{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "import tensorflow as tf\n",
    "# tf.random.set_seed(777)  # for reproducibility\n",
    "\n",
    "from pso_tf import PSO\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def get_data():\n",
    "    x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "    y = np.array([[0], [1], [1], [0]])\n",
    "    \n",
    "    return x, y\n",
    "\n",
    "def make_model():\n",
    "    leyer = []\n",
    "    leyer.append(layers.Dense(2, activation='sigmoid', input_shape=(2,)))\n",
    "    leyer.append(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "    model = Sequential(leyer)\n",
    "\n",
    "    sgd = keras.optimizers.SGD(lr=0.1, momentum=1, decay=1e-05, nesterov=True)\n",
    "    # adam = keras.optimizers.Adam(lr=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.)\n",
    "    model.compile(loss='mse', optimizer=sgd, metrics=['accuracy'])\n",
    "\n",
    "    print(model.summary())\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_11\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_22 (Dense)            (None, 2)                 6         \n",
      "                                                                 \n",
      " dense_23 (Dense)            (None, 1)                 3         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 9\n",
      "Trainable params: 9\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "init particles position: 100%|██████████| 15/15 [00:00<00:00, 85.12it/s]\n",
      "init velocities: 100%|██████████| 15/15 [00:00<00:00, 46465.70it/s]\n",
      "Iteration 0 / 10: 100%|##########| 15/15 [00:05<00:00,  2.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.2620863338311513 | acc : 0.5 | best loss : 0.24143654108047485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 1 / 10: 100%|##########| 15/15 [00:05<00:00,  2.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.24147300918896994 | acc : 0.6333333333333333 | best loss : 0.20360520482063293\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 2 / 10: 100%|##########| 15/15 [00:05<00:00,  2.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.211648628115654 | acc : 0.65 | best loss : 0.17383326590061188\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 3 / 10: 100%|##########| 15/15 [00:05<00:00,  2.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.21790608167648315 | acc : 0.6833333333333333 | best loss : 0.16785581409931183\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 4 / 10: 100%|##########| 15/15 [00:05<00:00,  2.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.20557119349638622 | acc : 0.7333333333333333 | best loss : 0.16668711602687836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 5 / 10: 100%|##########| 15/15 [00:05<00:00,  2.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.20823073089122773 | acc : 0.7 | best loss : 0.16668711602687836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 6 / 10: 100%|##########| 15/15 [00:05<00:00,  2.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.21380058924357095 | acc : 0.7166666666666667 | best loss : 0.16668711602687836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 7 / 10: 100%|##########| 15/15 [00:06<00:00,  2.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.2561836312214533 | acc : 0.6833333333333333 | best loss : 0.16667115688323975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 8 / 10: 100%|##########| 15/15 [00:05<00:00,  2.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.30372582376003265 | acc : 0.65 | best loss : 0.16667115688323975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 9 / 10: 100%|##########| 15/15 [00:05<00:00,  2.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss : 0.281868569056193 | acc : 0.7 | best loss : 0.16667115688323975\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "[[0.        ]\n",
      " [0.66422266]\n",
      " [0.6642227 ]\n",
      " [0.6642227 ]]\n",
      "[[0]\n",
      " [1]\n",
      " [1]\n",
      " [0]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "x, y = get_data()\n",
    "x_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "y_test = np.array([[0], [1], [1], [0]])\n",
    "\n",
    "model = make_model()\n",
    "\n",
    "loss = keras.losses.MeanSquaredError()\n",
    "optimizer = keras.optimizers.SGD(lr=0.1, momentum=0.9, decay=1e-05, nesterov=True)\n",
    "\n",
    "\n",
    "pso_xor = PSO(model=model, loss_method=loss, optimizer=optimizer, n_particles=15)\n",
    "\n",
    "best_weights, score = pso_xor.optimize(x, y, x_test, y_test, maxiter=10, epochs=20)\n",
    "\n",
    "model.set_weights(best_weights)\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "print(y_pred)\n",
    "print(y_test)\n",
    "\n",
    "history = pso_xor.global_history()\n",
    "\n",
    "# print(f\"history > {history}\")\n",
    "# print(f\"score > {score}\")\n",
    "# plt.plot(history)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = np.array([[0, 1], [0, 0], [1, 1], [1, 0]])\n",
    "y_pred = model.predict(x_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test():\n",
    "    model = make_model()\n",
    "    x, y = get_data()\n",
    "    x_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
    "    y_test = np.array([[0], [1], [1], [0]])\n",
    "    \n",
    "    callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
    "\n",
    "    hist = model.fit(x, y, epochs=50000, verbose=1, callbacks=[callback] , validation_data=(x_test, y_test))\n",
    "    y_pred=model.predict(x_test)\n",
    "    print(y_pred)\n",
    "    print(y_test)\n",
    "    \n",
    "    return hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    fig, loss_ax = plt.subplots()\n",
    "    acc_ax = loss_ax.twinx()\n",
    "\n",
    "    loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n",
    "    loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')\n",
    "    loss_ax.set_xlabel('epoch')\n",
    "    loss_ax.set_ylabel('loss')\n",
    "    loss_ax.legend(loc='upper left')\n",
    "\n",
    "    acc_ax.plot(hist.history['accuracy'], 'b', label='train acc')\n",
    "    acc_ax.plot(hist.history['val_accuracy'], 'g', label='val acc')\n",
    "    acc_ax.set_ylabel('accuracy')\n",
    "    acc_ax.legend(loc='upper right')\n",
    "\n",
    "    plt.show()\n",
    "hist = test()\n",
    "plot_history(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(model(x).numpy())\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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