First commit

02ea2986 · Mattia Mancini · 02ea2986 · 02ea2986 · 02ea2986 · 02ea2986
Commit 02ea2986 authored May 23, 2023 by Mattia Mancini
--- a/ConvSupervised.ipynb
+++ b/ConvSupervised.ipynb
--- a/ConvSupervisedFancy.ipynb
+++ b/ConvSupervisedFancy.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "277d841b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-04-12 14:45:57.443373: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-04-12 14:45:57.611485: I tensorflow/core/util/port.cc:104] 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",
+      "/usr/local/lib/python3.8/dist-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
+      "2023-04-12 14:45:59.939213: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:45:59.980067: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:45:59.980790: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(7857, 500, 800, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from tqdm import tqdm_notebook\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import tensorflow as tf\n",
+    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
+    "for gpu in gpus:\n",
+    "  tf.config.experimental.set_memory_growth(gpu, True)\n",
+    "\n",
+    "  \n",
+    "import h5py\n",
+    "import os.path\n",
+    "dataset=None\n",
+    "timerange=None\n",
+    "freqrange=None\n",
+    "ids=None\n",
+    "base_dir = '/project/lofarsw/Data/Dynspec/'\n",
+    "fpath = os.path.join(base_dir, 'dset.h5')\n",
+    "lpath = os.path.join(base_dir, 'labels.csv')\n",
+    "\n",
+    "\n",
+    "ts_to_klass = None\n",
+    "with open(lpath, 'r') as f_in:\n",
+    "  ts_to_klass = {x[0]: int(x[1]) for x in map(lambda x: x.split(','), filter(lambda x: x, f_in.read().split('\\n')[1:]))}\n",
+    "\n",
+    "with h5py.File(fpath, 'r') as fin:\n",
+    "  in_dataset=fin['/data'][:]\n",
+    "  in_timerange=fin['/time_range'][:]\n",
+    "  in_freqrange=fin['/freq_range'][:]\n",
+    "  in_ids=fin['timestamps'][:]\n",
+    "  in_label_names = {1: 'Type I storm', 2: 'Type II', 3: 'Type III', 4: 'Type IV',  5: 'Atypical', 6: 'Nothing'}\n",
+    "  \n",
+    "g = np.where(~np.isnan(in_dataset.sum(axis=(1,2))))\n",
+    "\n",
+    "dataset = in_dataset[g]\n",
+    "timerange= in_timerange[g]\n",
+    "freqrange= in_freqrange[g]\n",
+    "ids = in_ids[g]\n",
+    "#avg = dataset[:, :, 404]\n",
+    "\n",
+    "\n",
+    "klasses = np.array([ts_to_klass.get(ts.decode(), -1) for ts in in_ids], dtype=int)\n",
+    "shape = dataset.shape\n",
+    "dataset = dataset.reshape((*shape, 1))\n",
+    "print(dataset.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "da00f1d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-04-12 14:46:14.907179: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-04-12 14:46:14.911721: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:46:14.912555: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:46:14.913131: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:46:15.007508: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:46:15.008240: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:46:15.008822: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-04-12 14:46:15.009023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14412 MB memory:  -> device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:00:06.0, compute capability: 7.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "with_label = np.where(klasses != -1)[0]\n",
+    "class_names = [1,2,3,4,5,6]\n",
+    "num_classes = len(class_names)\n",
+    "def to_logits(labels, num_classes):\n",
+    "  logits = np.zeros((len(labels), num_classes))\n",
+    "  for i, r in enumerate(labels):\n",
+    "    logits[i, r] = 1\n",
+    "  return logits\n",
+    "shuffled_dset = dataset[with_label, :, :, :]\n",
+    "shuffled_ids = dataset[with_label]\n",
+    "shuffled_klasses = klasses[with_label] - 1\n",
+    "logits_klasses = to_logits(shuffled_klasses, num_classes)\n",
+    "img_height, img_width, _ = dataset[0].shape\n",
+    "batch_size = 32\n",
+    "training_size = 2500\n",
+    "test_size = 100\n",
+    "validation_size = len(with_label) - training_size - test_size\n",
+    "\n",
+    "\n",
+    "tfdataset = tf.data.Dataset.from_tensor_slices((shuffled_dset, logits_klasses)).shuffle(len(with_label), seed=42)\n",
+    "\n",
+    "\n",
+    "training_dset = tfdataset.take(training_size).batch(batch_size)\n",
+    "validation_dset = tfdataset.skip(training_size).take(validation_size).batch(batch_size)\n",
+    "test_dset = tfdataset.skip(training_size).skip(validation_size).take(test_size).batch(batch_size)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aa0f3be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformIntV2 cause there is no registered converter for this op.\n",
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " random_translation_1 (Rando  (None, 500, 800, 1)      0         \n",
+      " mTranslation)                                                   \n",
+      "                                                                 \n",
