.\n", "\n", "\n", "> **Modification section** \n", "> Feel free to modify the model.\n", "> For example:\n", "> * Change the number of nodes (but remember that the number of weights scales with n x n. Also, the final layer has to have only one node as we are reconstructing the energy, which is a scalar.).\n", "> * Change the activation function, e.g., use `relu, tanh, sigmoid, softplus, elu, ` (take care to not use an activation function for the final layer!).\n", "> * Add new layers convolutional layers.\n", "> * Add new pooling layers, followed by other convolutional layers.\n", "> * Add fully-connected layers (remember to use `Flatten()` before).\n", "> * Increase the Dropout fraction or place Dropout between several layers if you observe overtraining (validation loss increases).\n", "\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "osFl4FlNMEor" }, "source": [ "model = keras.models.Sequential(name=\"energy_regression_CNN\")\n", "kwargs = dict(kernel_initializer=\"he_normal\", padding=\"same\",)\n", "\n", "model.add(layers.Conv2D(32, (3, 3), activation=\"elu\", input_shape=X_train.shape[1:], **kwargs))\n", "model.add(layers.Conv2D(32, (3, 3), activation=\"elu\", **kwargs))\n", "model.add(layers.MaxPooling2D((2, 2)))\n", "model.add(layers.Conv2D(64, (3, 3), activation=\"elu\", **kwargs))\n", "model.add(layers.Conv2D(64, (3, 3), activation=\"elu\", **kwargs))\n", "model.add(layers.MaxPooling2D((2, 2)))\n", "model.add(layers.Conv2D(128, (3, 3), activation=\"elu\", **kwargs))\n", "model.add(layers.Conv2D(128, (3, 3), activation=\"elu\", **kwargs))\n", "model.add(layers.GlobalMaxPooling2D())\n", "model.add(layers.Dropout(0.3))\n", "model.add(layers.Dense(1))" ], "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "kYCXaKAlTnY4" }, "source": [ "We can have a deeper look at our designed model and inspect the number of adaptive parameters by printing the model summary." ] }, { "cell_type": "code", "metadata": { "id": "gI2q65DlTvow", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "66c5868e-39d2-4ad7-a03f-fa8ef47e07c5" }, "source": [ "print(model.summary())" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"energy_regression_CNN\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d (Conv2D) (None, 14, 14, 32) 608 \n", "_________________________________________________________________\n", "conv2d_1 (Conv2D) (None, 14, 14, 32) 9248 \n", "_________________________________________________________________\n", "max_pooling2d (MaxPooling2D) (None, 7, 7, 32) 0 \n", "_________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 7, 7, 64) 18496 \n", "_________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 7, 7, 64) 36928 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 3, 3, 64) 0 \n", "_________________________________________________________________\n", "conv2d_4 (Conv2D) (None, 3, 3, 128) 73856 \n", "_________________________________________________________________\n", "conv2d_5 (Conv2D) (None, 3, 3, 128) 147584 \n", "_________________________________________________________________\n", "global_max_pooling2d (Global (None, 128) 0 \n", "_________________________________________________________________\n", "dropout (Dropout) (None, 128) 0 \n", "_________________________________________________________________\n", "dense (Dense) (None, 1) 129 \n", "=================================================================\n", "Total params: 286,849\n", "Trainable params: 286,849\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "LvP_7f3kT6qC" }, "source": [ "### Compile\n", "We now compile the model to prepare it for the training. During the **compile** step, we set a loss/objective function (`mean_squared_error`, as energy reconstruction is a regression task) and set an optimizer. In this case, we use the Adam optimizer with a learning rate of 0.001.