8-3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Khb4cafb5-It"
   },
   "source": [
    "# 합성곱 신경망의 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gtBpMgcc5-I0"
   },
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/8-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다. \n",
    "import tensorflow as tf\n",
    "\n",
    "tf.keras.utils.set_random_seed(42)\n",
    "tf.config.experimental.enable_op_determinism()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "10YURv8s5-I0"
   },
   "source": [
    "## 가중치 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Q7Uqs_t65-I0"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FdM7Nyis5-I1",
    "outputId": "6306b925-0861-40bb-f3ee-d906faa91f7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-05-19 01:28:27--  https://github.com/rickiepark/hg-mldl/raw/master/best-cnn-model.h5\n",
      "Resolving github.com (github.com)... 140.82.113.4\n",
      "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://raw.githubusercontent.com/rickiepark/hg-mldl/master/best-cnn-model.h5 [following]\n",
      "--2022-05-19 01:28:28--  https://raw.githubusercontent.com/rickiepark/hg-mldl/master/best-cnn-model.h5\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4049416 (3.9M) [application/octet-stream]\n",
      "Saving to: 'best-cnn-model.h5.2'\n",
      "\n",
      "best-cnn-model.h5.2 100%[===================>]   3.86M  --.-KB/s    in 0.08s   \n",
      "\n",
      "2022-05-19 01:28:28 (47.6 MB/s) - 'best-cnn-model.h5.2' saved [4049416/4049416]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 코랩에서 실행하는 경우에는 다음 명령을 실행하여 best-cnn-model.h5 파일을 다운로드받아 사용하세요.\n",
    "!wget https://github.com/rickiepark/hg-mldl/raw/master/best-cnn-model.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "y6Fx0cai5-I1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-19 01:28:28.480940: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model('best-cnn-model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tOhJnNvW5-I1",
    "outputId": "bc5fa0a3-acdc-4428-c9ea-544acd6e16ef",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.layers.convolutional.conv2d.Conv2D at 0x7f1da1df4250>,\n",
       " <keras.layers.pooling.max_pooling2d.MaxPooling2D at 0x7f1da1df4fa0>,\n",
       " <keras.layers.convolutional.conv2d.Conv2D at 0x7f1da0533730>,\n",
       " <keras.layers.pooling.max_pooling2d.MaxPooling2D at 0x7f1da04ec4f0>,\n",
       " <keras.layers.reshaping.flatten.Flatten at 0x7f1da04fe9a0>,\n",
       " <keras.layers.core.dense.Dense at 0x7f1da04fef40>,\n",
       " <keras.layers.regularization.dropout.Dropout at 0x7f1da04feca0>,\n",
       " <keras.layers.core.dense.Dense at 0x7f1da0506d30>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7e-EMS_h5-I2",
    "outputId": "b8169942-9df2-4136-bb4e-f70005ab06c0",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 1, 32) (32,)\n"
     ]
    }
   ],
   "source": [
    "conv = model.layers[0]\n",
    "\n",
    "print(conv.weights[0].shape, conv.weights[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D5WNlXS55-I3",
    "outputId": "4e058ee3-517e-4734-8f72-a4cb4f8c7cfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.021033935 0.23466988\n"
     ]
    }
   ],
   "source": [
    "conv_weights = conv.weights[0].numpy()\n",
    "\n",
    "print(conv_weights.mean(), conv_weights.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Mm_xATS95-I3"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "lm37R-hD5-I3",
    "outputId": "68810f67-0486-4c02-a71d-d44bc6c0760d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(conv_weights.reshape(-1, 1))\n",
    "plt.xlabel('weight')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "id": "FBxjPV045-I4",
    "outputId": "5e83f4fb-69f6-4e5b-cb3f-81e3086dc079",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 16, figsize=(15,2))\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(16):\n",
    "        axs[i, j].imshow(conv_weights[:,:,0,i*16 + j], vmin=-0.5, vmax=0.5)\n",
    "        axs[i, j].axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "P-r4Dx2N5-I4"
   },
   "outputs": [],
   "source": [
    "no_training_model = keras.Sequential()\n",
    "\n",
    "no_training_model.add(keras.layers.Conv2D(32, kernel_size=3, activation='relu', \n",
    "                                          padding='same', input_shape=(28,28,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SRz0Z0z_5-I4",
    "outputId": "cae417af-a18f-4463-9574-94123cc931f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 1, 32)\n"