+      " random_crop_1 (RandomCrop)  (None, 40, 150, 1)        0         \n",
+      "                                                                 \n",
+      " resnet50v2 (Functional)     (None, 6)                 23570822  \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 23,570,822\n",
+      "Trainable params: 23,525,382\n",
+      "Non-trainable params: 45,440\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "\n",
+    "model = Sequential([\n",
+    "  tf.keras.layers.InputLayer(input_shape=(img_height, img_width, 1)),\n",
+    "  layers.RandomTranslation(height_factor=(-.2, .2), width_factor=(-.2, .2)),\n",
+    "  layers.RandomCrop(height=40, width=150),\n",
+    "  tf.keras.applications.resnet_v2.ResNet50V2(\n",
+    "    include_top=True,\n",
+    "    weights=None,\n",
+    "    input_shape=(40, 150, 1),\n",
+    "    pooling=None,\n",
+    "    classes=6,\n",
+    "    classifier_activation='relu'\n",
+    "  )\n",
+    "])\n",
+    "\n",
+    "model.compile(optimizer='adam',\n",
+    "              #loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n",
+    "              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n",
+    "              metrics=['accuracy'])\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3225424",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/200\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformIntV2 cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
+      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformIntV2 cause there is no registered converter for this op.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-04-12 14:50:03.457149: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:907] Skipping loop optimization for Merge node with control input: sequential_1/random_crop_1/loop_body/cond/pfor/cond/branch_executed/_527\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "79/79 [==============================] - 45s 204ms/step - loss: 1.4692 - accuracy: 0.5160 - val_loss: 1.3556 - val_accuracy: 0.5630\n",
+      "Epoch 2/200\n",
+      "79/79 [==============================] - 15s 193ms/step - loss: 1.3913 - accuracy: 0.5312 - val_loss: 1.4141 - val_accuracy: 0.5197\n",
+      "Epoch 3/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.4021 - accuracy: 0.5256 - val_loss: 1.7683 - val_accuracy: 0.2047\n",
+      "Epoch 4/200\n",
+      "79/79 [==============================] - 15s 193ms/step - loss: 1.4181 - accuracy: 0.5268 - val_loss: 1.5771 - val_accuracy: 0.4173\n",
+      "Epoch 5/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.3899 - accuracy: 0.5292 - val_loss: 1.3639 - val_accuracy: 0.5394\n",
+      "Epoch 6/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.4041 - accuracy: 0.5280 - val_loss: 1.7560 - val_accuracy: 0.2126\n",
+      "Epoch 7/200\n",
+      "79/79 [==============================] - 15s 194ms/step - loss: 1.4212 - accuracy: 0.5012 - val_loss: 1.6034 - val_accuracy: 0.3780\n",
+      "Epoch 8/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.3979 - accuracy: 0.5232 - val_loss: 1.3061 - val_accuracy: 0.5906\n",
+      "Epoch 9/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.3850 - accuracy: 0.5288 - val_loss: 1.2938 - val_accuracy: 0.5906\n",
+      "Epoch 10/200\n",
+      "79/79 [==============================] - 15s 193ms/step - loss: 1.3933 - accuracy: 0.5228 - val_loss: 1.5326 - val_accuracy: 0.4567\n",
+      "Epoch 11/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.4277 - accuracy: 0.5164 - val_loss: 1.7918 - val_accuracy: 0.1969\n",
+      "Epoch 12/200\n",
+      "79/79 [==============================] - 15s 194ms/step - loss: 1.4187 - accuracy: 0.5280 - val_loss: 1.7918 - val_accuracy: 0.1575\n",
+      "Epoch 13/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.3864 - accuracy: 0.5304 - val_loss: 1.3320 - val_accuracy: 0.6024\n",
+      "Epoch 14/200\n",
+      "79/79 [==============================] - 15s 193ms/step - loss: 1.4006 - accuracy: 0.5220 - val_loss: 1.7215 - val_accuracy: 0.2520\n",
+      "Epoch 15/200\n",
+      "79/79 [==============================] - 15s 194ms/step - loss: 1.3964 - accuracy: 0.5216 - val_loss: 1.5706 - val_accuracy: 0.4055\n",
+      "Epoch 16/200\n",
+      "79/79 [==============================] - 16s 196ms/step - loss: 1.3942 - accuracy: 0.5264 - val_loss: 1.7317 - val_accuracy: 0.2244\n",
+      "Epoch 17/200\n",
+      "79/79 [==============================] - 15s 193ms/step - loss: 1.3973 - accuracy: 0.5256 - val_loss: 1.3525 - val_accuracy: 0.5669\n",
+      "Epoch 18/200\n",
+      "79/79 [==============================] - 12s 157ms/step - loss: 1.4012 - accuracy: 0.5136 - val_loss: 1.7676 - val_accuracy: 0.2126\n",
+      "Epoch 19/200\n",
+      "79/79 [==============================] - 12s 156ms/step - loss: 1.3992 - accuracy: 0.5256 - val_loss: 1.4661 - val_accuracy: 0.4921\n",
+      "Epoch 20/200\n",
+      "79/79 [==============================] - 12s 157ms/step - loss: 1.4108 - accuracy: 0.5188 - val_loss: 1.3133 - val_accuracy: 0.5709\n",