\n", "To monitor the resolution and the bias of the model, we add them as a metric." ] }, { "cell_type": "code", "metadata": { "id": "neGTmqafXVV2" }, "source": [ "def resolution(y_true, y_pred):\n", " \"\"\" Metric to control for standart deviation \"\"\"\n", " mean, var = tf.nn.moments((y_true - y_pred), axes=[0])\n", " return tf.sqrt(var)\n", "\n", "\n", "def bias(y_true, y_pred):\n", " \"\"\" Metric to control for standart deviation \"\"\"\n", " mean, var = tf.nn.moments((y_true - y_pred), axes=[0])\n", " return mean" ], "execution_count": 14, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "bgigKtfOMEor" }, "source": [ "model.compile(\n", " loss='mean_squared_error',\n", " metrics=[bias, resolution],\n", " optimizer=keras.optimizers.Adam(learning_rate=1e-3))" ], "execution_count": 15, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "1gO22y3jUfos" }, "source": [ "---\n", "### Training\n", "We can now run the training for 20 `epochs` (20-fold iteration over the dataset) using our training data `X_train` and our energy labels `energy_train`.\n", "For each iteration (calculating the gradients, updating the model parameters), we use 128 samples (`batch_size`).\n", "\n", "We furthermore can set the fraction of validation data (initially set to `0.1`) that is used to monitor overtraining.\n", "\n", "> **Modification section** \n", "> Feel free to modify the training procedure, for example:\n", ">* Change (increase) the number of `epochs`.\n", ">* Modify the batch size via `batch_size`.\n" ] }, { "cell_type": "code", "metadata": { "id": "uj1ecZz7MEos", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "59811a2b-f03a-4dba-ce8d-82cb7c709d91" }, "source": [ "fit = model.fit(\n", " X_train,\n", " energy_train,\n", " batch_size=64,\n", " epochs=50,\n", " verbose=2,\n", " validation_split=0.1,\n", ")" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/50\n", "1125/1125 - 10s - loss: 51.2510 - bias: 0.2206 - resolution: 5.5853 - val_loss: 7.1643 - val_bias: 0.2620 - val_resolution: 2.6028\n", "Epoch 2/50\n", "1125/1125 - 7s - loss: 18.3058 - bias: 0.1312 - resolution: 3.8974 - val_loss: 7.3300 - val_bias: 0.8466 - val_resolution: 2.5060\n", "Epoch 3/50\n", "1125/1125 - 7s - loss: 14.9923 - bias: 0.1080 - resolution: 3.6163 - val_loss: 8.8342 - val_bias: 1.0222 - val_resolution: 2.7385\n", "Epoch 4/50\n", "1125/1125 - 7s - loss: 14.7818 - bias: 0.1117 - resolution: 3.5481 - val_loss: 6.2154 - val_bias: -1.0135e+00 - val_resolution: 2.2248\n", "Epoch 5/50\n", "1125/1125 - 7s - loss: 13.4509 - bias: 0.1009 - resolution: 3.3991 - val_loss: 5.4807 - val_bias: 0.7123 - val_resolution: 2.1765\n", "Epoch 6/50\n", "1125/1125 - 7s - loss: 12.5764 - bias: 0.1025 - resolution: 3.3318 - val_loss: 3.7811 - val_bias: 0.2103 - val_resolution: 1.8875\n", "Epoch 7/50\n", "1125/1125 - 7s - loss: 12.3931 - bias: 0.0986 - resolution: 3.3015 - val_loss: 6.3476 - val_bias: 0.2326 - val_resolution: 2.4490\n", "Epoch 8/50\n", "1125/1125 - 8s - loss: 11.8061 - bias: 0.0998 - resolution: 3.2197 - val_loss: 4.8639 - val_bias: -6.1058e-01 - val_resolution: 2.0738\n", "Epoch 9/50\n", "1125/1125 - 7s - loss: 11.0862 - bias: 