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@dickreuter
dickreuter committed Aug 14, 2023
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3 changes: 2 additions & 1 deletion .gitignore
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*.njsproj
*.sln
*.sw?
*.js
*.js
notebooks/PlayingCards-1/*
347 changes: 347 additions & 0 deletions notebooks/train_card_nn.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"log = logging.getLogger(__name__)\n",
"\n",
"SCRAPER_DIR = 'pics'\n",
"TRAIN_FOLDER = 'pics/train_cards'\n",
"VALIDATE_FOLDER = 'pics/validate_cards'\n",
"TEST_FOLDER = 'pics/test_cards'\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"\n",
"sys.path.insert(0, os.path.abspath('..'))\n",
"from poker.scraper.table_scraper_nn import CardNeuralNetwork"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 52/52 [00:09<00:00, 5.21it/s]\n",
"100%|██████████| 52/52 [00:09<00:00, 5.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 25429 images belonging to 52 classes.\n",
"Found 25362 images belonging to 52 classes.\n",
"Metal device set to: Apple M2 Pro\n",
"\n",
"systemMemory: 32.00 GB\n",
"maxCacheSize: 10.67 GB\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-06-20 21:44:13.159095: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
"2023-06-20 21:44:13.159735: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" conv2d (Conv2D) (None, 50, 15, 64) 1792 \n",
" \n",
" conv2d_1 (Conv2D) (None, 50, 15, 64) 36928 \n",
" \n",
" max_pooling2d (MaxPooling2D (None, 25, 7, 64) 0 \n",
" ) \n",
" \n",
" dropout (Dropout) (None, 25, 7, 64) 0 \n",
" \n",
" conv2d_2 (Conv2D) (None, 25, 7, 128) 73856 \n",
" \n",
" conv2d_3 (Conv2D) (None, 25, 7, 128) 147584 \n",
" \n",
" max_pooling2d_1 (MaxPooling (None, 12, 3, 128) 0 \n",
" 2D) \n",
" \n",
" dropout_1 (Dropout) (None, 12, 3, 128) 0 \n",
" \n",
" conv2d_4 (Conv2D) (None, 12, 3, 256) 295168 \n",
" \n",
" conv2d_5 (Conv2D) (None, 12, 3, 256) 590080 \n",
" \n",
" max_pooling2d_2 (MaxPooling (None, 6, 1, 256) 0 \n",
" 2D) \n",
" \n",
" dropout_2 (Dropout) (None, 6, 1, 256) 0 \n",
" \n",
" flatten (Flatten) (None, 1536) 0 \n",
" \n",
" dense (Dense) (None, 2048) 3147776 \n",
" \n",
" dropout_3 (Dropout) (None, 2048) 0 \n",
" \n",
" dense_1 (Dense) (None, 1024) 2098176 \n",
" \n",
" dropout_4 (Dropout) (None, 1024) 0 \n",
" \n",
" dense_2 (Dense) (None, 52) 53300 \n",
" \n",
"=================================================================\n",
"Total params: 6,444,660\n",
"Trainable params: 6,444,660\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"Epoch 1/50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-06-20 21:44:13.541140: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
"2023-06-20 21:44:13.875994: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"199/199 [==============================] - ETA: 0s - loss: 3.1090 - accuracy: 0.1287"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-06-20 21:44:24.432235: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"199/199 [==============================] - 20s 96ms/step - loss: 3.1090 - accuracy: 0.1287 - val_loss: 2.7084 - val_accuracy: 0.2593\n",
"Epoch 2/50\n",
"199/199 [==============================] - 18s 91ms/step - loss: 1.4316 - accuracy: 0.5396 - val_loss: 1.3452 - val_accuracy: 0.6046\n",
"Epoch 3/50\n",
"199/199 [==============================] - 18s 90ms/step - loss: 0.4745 - accuracy: 0.8413 - val_loss: 0.6106 - val_accuracy: 0.8295\n",
"Epoch 4/50\n",
"199/199 [==============================] - 19s 95ms/step - loss: 0.2253 - accuracy: 0.9250 - val_loss: 0.5851 - val_accuracy: 0.8307\n",
"Epoch 5/50\n",
"199/199 [==============================] - 19s 96ms/step - loss: 0.1481 - accuracy: 0.9518 - val_loss: 0.2995 - val_accuracy: 0.9184\n",
"Epoch 6/50\n",
"199/199 [==============================] - 19s 93ms/step - loss: 0.1069 - accuracy: 0.9669 - val_loss: 0.1781 - val_accuracy: 0.9496\n",
"Epoch 7/50\n",
"199/199 [==============================] - 19s 93ms/step - loss: 0.0908 - accuracy: 0.9727 - val_loss: 0.1818 - val_accuracy: 0.9503\n",
"Epoch 8/50\n",
"199/199 [==============================] - 20s 102ms/step - loss: 0.0890 - accuracy: 0.9714 - val_loss: 0.2145 - val_accuracy: 0.9413\n",
"Epoch 9/50\n",
"199/199 [==============================] - 20s 102ms/step - loss: 0.0697 - accuracy: 0.9773 - val_loss: 0.1006 - val_accuracy: 0.9735\n",
"Epoch 10/50\n",
"199/199 [==============================] - 19s 96ms/step - loss: 0.0678 - accuracy: 0.9788 - val_loss: 0.1634 - val_accuracy: 0.9606\n",
"Epoch 11/50\n",
"199/199 [==============================] - 19s 95ms/step - loss: 0.0537 - accuracy: 0.9838 - val_loss: 0.3071 - val_accuracy: 0.9058\n",