+      "Epoch 21/200\n",
+      "79/79 [==============================] - 14s 179ms/step - loss: 1.4016 - accuracy: 0.5212 - val_loss: 1.3943 - val_accuracy: 0.5197\n",
+      "Epoch 22/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3981 - accuracy: 0.5232 - val_loss: 1.3928 - val_accuracy: 0.5512\n",
+      "Epoch 23/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3912 - accuracy: 0.5308 - val_loss: 1.4124 - val_accuracy: 0.5315\n",
+      "Epoch 24/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3964 - accuracy: 0.5176 - val_loss: 1.3383 - val_accuracy: 0.5591\n",
+      "Epoch 25/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.3829 - accuracy: 0.5332 - val_loss: 1.3985 - val_accuracy: 0.5315\n",
+      "Epoch 26/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.4624 - accuracy: 0.5124 - val_loss: 1.5770 - val_accuracy: 0.4134\n",
+      "Epoch 27/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.4359 - accuracy: 0.4980 - val_loss: 1.6139 - val_accuracy: 0.3858\n",
+      "Epoch 28/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.3948 - accuracy: 0.5296 - val_loss: 1.7895 - val_accuracy: 0.2283\n",
+      "Epoch 29/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.4227 - accuracy: 0.5156 - val_loss: 1.6671 - val_accuracy: 0.3583\n",
+      "Epoch 30/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.4064 - accuracy: 0.5340 - val_loss: 1.7951 - val_accuracy: 0.5354\n",
+      "Epoch 31/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3993 - accuracy: 0.5268 - val_loss: 1.3242 - val_accuracy: 0.5827\n",
+      "Epoch 32/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.4091 - accuracy: 0.5240 - val_loss: 1.4127 - val_accuracy: 0.5079\n",
+      "Epoch 33/200\n",
+      "79/79 [==============================] - 14s 170ms/step - loss: 1.4095 - accuracy: 0.5268 - val_loss: 1.7918 - val_accuracy: 0.1732\n",
+      "Epoch 34/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.4207 - accuracy: 0.5300 - val_loss: 1.7699 - val_accuracy: 0.1299\n",
+      "Epoch 35/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.4023 - accuracy: 0.5176 - val_loss: 1.6538 - val_accuracy: 0.2717\n",
+      "Epoch 36/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.4641 - accuracy: 0.5056 - val_loss: 1.4979 - val_accuracy: 0.5000\n",
+      "Epoch 37/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.4453 - accuracy: 0.5348 - val_loss: 1.3891 - val_accuracy: 0.5551\n",
+      "Epoch 38/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.4198 - accuracy: 0.5312 - val_loss: 1.3272 - val_accuracy: 0.5591\n",
+      "Epoch 39/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.4148 - accuracy: 0.5376 - val_loss: 1.4845 - val_accuracy: 0.5276\n",
+      "Epoch 40/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3911 - accuracy: 0.5416 - val_loss: 1.6488 - val_accuracy: 0.3898\n",
+      "Epoch 41/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.4155 - accuracy: 0.5204 - val_loss: 1.3346 - val_accuracy: 0.5433\n",
+      "Epoch 42/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.4017 - accuracy: 0.5212 - val_loss: 1.3755 - val_accuracy: 0.5315\n",
+      "Epoch 43/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3982 - accuracy: 0.5264 - val_loss: 1.4069 - val_accuracy: 0.4961\n",
+      "Epoch 44/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.3734 - accuracy: 0.5392 - val_loss: 1.3838 - val_accuracy: 0.5276\n",
+      "Epoch 45/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3945 - accuracy: 0.5324 - val_loss: 1.5031 - val_accuracy: 0.4488\n",
+      "Epoch 46/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3970 - accuracy: 0.5296 - val_loss: 1.3349 - val_accuracy: 0.5748\n",
+      "Epoch 47/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3914 - accuracy: 0.5240 - val_loss: 1.4520 - val_accuracy: 0.5000\n",
+      "Epoch 48/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3858 - accuracy: 0.5300 - val_loss: 1.5360 - val_accuracy: 0.4094\n",
+      "Epoch 49/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3876 - accuracy: 0.5240 - val_loss: 1.4506 - val_accuracy: 0.4961\n",
+      "Epoch 50/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.3866 - accuracy: 0.5316 - val_loss: 1.4856 - val_accuracy: 0.4803\n",
+      "Epoch 51/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.3881 - accuracy: 0.5316 - val_loss: 1.4590 - val_accuracy: 0.5039\n",
+      "Epoch 52/200\n",
+      "79/79 [==============================] - 15s 187ms/step - loss: 1.3926 - accuracy: 0.5292 - val_loss: 1.4648 - val_accuracy: 0.5118\n",
+      "Epoch 53/200\n",
+      "79/79 [==============================] - 15s 187ms/step - loss: 1.3791 - accuracy: 0.5360 - val_loss: 1.4039 - val_accuracy: 0.5197\n",
+      "Epoch 54/200\n",
+      "79/79 [==============================] - 15s 187ms/step - loss: 1.4000 - accuracy: 0.5276 - val_loss: 1.2531 - val_accuracy: 0.5906\n",
+      "Epoch 55/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.3777 - accuracy: 0.5392 - val_loss: 1.2780 - val_accuracy: 0.5709\n",