0.1001 - resolution: 3.1308 - val_loss: 5.6667 - val_bias: 1.0685 - val_resolution: 2.0814\n", "Epoch 10/50\n", "1125/1125 - 7s - loss: 10.9595 - bias: 0.1017 - resolution: 3.1238 - val_loss: 5.8789 - val_bias: -7.6734e-01 - val_resolution: 2.2383\n", "Epoch 11/50\n", "1125/1125 - 7s - loss: 10.9668 - bias: 0.0949 - resolution: 3.1261 - val_loss: 5.4742 - val_bias: -6.9801e-01 - val_resolution: 2.1887\n", "Epoch 12/50\n", "1125/1125 - 8s - loss: 14.2411 - bias: 0.1067 - resolution: 3.3518 - val_loss: 5.0514 - val_bias: -1.0154e+00 - val_resolution: 1.9655\n", "Epoch 13/50\n", "1125/1125 - 8s - loss: 11.0807 - bias: 0.0909 - resolution: 3.1564 - val_loss: 4.4283 - val_bias: -5.9475e-02 - val_resolution: 1.9238\n", "Epoch 14/50\n", "1125/1125 - 8s - loss: 10.9338 - bias: 0.0962 - resolution: 3.1070 - val_loss: 3.2731 - val_bias: -4.4556e-01 - val_resolution: 1.7086\n", "Epoch 15/50\n", "1125/1125 - 8s - loss: 10.2959 - bias: 0.0961 - resolution: 3.0459 - val_loss: 4.9692 - val_bias: -9.8476e-02 - val_resolution: 2.1837\n", "Epoch 16/50\n", "1125/1125 - 8s - loss: 10.3641 - bias: 0.0912 - resolution: 3.0307 - val_loss: 3.9938 - val_bias: 0.4335 - val_resolution: 1.9019\n", "Epoch 17/50\n", "1125/1125 - 8s - loss: 10.3517 - bias: 0.0936 - resolution: 3.0283 - val_loss: 4.7277 - val_bias: -1.8346e-02 - val_resolution: 2.1363\n", "Epoch 18/50\n", "1125/1125 - 8s - loss: 10.3864 - bias: 0.0891 - resolution: 3.0255 - val_loss: 6.6410 - val_bias: 1.5696 - val_resolution: 1.9911\n", "Epoch 19/50\n", "1125/1125 - 8s - loss: 10.2311 - bias: 0.0963 - resolution: 3.0281 - val_loss: 3.9939 - val_bias: 0.1593 - val_resolution: 1.9415\n", "Epoch 20/50\n", "1125/1125 - 8s - loss: 10.2525 - bias: 0.0925 - resolution: 3.0340 - val_loss: 6.6985 - val_bias: -1.5925e-01 - val_resolution: 2.5286\n", "Epoch 21/50\n", "1125/1125 - 8s - loss: 9.8306 - bias: 0.0920 - resolution: 2.9892 - val_loss: 3.3819 - val_bias: 0.2883 - val_resolution: 1.7735\n", "Epoch 22/50\n", "1125/1125 - 8s - loss: 9.6167 - bias: 0.0896 - resolution: 2.9479 - val_loss: 3.2679 - val_bias: 0.1500 - val_resolution: 1.7628\n", "Epoch 23/50\n", "1125/1125 - 8s - loss: 9.8323 - bias: 0.0923 - resolution: 2.9840 - val_loss: 4.7279 - val_bias: -1.1579e+00 - val_resolution: 1.7977\n", "Epoch 24/50\n", "1125/1125 - 8s - loss: 10.0505 - bias: 0.0934 - resolution: 3.0126 - val_loss: 3.2452 - val_bias: -1.2292e-02 - val_resolution: 1.7605\n", "Epoch 25/50\n", "1125/1125 - 8s - loss: 9.5215 - bias: 0.0890 - resolution: 2.9296 - val_loss: 3.9381 - val_bias: 0.2119 - val_resolution: 1.9251\n", "Epoch 26/50\n", "1125/1125 - 8s - loss: 9.5036 - bias: 0.0914 - resolution: 2.9311 - val_loss: 7.8479 - val_bias: 0.6907 - val_resolution: 2.6659\n", "Epoch 27/50\n", "1125/1125 - 8s - loss: 9.2302 - bias: 0.0897 - resolution: 2.8851 - val_loss: 4.3504 - val_bias: 0.5339 - val_resolution: 1.9730\n", "Epoch 28/50\n", "1125/1125 - 8s - loss: 9.0326 - bias: 0.0875 - resolution: 2.8620 - val_loss: 4.1697 - val_bias: 0.8761 - val_resolution: 1.7970\n", "Epoch 29/50\n", "1125/1125 - 8s - loss: 8.7526 - bias: 0.0898 - resolution: 2.8216 - val_loss: 4.3673 - val_bias: -2.6423e-01 - val_resolution: 2.0314\n", "Epoch 30/50\n", "1125/1125 - 8s - loss: 8.7202 - bias: 0.0855 - resolution: 2.8117 - val_loss: 4.2290 - val_bias: -1.4770e-01 - val_resolution: 2.0123\n", "Epoch 31/50\n", "1125/1125 - 8s - loss: 8.6606 - bias: 0.0905 - resolution: 2.8043 - val_loss: 4.4510 - val_bias: -1.0919e+00 - val_resolution: 1.7586\n", "Epoch 