     ]
    }
   ],
   "source": [
    "no_training_conv = no_training_model.layers[0]\n",
    "\n",
    "print(no_training_conv.weights[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7FUqZL695-I4",
    "outputId": "e98160a3-4849-497f-d29c-041f17fcb6fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0029798597 0.08092386\n"
     ]
    }
   ],
   "source": [
    "no_training_weights = no_training_conv.weights[0].numpy()\n",
    "\n",
    "print(no_training_weights.mean(), no_training_weights.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "DktjNm4Z5-I5",
    "outputId": "d25aa974-915f-4dc1-f51f-6e0bfd835614"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(no_training_weights.reshape(-1, 1))\n",
    "plt.xlabel('weight')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "id": "b7QmwoUe5-I5",
    "outputId": "c0df75ae-5501-4dc2-b567-8664ad0cf2c5",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 16, figsize=(15,2))\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(16):\n",
    "        axs[i, j].imshow(no_training_weights[:,:,0,i*16 + j], vmin=-0.5, vmax=0.5)\n",
    "        axs[i, j].axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GEc9KyP35-I6"
   },
   "source": [
    "## 함수형 API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RZ27RyZJ5-I6",
    "outputId": "71b66fba-066f-4e98-db5a-ae3c7563ad09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KerasTensor(type_spec=TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='conv2d_input'), name='conv2d_input', description=\"created by layer 'conv2d_input'\")\n"
     ]
    }
   ],
   "source": [
    "print(model.input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "FfHfv9w_5-I6"
   },
   "outputs": [],
   "source": [
    "conv_acti = keras.Model(model.input, model.layers[0].output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CF6JzHj-5-I6"
   },
   "source": [
    "## 특성 맵 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TU5ENBPB5-I7",
    "outputId": "57189673-d0ff-4d38-ffc2-086cc4954fde"
   },
   "outputs": [],
   "source": [
    "(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "8-GBYsqm5-I7",
    "outputId": "65a4d48f-367a-4f54-8176-76775b32441d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_input[0], cmap='gray_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "aJDrgyzo5-I7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 235ms/step\n"
     ]
    }
   ],
   "source": [
    "inputs = train_input[0:1].reshape(-1, 28, 28, 1)/255.0\n",
    "\n",
    "feature_maps = conv_acti.predict(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wYMHCQ0C5-I7",
    "outputId": "98ca5d17-8590-4dfc-b4c1-98b06731cef6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28, 32)\n"
     ]
    }
   ],
   "source": [
    "print(feature_maps.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "id": "n9hhqasw5-I7",
    "outputId": "2bf80584-39aa-4967-a64b-621b4e261533"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 8, figsize=(15,8))\n",
    "\n",
    "for i in range(4):\n",
    "    for j in range(8):\n",
    "        axs[i, j].imshow(feature_maps[0,:,:,i*8 + j])\n",
    "        axs[i, j].axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "RBCPiHm_5-I8"
   },
   "outputs": [],
   "source": [
    "conv2_acti = keras.Model(model.input, model.layers[2].output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "n5U3pxZc5-I8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 72ms/step\n"
     ]
    }
   ],
   "source": [
    "feature_maps = conv2_acti.predict(train_input[0:1].reshape(-1, 28, 28, 1)/255.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9QcCxjOf5-I8",
    "outputId": "72e976df-3572-476d-b136-07279a68d9b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 14, 14, 64)\n"
     ]
    }
   ],
   "source": [
    "print(feature_maps.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "id": "6xc2oG6D5-I8",
    "outputId": "964e99a6-334d-4f6d-abac-7f177c1b13a0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(8, 8, figsize=(12,12))\n",
    "\n",
    "for i in range(8):\n",
    "    for j in range(8):\n",
    "        axs[i, j].imshow(feature_maps[0,:,:,i*8 + j])\n",
    "        axs[i, j].axis('off')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "8-3.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "default:Python",
   "language": "python",
   "name": "conda-env-default-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}