+      "Epoch 56/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3821 - accuracy: 0.5320 - val_loss: 1.3780 - val_accuracy: 0.5157\n",
+      "Epoch 57/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.4003 - accuracy: 0.5276 - val_loss: 1.4809 - val_accuracy: 0.4803\n",
+      "Epoch 58/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.3873 - accuracy: 0.5252 - val_loss: 1.7747 - val_accuracy: 0.1890\n",
+      "Epoch 59/200\n",
+      "79/79 [==============================] - 15s 187ms/step - loss: 1.3802 - accuracy: 0.5376 - val_loss: 1.6755 - val_accuracy: 0.3307\n",
+      "Epoch 60/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.3931 - accuracy: 0.5240 - val_loss: 1.3273 - val_accuracy: 0.5787\n",
+      "Epoch 61/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3793 - accuracy: 0.5324 - val_loss: 1.3590 - val_accuracy: 0.5512\n",
+      "Epoch 62/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.3866 - accuracy: 0.5268 - val_loss: 1.3875 - val_accuracy: 0.5354\n",
+      "Epoch 63/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.4101 - accuracy: 0.5180 - val_loss: 1.3784 - val_accuracy: 0.5354\n",
+      "Epoch 64/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.4072 - accuracy: 0.5204 - val_loss: 1.3794 - val_accuracy: 0.5394\n",
+      "Epoch 65/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3871 - accuracy: 0.5296 - val_loss: 1.3762 - val_accuracy: 0.5472\n",
+      "Epoch 66/200\n",
+      "79/79 [==============================] - 15s 188ms/step - loss: 1.3802 - accuracy: 0.5312 - val_loss: 1.3611 - val_accuracy: 0.5315\n",
+      "Epoch 67/200\n",
+      "79/79 [==============================] - 15s 190ms/step - loss: 1.3855 - accuracy: 0.5284 - val_loss: 1.3567 - val_accuracy: 0.5709\n",
+      "Epoch 68/200\n",
+      "79/79 [==============================] - 15s 186ms/step - loss: 1.3996 - accuracy: 0.5208 - val_loss: 1.2827 - val_accuracy: 0.5709\n",
+      "Epoch 69/200\n",
+      "79/79 [==============================] - 15s 189ms/step - loss: 1.3824 - accuracy: 0.5276 - val_loss: 1.3300 - val_accuracy: 0.5669\n",
+      "Epoch 70/200\n",
+      "79/79 [==============================] - 15s 192ms/step - loss: 1.3905 - accuracy: 0.5324 - val_loss: 1.3069 - val_accuracy: 0.5591\n",
+      "Epoch 71/200\n",
+      "79/79 [==============================] - 15s 191ms/step - loss: 1.3792 - accuracy: 0.5360 - val_loss: 1.4586 - val_accuracy: 0.4921\n",
+      "Epoch 72/200\n",
+      "79/79 [==============================] - 15s 193ms/step - loss: 1.3905 - accuracy: 0.5288 - val_loss: 1.3358 - val_accuracy: 0.5669\n",
+      "Epoch 73/200\n",
+      "79/79 [==============================] - 14s 173ms/step - loss: 1.3917 - accuracy: 0.5252 - val_loss: 1.3838 - val_accuracy: 0.5354\n",
+      "Epoch 74/200\n",
+      "79/79 [==============================] - 12s 157ms/step - loss: 1.3974 - accuracy: 0.5264 - val_loss: 1.3751 - val_accuracy: 0.5354\n",
+      "Epoch 75/200\n",
+      "43/79 [===============>..............] - ETA: 5s - loss: 1.3721 - accuracy: 0.5385"
+     ]
+    }
+   ],
+   "source": [
+    "epochs=200\n",
+    "history = model.fit(\n",
+    "  training_dset,\n",
+    "  validation_data=validation_dset,\n",
+    "  epochs=epochs\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deb51d3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acc = history.history['accuracy']\n",
+    "val_acc = history.history['val_accuracy']\n",
+    "\n",
+    "loss = history.history['loss']\n",
+    "val_loss = history.history['val_loss']\n",
+    "\n",
+    "epochs_range = range(epochs)\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
+    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.title('Training and Validation Accuracy')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(epochs_range, loss, label='Training Loss')\n",
+    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.title('Training and Validation Loss')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d231f71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "def predict_labels(dset):\n",
+    "  predictions = model.predict(dset)\n",
+    "  results = [tf.nn.softmax(row) for row in predictions]\n",
+    "  results = [np.argmax(row) for row in results]\n",
+    "  \n",
+    "  return results\n",
+    "\n",
+    "def predict_class_label_number(dset):\n",
+    "  label_names = in_label_names\n",
+    "  return [label_names[o[0][0] + 1] for o in model.predict_top_k(dset, batch_size=128)]\n",
+    "\n",
+    "def show_confusion_matrix(cm, labels):\n",
+    "  plt.figure(figsize=(10, 8))\n",
+    "  sns.heatmap(cm, xticklabels=labels, yticklabels=labels, \n",
+    "              annot=True, fmt='g')\n",
+    "  plt.xlabel('Prediction')\n",
+    "  plt.ylabel('Label')\n",
+    "  plt.show()\n",
+    "\n",
+    "true_classes = []\n",
+    "for img, labels in training_dset:\n",
+    "  true_classes += [np.argmax(row) for row in labels]\n",
+    "\n",
+    "predicted_classes = predict_labels(training_dset)\n",
