PSO/mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a637c69-9071-4012-ac1e-93037548b3e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-21 03:38:18.127052: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n",
      "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "\n",
    "from pso_tf import PSO\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "tf.random.set_seed(777)  # for reproducibility\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import date\n",
    "\n",
    "print(tf.__version__)\n",
    "print(tf.config.list_physical_devices())\n",
    "\n",
    "def get_data():\n",
    "    (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "    x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "    x_train = x_train.reshape((60000, 28 ,28, 1))\n",
    "    x_test = x_test.reshape((10000, 28 ,28, 1))\n",
    "\n",
    "    print(f\"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}\")\n",
    "    print(f\"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}\")\n",
    "    return x_train, y_train, x_test, y_test\n",
    "\n",
    "def make_model():\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1)))\n",
    "    model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "    model.compile(loss='sparse_categorical_crossentropy',optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2d9891d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train : (28, 28, 1) | y_train : ()\n",
      "x_test : (28, 28, 1) | y_test : ()\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 24, 24, 32)        832       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 8, 8, 32)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 6, 6, 64)          18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 3, 3, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 3, 3, 64)          0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 576)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               73856     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 94,474\n",
      "Trainable params: 94,474\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:111: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n",
      "init particles position: 100%|██████████| 2/2 [00:00<00:00, 34.52it/s]\n",
      "init velocities: 100%|██████████| 2/2 [00:00<00:00, 1203.19it/s]\n",
      "Iteration 0 / 10: 100%|##########| 2/2 [00:48<00:00, 24.43s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.021180160343647003 | acc : 0.9930999875068665\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 1 / 10: 100%|##########| 2/2 [00:46<00:00, 23.46s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.029995786026120186 | acc : 0.9927999973297119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 2 / 10: 100%|##########| 2/2 [00:47<00:00, 23.57s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.13020965456962585 | acc : 0.9929999709129333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 3 / 10: 100%|##########| 2/2 [00:47<00:00, 23.62s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.032199352979660034 | acc : 0.9918000102043152\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 4 / 10: 100%|##########| 2/2 [00:47<00:00, 23.66s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.025606701150536537 | acc : 0.9925000071525574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 5 / 10: 100%|##########| 2/2 [00:47<00:00, 23.64s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.04198306426405907 | acc : 0.9921000003814697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 6 / 10: 100%|##########| 2/2 [00:47<00:00, 23.69s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.048351287841796875 | acc : 0.9919999837875366\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 7 / 10: 100%|##########| 2/2 [00:47<00:00, 23.73s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.0416271910071373 | acc : 0.9890999794006348\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 8 / 10: 100%|##########| 2/2 [00:47<00:00, 23.70s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.18129077553749084 | acc : 0.9502000212669373\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 9 / 10: 100%|##########| 2/2 [00:47<00:00, 23.69s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.8072962760925293 | acc : 0.7225000262260437\n",
      "313/313 - 0s - loss: 0.0212 - accuracy: 0.9931 - 290ms/epoch - 926us/step\n",
      "Test loss: 0.021180160343647003 / Test accuracy: 0.9930999875068665\n"
     ]
    }
   ],
   "source": [
    "x_train, y_train, x_test, y_test = get_data()\n",
    "model = make_model()\n",
    "\n",
    "loss = keras.losses.MeanSquaredError()\n",
    "optimizer = keras.optimizers.SGD(lr=0.1, momentum=1, decay=1e-05, nesterov=True)\n",
    "\n",
    "pso_m = PSO(model=model, loss_method=loss, optimizer=optimizer, n_particles=2)\n",
    "best_weights, score = pso_m.optimize(x_train, y_train, x_test, y_test, maxiter=10)\n",
    "\n",
    "model.set_weights(best_weights)\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=2)\n",
    "print(f\"Test loss: {score[0]} / Test accuracy: {score[1]}\")\n",
    "\n",
    "day = date.today().strftime(\"%Y-%m-%d\")\n",
    "\n",
    "model.save(f'./model/{day}_mnist.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a38f3c1-8291-40d9-838e-4ffbf4578be5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 685us/step\n",
      "틀린 것 갯수 > 69\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(f\"정답 > {y_test}\")\n",
    "predicted_result = model.predict(x_test)\n",
    "predicted_labels = np.argmax(predicted_result, axis=1)\n",
    "not_correct = []\n",
    "for i in range(len(y_test)):\n",
    "    if predicted_labels[i] != y_test[i]:\n",
    "        not_correct.append(i)\n",
    "        # print(f\"추론 > {predicted_labels[i]} | 정답 > {y_test[i]}\")\n",
    "        \n",
    "print(f\"틀린 것 갯수 > {len(not_correct)}\")\n",
    "for i in range(3):\n",
    "    plt.imshow(x_test[not_correct[i]].reshape(28,28), cmap='Greys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc2d7044",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pso",
   "language": "python",
   "name": "pso"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}