"Epoch 12/50\n",
"199/199 [==============================] - 19s 97ms/step - loss: 0.0577 - accuracy: 0.9831 - val_loss: 0.2502 - val_accuracy: 0.9339\n",
"Epoch 13/50\n",
"199/199 [==============================] - 19s 94ms/step - loss: 0.0601 - accuracy: 0.9815 - val_loss: 0.0625 - val_accuracy: 0.9843\n",
"Epoch 14/50\n",
"199/199 [==============================] - 19s 97ms/step - loss: 0.0543 - accuracy: 0.9839 - val_loss: 0.1312 - val_accuracy: 0.9664\n",
"Epoch 15/50\n",
"199/199 [==============================] - 19s 97ms/step - loss: 0.0491 - accuracy: 0.9851 - val_loss: 0.0999 - val_accuracy: 0.9772\n",
"Epoch 16/50\n",
"199/199 [==============================] - 19s 96ms/step - loss: 0.0424 - accuracy: 0.9873 - val_loss: 0.0492 - val_accuracy: 0.9879\n",
"Epoch 17/50\n",
"199/199 [==============================] - 19s 94ms/step - loss: 0.0530 - accuracy: 0.9856 - val_loss: 0.1115 - val_accuracy: 0.9742\n",
"Epoch 18/50\n",
"199/199 [==============================] - 18s 92ms/step - loss: 0.0593 - accuracy: 0.9840 - val_loss: 0.3231 - val_accuracy: 0.9159\n",
"Epoch 19/50\n",
"199/199 [==============================] - 19s 97ms/step - loss: 0.0402 - accuracy: 0.9887 - val_loss: 0.0673 - val_accuracy: 0.9830\n",
"Epoch 20/50\n",
"199/199 [==============================] - 19s 93ms/step - loss: 0.0575 - accuracy: 0.9846 - val_loss: 0.0373 - val_accuracy: 0.9889\n",
"Epoch 21/50\n",
"199/199 [==============================] - 19s 97ms/step - loss: 0.0519 - accuracy: 0.9855 - val_loss: 0.1266 - val_accuracy: 0.9680\n",
"Epoch 22/50\n",
"199/199 [==============================] - 19s 96ms/step - loss: 0.0414 - accuracy: 0.9891 - val_loss: 0.0780 - val_accuracy: 0.9779\n",
"Epoch 23/50\n",
"199/199 [==============================] - 19s 95ms/step - loss: 0.0438 - accuracy: 0.9886 - val_loss: 0.1230 - val_accuracy: 0.9674\n",
"Epoch 24/50\n",
"199/199 [==============================] - 19s 96ms/step - loss: 0.0364 - accuracy: 0.9895 - val_loss: 0.0721 - val_accuracy: 0.9787\n",
"Epoch 25/50\n",
"199/199 [==============================] - 19s 95ms/step - loss: 0.0487 - accuracy: 0.9874 - val_loss: 0.1245 - val_accuracy: 0.9708\n",
"Epoch 25: early stopping\n",
"52/52 [==============================] - 2s 43ms/step - loss: 0.1217 - accuracy: 0.9701\n",
"Validation loss: 0.12174148112535477\n",
"Validation accuracy: 0.9701021909713745\n"
]
}
],
"source": [
"table_name = \"test\"\n",
"log.info(f\"Start trainig for {table_name}\")\n",
"\n",
"n = CardNeuralNetwork()\n",
"log.info(f\"Creating augmented images in {TRAIN_FOLDER}\")\n",
"n.create_augmented_images(table_name)\n",
"n.train_neural_network()\n",
"# n.save_model_to_disk()\n",
"# n.save_model_to_db(stable_name)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 6). These functions will not be directly callable after loading.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /var/folders/54/bgjlgvk9363ghkydvgjpk5p40000gn/T/tmpi6ml6284/dickreuter/poker-card-classification/assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /var/folders/54/bgjlgvk9363ghkydvgjpk5p40000gn/T/tmpi6ml6284/dickreuter/poker-card-classification/assets\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "937235229cfd4ef6aa9046f08f13aa91",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Upload 3 LFS files: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7878cb49a5834bdf99b97d7a10e33217",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"keras_metadata.pb: 0%| | 0.00/32.8k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a5f2faa3d43a4ad2be81bfe94913b5f1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"saved_model.pb: 0%| | 0.00/254k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9f0dcb715c32407b8687bc225b8b72f5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"variables.data-00000-of-00001: 0%| | 0.00/25.8M [00:00<?, ?B/s]"
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},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'https://huggingface.co/dickreuter/poker-card-classification/tree/main/'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from huggingface_hub import push_to_hub_keras\n",
"token = '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'\n",
"push_to_hub_keras(n.model, \n",
" \"dickreuter/poker-card-classification\", token=token,\n",
" tags = [\"poker-card-classification\", \"pokerbot\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "poker310",
"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": "3.10.9"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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