+    "print(true_classes, predicted_classes)\n",
+    "\n",
+    "\n",
+    "conf_matrix = tf.math.confusion_matrix(true_classes, predicted_classes, num_classes = num_classes)\n",
+    "show_confusion_matrix(conf_matrix, [in_label_names[k+1] for k in range(num_classes)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "046d05fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+%% Cell type:code id:277d841b tags:
+``` python
+import numpy as np
+from tqdm import tqdm_notebook
+import matplotlib.pyplot as plt
+%matplotlib inline
+import tensorflow as tf
+gpus = tf.config.experimental.list_physical_devices('GPU')
+for gpu in gpus:
+  tf.config.experimental.set_memory_growth(gpu, True)
+import h5py
+import os.path
+dataset=None
+timerange=None
+freqrange=None
+ids=None
+base_dir = '/project/lofarsw/Data/Dynspec/'
+fpath = os.path.join(base_dir, 'dset.h5')
+lpath = os.path.join(base_dir, 'labels.csv')
+ts_to_klass = None
+with open(lpath, 'r') as f_in:
+  ts_to_klass = {x[0]: int(x[1]) for x in map(lambda x: x.split(','), filter(lambda x: x, f_in.read().split('\n')[1:]))}
+with h5py.File(fpath, 'r') as fin:
+  in_dataset=fin['/data'][:]
+  in_timerange=fin['/time_range'][:]
+  in_freqrange=fin['/freq_range'][:]
+  in_ids=fin['timestamps'][:]
+  in_label_names = {1: 'Type I storm', 2: 'Type II', 3: 'Type III', 4: 'Type IV',  5: 'Atypical', 6: 'Nothing'}
+g = np.where(~np.isnan(in_dataset.sum(axis=(1,2))))
+dataset = in_dataset[g]
+timerange= in_timerange[g]
+freqrange= in_freqrange[g]
+ids = in_ids[g]
+#avg = dataset[:, :, 404]
+klasses = np.array([ts_to_klass.get(ts.decode(), -1) for ts in in_ids], dtype=int)
+shape = dataset.shape
+dataset = dataset.reshape((*shape, 1))
+print(dataset.shape)
+```
+%% Output
+    2023-04-12 14:45:57.443373: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX
+    To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2023-04-12 14:45:57.611485: I tensorflow/core/util/port.cc:104] 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`.
+    /usr/local/lib/python3.8/dist-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).
+      from pandas.core.computation.check import NUMEXPR_INSTALLED
+    2023-04-12 14:45:59.939213: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:45:59.980067: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:45:59.980790: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    (7857, 500, 800, 1)
+%% Cell type:code id:da00f1d5 tags:
+``` python
+with_label = np.where(klasses != -1)[0]
+class_names = [1,2,3,4,5,6]
+num_classes = len(class_names)
+def to_logits(labels, num_classes):
+  logits = np.zeros((len(labels), num_classes))
+  for i, r in enumerate(labels):
+    logits[i, r] = 1
+  return logits
+shuffled_dset = dataset[with_label, :, :, :]
+shuffled_ids = dataset[with_label]
+shuffled_klasses = klasses[with_label] - 1
+logits_klasses = to_logits(shuffled_klasses, num_classes)
+img_height, img_width, _ = dataset[0].shape
+batch_size = 32
+training_size = 2500
+test_size = 100
+validation_size = len(with_label) - training_size - test_size
+tfdataset = tf.data.Dataset.from_tensor_slices((shuffled_dset, logits_klasses)).shuffle(len(with_label), seed=42)
+training_dset = tfdataset.take(training_size).batch(batch_size)
+validation_dset = tfdataset.skip(training_size).take(validation_size).batch(batch_size)
+test_dset = tfdataset.skip(training_size).skip(validation_size).take(test_size).batch(batch_size)
+```
+%% Output
+    2023-04-12 14:46:14.907179: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX
+    To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2023-04-12 14:46:14.911721: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:46:14.912555: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:46:14.913131: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:46:15.007508: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:46:15.008240: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:46:15.008822: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+    2023-04-12 14:46:15.009023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14412 MB memory:  -> device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:00:06.0, compute capability: 7.0
+%% Cell type:code id:aa0f3be0 tags:
+``` python
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+model = Sequential([
+  tf.keras.layers.InputLayer(input_shape=(img_height, img_width, 1)),
+  layers.RandomTranslation(height_factor=(-.2, .2), width_factor=(-.2, .2)),
+  layers.RandomCrop(height=40, width=150),
+  tf.keras.applications.resnet_v2.ResNet50V2(
+    include_top=True,
+    weights=None,
+    input_shape=(40, 150, 1),
+    pooling=None,
+    classes=6,
+    classifier_activation='relu'
+  )
+])
+model.compile(optimizer='adam',
+              #loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
+              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+model.summary()
+```
+%% Output
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformIntV2 cause there is no registered converter for this op.