32/50\n", "1125/1125 - 8s - loss: 8.8006 - bias: 0.0843 - resolution: 2.8235 - val_loss: 3.2122 - val_bias: 0.3594 - val_resolution: 1.7116\n", "Epoch 33/50\n", "1125/1125 - 8s - loss: 8.6037 - bias: 0.0867 - resolution: 2.7932 - val_loss: 3.3244 - val_bias: -5.3664e-02 - val_resolution: 1.7777\n", "Epoch 34/50\n", "1125/1125 - 8s - loss: 8.4480 - bias: 0.0854 - resolution: 2.7728 - val_loss: 4.9355 - val_bias: -6.1919e-02 - val_resolution: 2.1743\n", "Epoch 35/50\n", "1125/1125 - 8s - loss: 8.3973 - bias: 0.0848 - resolution: 2.7553 - val_loss: 2.9338 - val_bias: -1.5135e-01 - val_resolution: 1.6573\n", "Epoch 36/50\n", "1125/1125 - 8s - loss: 8.4444 - bias: 0.0845 - resolution: 2.7592 - val_loss: 4.0460 - val_bias: -3.4717e-01 - val_resolution: 1.9152\n", "Epoch 37/50\n", "1125/1125 - 8s - loss: 8.4157 - bias: 0.0828 - resolution: 2.7608 - val_loss: 3.3563 - val_bias: 0.1284 - val_resolution: 1.7755\n", "Epoch 38/50\n", "1125/1125 - 8s - loss: 8.4045 - bias: 0.0835 - resolution: 2.7544 - val_loss: 3.2529 - val_bias: 0.0631 - val_resolution: 1.7567\n", "Epoch 39/50\n", "1125/1125 - 8s - loss: 8.1558 - bias: 0.0830 - resolution: 2.7154 - val_loss: 4.0424 - val_bias: 0.3233 - val_resolution: 1.9358\n", "Epoch 40/50\n", "1125/1125 - 8s - loss: 8.3666 - bias: 0.0801 - resolution: 2.7408 - val_loss: 2.9017 - val_bias: 0.4578 - val_resolution: 1.5972\n", "Epoch 41/50\n", "1125/1125 - 8s - loss: 7.9695 - bias: 0.0810 - resolution: 2.6883 - val_loss: 2.9058 - val_bias: -6.1835e-02 - val_resolution: 1.6628\n", "Epoch 42/50\n", "1125/1125 - 8s - loss: 7.8910 - bias: 0.0803 - resolution: 2.6773 - val_loss: 5.1250 - val_bias: 1.5005 - val_resolution: 1.6534\n", "Epoch 43/50\n", "1125/1125 - 8s - loss: 7.9850 - bias: 0.0815 - resolution: 2.6898 - val_loss: 2.8235 - val_bias: 0.1839 - val_resolution: 1.6263\n", "Epoch 44/50\n", "1125/1125 - 8s - loss: 8.1262 - bias: 0.0800 - resolution: 2.7059 - val_loss: 3.6428 - val_bias: 0.4255 - val_resolution: 1.8185\n", "Epoch 45/50\n", "1125/1125 - 8s - loss: 7.8745 - bias: 0.0794 - resolution: 2.6626 - val_loss: 3.5905 - val_bias: 0.0579 - val_resolution: 1.8435\n", "Epoch 46/50\n", "1125/1125 - 8s - loss: 7.9296 - bias: 0.0789 - resolution: 2.6789 - val_loss: 3.1791 - val_bias: 0.6325 - val_resolution: 1.6294\n", "Epoch 47/50\n", "1125/1125 - 8s - loss: 7.8913 - bias: 0.0777 - resolution: 2.6621 - val_loss: 2.7095 - val_bias: -1.1288e-01 - val_resolution: 1.5988\n", "Epoch 48/50\n", "1125/1125 - 8s - loss: 7.6065 - bias: 0.0775 - resolution: 2.6237 - val_loss: 4.8592 - val_bias: 1.2698 - val_resolution: 1.7565\n", "Epoch 49/50\n", "1125/1125 - 8s - loss: 7.8245 - bias: 0.0793 - resolution: 2.6488 - val_loss: 2.8825 - val_bias: 0.3158 - val_resolution: 1.6318\n", "Epoch 50/50\n", "1125/1125 - 8s - loss: 7.7593 - bias: 0.0783 - resolution: 2.6517 - val_loss: 3.1409 - val_bias: -5.9592e-01 - val_resolution: 1.6252\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "iknMUMavMEos" }, "source": [ "### Plot training curves" ] }, { "cell_type": "code", "metadata": { "id": "iqt6hivFMEot", "colab": { "base_uri": "https://localhost:8080/", "height": 334 }, "outputId": "a04dd827-4e7a-42fc-e30c-fda32f9c8e2e" }, "source": [ "fig, ax = plt.subplots(1, figsize=(8,5))\n", "n = np.arange(len(fit.history['loss']))\n", "\n", "ax.plot(n, fit.history['loss'], ls='--', c='k', label='loss')\n", "ax.plot(n, fit.history['val_loss'], label='val_loss', c='k')\n", "\n", "ax.set_xlabel('Epoch')\n", "ax.set_ylabel('Loss')\n", "ax.legend()\n", "ax.semilogy()\n", "ax.grid()\n", "plt.show()" ], "execution_count": 17, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "-3j0ScknMEou" }, "source": [ "---\n", "### Performance of the DNN\n", "After training the model, we can use the test set `X_test` to evaluate the performance of the DNN. \n", "\n", "Particularly interesting are the resolution and the bias of the method. A bias close to 0 is desirable. A resolution below 3 EeV can be regarded as good." ] }, { "cell_type": "code", "metadata": { "id": "aObX1rK3MEov", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c6645675-5a12-4455-81ed-0d00f3f5f506" }, "source": [ "energy_pred = model.predict(X_test, batch_size=128, verbose=1)[:,0]\n" ], "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "157/157 [==============================] - 1s 4ms/step\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "DZoXjUnDg6pB", "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "outputId": "325bd357-b176-426b-bc19-d14280c1dcc5" }, "source": [ "diff = energy_pred - energy_test\n", "resolution = np.std(diff)\n", "plt.figure()\n", "plt.hist(diff, bins=100)\n", "plt.xlabel('$E_\\mathrm{rec} - E_\\mathrm{true}$')\n", "plt.ylabel('# Events')\n", "plt.text(0.95, 0.95, '$\\sigma = %.3f$ EeV' % resolution, ha='right', va='top', transform=plt.gca().transAxes)\n", "plt.text(0.95, 0.85, '$\\mu = %.1f$ EeV' % diff.mean(), ha='right', va='top', transform=plt.gca().transAxes)\n", "plt.grid()\n", "plt.xlim(-10, 10)\n", "plt.tight_layout()" ], "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "xxTIoNH4O9ng" }, "source": [ "After estimating the bias and the resolution, we can now inspect the reconstruction via a scatter plot. \n", "\n", "Furthermore, we can study the energy dependence of the resolution. With increasing energy, the performance increases due to the lower sampling fluctuation of the ground-based particle detectors and the larger footprints." ] }, { "cell_type": "code", "metadata": { "id": "RSVhxnNFZ2Ro", "colab": { "base_uri": "https://localhost:8080/", "height": 559 }, "outputId": "bbb73f6c-bddf-42d7-f238-1908a33fa55b" }, "source": [ "x = [3, 10, 30, 100]\n", "labels = [\"3\", \"10\", \"30\", \"100\"]\n", "\n", "diff = energy_pred - energy_test\n", "\n", "# Embedding plot\n", "fig, axes = plt.subplots(1, 2, figsize=(20, 9))\n", "axes[0].scatter(energy_test, energy_pred, s=3, alpha=0.60)\n", "axes[0].set_xlabel(r\"$E_{true}\\;/\\;\\mathrm{EeV}$\")\n", "axes[0].set_ylabel(r\"$E_{DNN}\\;/\\;\\mathrm{EeV}$\")\n", "\n", "stat_box = r\"$\\mu = $ %.3f\" % np.mean(diff) + \" / EeV\" + \"\\n\" + \"$\\sigma = $ %.3f\" % np.std(diff) + \" / EeV\"\n", "axes[0].text(0.95, 0.2, stat_box, verticalalignment=\"top\", horizontalalignment=\"right\",\n", " transform=axes[0].transAxes, backgroundcolor=\"w\")\n", "axes[0].plot([np.min(energy_test), np.max(energy_test)],\n", " [np.min(energy_test), np.max(energy_test)], color=\"red\")\n", "\n", "sns.regplot(x=energy_test, y=diff / energy_test, x_estimator=np.std, x_bins=12,\n", " fit_reg=False, color=\"royalblue\", ax=axes[1])\n", "axes[1].tick_params(axis=\"both\", which=\"major\")\n", "axes[1].set(xscale=\"log\")\n", "plt.xticks(x, labels)\n", "\n", "axes[1].set_xlabel(r\"$E_{true}\\;/\\;\\mathrm{EeV}$\")\n", "axes[1].set_ylabel(r\"$\\sigma_{E}/E$\")\n", "axes[1].set_ylim(0, 0.2)\n", "plt.show()" ], "execution_count": 20, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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