+    Model: "sequential_1"
+    _________________________________________________________________
+     Layer (type)                Output Shape              Param #
+    =================================================================
+     random_translation_1 (Rando  (None, 500, 800, 1)      0
+     mTranslation)
+     random_crop_1 (RandomCrop)  (None, 40, 150, 1)        0
+     resnet50v2 (Functional)     (None, 6)                 23570822
+    =================================================================
+    Total params: 23,570,822
+    Trainable params: 23,525,382
+    Non-trainable params: 45,440
+    _________________________________________________________________
+%% Cell type:code id:e3225424 tags:
+``` python
+epochs=200
+history = model.fit(
+  training_dset,
+  validation_data=validation_dset,
+  epochs=epochs
+)
+```
+%% Output
+    Epoch 1/200
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformIntV2 cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.
+    WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformIntV2 cause there is no registered converter for this op.
+    2023-04-12 14:50:03.457149: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:907] Skipping loop optimization for Merge node with control input: sequential_1/random_crop_1/loop_body/cond/pfor/cond/branch_executed/_527
+    79/79 [==============================] - 45s 204ms/step - loss: 1.4692 - accuracy: 0.5160 - val_loss: 1.3556 - val_accuracy: 0.5630
+    Epoch 2/200
+    79/79 [==============================] - 15s 193ms/step - loss: 1.3913 - accuracy: 0.5312 - val_loss: 1.4141 - val_accuracy: 0.5197
+    Epoch 3/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.4021 - accuracy: 0.5256 - val_loss: 1.7683 - val_accuracy: 0.2047
+    Epoch 4/200
+    79/79 [==============================] - 15s 193ms/step - loss: 1.4181 - accuracy: 0.5268 - val_loss: 1.5771 - val_accuracy: 0.4173
+    Epoch 5/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.3899 - accuracy: 0.5292 - val_loss: 1.3639 - val_accuracy: 0.5394
+    Epoch 6/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.4041 - accuracy: 0.5280 - val_loss: 1.7560 - val_accuracy: 0.2126
+    Epoch 7/200
+    79/79 [==============================] - 15s 194ms/step - loss: 1.4212 - accuracy: 0.5012 - val_loss: 1.6034 - val_accuracy: 0.3780
+    Epoch 8/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.3979 - accuracy: 0.5232 - val_loss: 1.3061 - val_accuracy: 0.5906
+    Epoch 9/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.3850 - accuracy: 0.5288 - val_loss: 1.2938 - val_accuracy: 0.5906
+    Epoch 10/200
+    79/79 [==============================] - 15s 193ms/step - loss: 1.3933 - accuracy: 0.5228 - val_loss: 1.5326 - val_accuracy: 0.4567
+    Epoch 11/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.4277 - accuracy: 0.5164 - val_loss: 1.7918 - val_accuracy: 0.1969
+    Epoch 12/200
+    79/79 [==============================] - 15s 194ms/step - loss: 1.4187 - accuracy: 0.5280 - val_loss: 1.7918 - val_accuracy: 0.1575
+    Epoch 13/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.3864 - accuracy: 0.5304 - val_loss: 1.3320 - val_accuracy: 0.6024
+    Epoch 14/200
+    79/79 [==============================] - 15s 193ms/step - loss: 1.4006 - accuracy: 0.5220 - val_loss: 1.7215 - val_accuracy: 0.2520
+    Epoch 15/200
+    79/79 [==============================] - 15s 194ms/step - loss: 1.3964 - accuracy: 0.5216 - val_loss: 1.5706 - val_accuracy: 0.4055
+    Epoch 16/200
+    79/79 [==============================] - 16s 196ms/step - loss: 1.3942 - accuracy: 0.5264 - val_loss: 1.7317 - val_accuracy: 0.2244
+    Epoch 17/200
+    79/79 [==============================] - 15s 193ms/step - loss: 1.3973 - accuracy: 0.5256 - val_loss: 1.3525 - val_accuracy: 0.5669
+    Epoch 18/200
+    79/79 [==============================] - 12s 157ms/step - loss: 1.4012 - accuracy: 0.5136 - val_loss: 1.7676 - val_accuracy: 0.2126
+    Epoch 19/200
+    79/79 [==============================] - 12s 156ms/step - loss: 1.3992 - accuracy: 0.5256 - val_loss: 1.4661 - val_accuracy: 0.4921
+    Epoch 20/200
+    79/79 [==============================] - 12s 157ms/step - loss: 1.4108 - accuracy: 0.5188 - val_loss: 1.3133 - val_accuracy: 0.5709
+    Epoch 21/200
+    79/79 [==============================] - 14s 179ms/step - loss: 1.4016 - accuracy: 0.5212 - val_loss: 1.3943 - val_accuracy: 0.5197
+    Epoch 22/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3981 - accuracy: 0.5232 - val_loss: 1.3928 - val_accuracy: 0.5512
+    Epoch 23/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3912 - accuracy: 0.5308 - val_loss: 1.4124 - val_accuracy: 0.5315
+    Epoch 24/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3964 - accuracy: 0.5176 - val_loss: 1.3383 - val_accuracy: 0.5591
+    Epoch 25/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.3829 - accuracy: 0.5332 - val_loss: 1.3985 - val_accuracy: 0.5315
+    Epoch 26/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.4624 - accuracy: 0.5124 - val_loss: 1.5770 - val_accuracy: 0.4134
+    Epoch 27/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.4359 - accuracy: 0.4980 - val_loss: 1.6139 - val_accuracy: 0.3858
+    Epoch 28/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.3948 - accuracy: 0.5296 - val_loss: 1.7895 - val_accuracy: 0.2283
+    Epoch 29/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.4227 - accuracy: 0.5156 - val_loss: 1.6671 - val_accuracy: 0.3583
+    Epoch 30/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.4064 - accuracy: 0.5340 - val_loss: 1.7951 - val_accuracy: 0.5354
+    Epoch 31/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3993 - accuracy: 0.5268 - val_loss: 1.3242 - val_accuracy: 0.5827
+    Epoch 32/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.4091 - accuracy: 0.5240 - val_loss: 1.4127 - val_accuracy: 0.5079
+    Epoch 33/200
+    79/79 [==============================] - 14s 170ms/step - loss: 1.4095 - accuracy: 0.5268 - val_loss: 1.7918 - val_accuracy: 0.1732
+    Epoch 34/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.4207 - accuracy: 0.5300 - val_loss: 1.7699 - val_accuracy: 0.1299
+    Epoch 35/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.4023 - accuracy: 0.5176 - val_loss: 1.6538 - val_accuracy: 0.2717
+    Epoch 36/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.4641 - accuracy: 0.5056 - val_loss: 1.4979 - val_accuracy: 0.5000
+    Epoch 37/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.4453 - accuracy: 0.5348 - val_loss: 1.3891 - val_accuracy: 0.5551
+    Epoch 38/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.4198 - accuracy: 0.5312 - val_loss: 1.3272 - val_accuracy: 0.5591
+    Epoch 39/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.4148 - accuracy: 0.5376 - val_loss: 1.4845 - val_accuracy: 0.5276
+    Epoch 40/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3911 - accuracy: 0.5416 - val_loss: 1.6488 - val_accuracy: 0.3898
+    Epoch 41/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.4155 - accuracy: 0.5204 - val_loss: 1.3346 - val_accuracy: 0.5433
+    Epoch 42/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.4017 - accuracy: 0.5212 - val_loss: 1.3755 - val_accuracy: 0.5315
+    Epoch 43/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3982 - accuracy: 0.5264 - val_loss: 1.4069 - val_accuracy: 0.4961
+    Epoch 44/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.3734 - accuracy: 0.5392 - val_loss: 1.3838 - val_accuracy: 0.5276
+    Epoch 45/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3945 - accuracy: 0.5324 - val_loss: 1.5031 - val_accuracy: 0.4488
+    Epoch 46/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3970 - accuracy: 0.5296 - val_loss: 1.3349 - val_accuracy: 0.5748
+    Epoch 47/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3914 - accuracy: 0.5240 - val_loss: 1.4520 - val_accuracy: 0.5000
+    Epoch 48/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3858 - accuracy: 0.5300 - val_loss: 1.5360 - val_accuracy: 0.4094
+    Epoch 49/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3876 - accuracy: 0.5240 - val_loss: 1.4506 - val_accuracy: 0.4961
+    Epoch 50/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.3866 - accuracy: 0.5316 - val_loss: 1.4856 - val_accuracy: 0.4803
+    Epoch 51/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.3881 - accuracy: 0.5316 - val_loss: 1.4590 - val_accuracy: 0.5039
+    Epoch 52/200
+    79/79 [==============================] - 15s 187ms/step - loss: 1.3926 - accuracy: 0.5292 - val_loss: 1.4648 - val_accuracy: 0.5118
+    Epoch 53/200
+    79/79 [==============================] - 15s 187ms/step - loss: 1.3791 - accuracy: 0.5360 - val_loss: 1.4039 - val_accuracy: 0.5197
+    Epoch 54/200
+    79/79 [==============================] - 15s 187ms/step - loss: 1.4000 - accuracy: 0.5276 - val_loss: 1.2531 - val_accuracy: 0.5906
+    Epoch 55/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.3777 - accuracy: 0.5392 - val_loss: 1.2780 - val_accuracy: 0.5709
+    Epoch 56/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3821 - accuracy: 0.5320 - val_loss: 1.3780 - val_accuracy: 0.5157
+    Epoch 57/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.4003 - accuracy: 0.5276 - val_loss: 1.4809 - val_accuracy: 0.4803
+    Epoch 58/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.3873 - accuracy: 0.5252 - val_loss: 1.7747 - val_accuracy: 0.1890
+    Epoch 59/200
+    79/79 [==============================] - 15s 187ms/step - loss: 1.3802 - accuracy: 0.5376 - val_loss: 1.6755 - val_accuracy: 0.3307
+    Epoch 60/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.3931 - accuracy: 0.5240 - val_loss: 1.3273 - val_accuracy: 0.5787
+    Epoch 61/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3793 - accuracy: 0.5324 - val_loss: 1.3590 - val_accuracy: 0.5512
+    Epoch 62/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.3866 - accuracy: 0.5268 - val_loss: 1.3875 - val_accuracy: 0.5354
+    Epoch 63/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.4101 - accuracy: 0.5180 - val_loss: 1.3784 - val_accuracy: 0.5354
+    Epoch 64/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.4072 - accuracy: 0.5204 - val_loss: 1.3794 - val_accuracy: 0.5394
+    Epoch 65/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3871 - accuracy: 0.5296 - val_loss: 1.3762 - val_accuracy: 0.5472
+    Epoch 66/200
+    79/79 [==============================] - 15s 188ms/step - loss: 1.3802 - accuracy: 0.5312 - val_loss: 1.3611 - val_accuracy: 0.5315
+    Epoch 67/200
+    79/79 [==============================] - 15s 190ms/step - loss: 1.3855 - accuracy: 0.5284 - val_loss: 1.3567 - val_accuracy: 0.5709
+    Epoch 68/200
+    79/79 [==============================] - 15s 186ms/step - loss: 1.3996 - accuracy: 0.5208 - val_loss: 1.2827 - val_accuracy: 0.5709
+    Epoch 69/200
+    79/79 [==============================] - 15s 189ms/step - loss: 1.3824 - accuracy: 0.5276 - val_loss: 1.3300 - val_accuracy: 0.5669
+    Epoch 70/200
+    79/79 [==============================] - 15s 192ms/step - loss: 1.3905 - accuracy: 0.5324 - val_loss: 1.3069 - val_accuracy: 0.5591
+    Epoch 71/200
+    79/79 [==============================] - 15s 191ms/step - loss: 1.3792 - accuracy: 0.5360 - val_loss: 1.4586 - val_accuracy: 0.4921
+    Epoch 72/200
+    79/79 [==============================] - 15s 193ms/step - loss: 1.3905 - accuracy: 0.5288 - val_loss: 1.3358 - val_accuracy: 0.5669
+    Epoch 73/200
+    79/79 [==============================] - 14s 173ms/step - loss: 1.3917 - accuracy: 0.5252 - val_loss: 1.3838 - val_accuracy: 0.5354
+    Epoch 74/200
+    79/79 [==============================] - 12s 157ms/step - loss: 1.3974 - accuracy: 0.5264 - val_loss: 1.3751 - val_accuracy: 0.5354
+    Epoch 75/200
+    43/79 [===============>..............] - ETA: 5s - loss: 1.3721 - accuracy: 0.5385
+%% Cell type:code id:deb51d3f tags:
+``` python
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+epochs_range = range(epochs)
+plt.figure(figsize=(8, 8))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
+```
+%% Cell type:code id:9d231f71 tags:
+``` python
+import seaborn as sns
+def predict_labels(dset):
+  predictions = model.predict(dset)
+  results = [tf.nn.softmax(row) for row in predictions]
+  results = [np.argmax(row) for row in results]
+  return results
+def predict_class_label_number(dset):
+  label_names = in_label_names
+  return [label_names[o[0][0] + 1] for o in model.predict_top_k(dset, batch_size=128)]
+def show_confusion_matrix(cm, labels):
+  plt.figure(figsize=(10, 8))
+  sns.heatmap(cm, xticklabels=labels, yticklabels=labels,
+              annot=True, fmt='g')
+  plt.xlabel('Prediction')
+  plt.ylabel('Label')
+  plt.show()
+true_classes = []
+for img, labels in training_dset:
+  true_classes += [np.argmax(row) for row in labels]
+predicted_classes = predict_labels(training_dset)
+print(true_classes, predicted_classes)
+conf_matrix = tf.math.confusion_matrix(true_classes, predicted_classes, num_classes = num_classes)
+show_confusion_matrix(conf_matrix, [in_label_names[k+1] for k in range(num_classes)])
+```
+%% Cell type:code id:046d05fc tags:
+``` python
+```
--- a/solar_dynspec_ml.ipynb
+++ b/solar_dynspec_ml.ipynb
--- a/solar_dynspec_ml_variational_auto.ipynb
+++ b/solar_dynspec_ml_variational_auto.ipynb