6_CNN/2_MNIST_CNN_TF2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning MNIST Dataset through CNN Model using tensorflow.keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2844,
     "status": "ok",
     "timestamp": 1620980235810,
     "user": {
      "displayName": "Swan Kim",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwZWbqO16KjgBErU6MHgxM-ERfzGrNpw3JmeSo=s64",
      "userId": "01255046753553274360"
     },
     "user_tz": -540
    },
    "id": "urNXCZo2u65n",
    "outputId": "e0a1f3a1-a42b-49d9-d5ed-628f93a09a64"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-19 17:13:28.508109: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2021-07-19 17:13:28.508241: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "(60000, 28, 28, 1)\n",
      "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# load MNIST Datasets and check the shape\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras import datasets \n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()\n",
    "\n",
    "# reshape images. 28*28 size with 1 color channel\n",
    "train_images = train_images.reshape((-1, 28, 28, 1)) # 60000\n",
    "test_images = test_images.reshape((-1, 28, 28, 1)) # 10000\n",
    "print(train_images.shape)\n",
    "\n",
    "# Normalize pixel value in 0~1 / One-hot Encoding label\n",
    "train_images, test_images = train_images / 255, test_images / 255.0\n",
    "train_y_onehot = to_categorical(train_labels)\n",
    "test_y_onehot = to_categorical(test_labels)\n",
    "print(train_y_onehot[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7745,
     "status": "ok",
     "timestamp": 1620980244274,
     "user": {
      "displayName": "Swan Kim",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwZWbqO16KjgBErU6MHgxM-ERfzGrNpw3JmeSo=s64",
      "userId": "01255046753553274360"
     },
     "user_tz": -540
    },
    "id": "R9_H-4rNu7pZ",
    "outputId": "fec8d99f-b015-49fb-ed77-6027d04d45d8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-19 17:13:31.582466: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2021-07-19 17:13:31.582527: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2021-07-19 17:13:31.582586: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fennecfox38-13UD580-GX30K): /proc/driver/nvidia/version does not exist\n",
      "2021-07-19 17:13:31.582953: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1600)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                16010     \n",
      "=================================================================\n",
      "Total params: 34,826\n",
      "Trainable params: 34,826\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Setting Model\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Passing ```conv2d layer``` with 3*3 filter and 32 output.\n",
    "    - convolutioned size: $26 \\times 26 \\quad (\\because \\frac{28-3}{1}+1=26)$\n",
    "    - parameters from weight: $3 \\times 3 \\times 32 = 288$ (9 from filter, 32 output)\n",
    "    - parameters from bias: $32$ (32 output)\n",
    "    - total parameter in this layer: $288 + 32 = 320$\n",
    "\n",
    "2. Passing ```max_pooling2d``` layer with 2*2 pool_size.\n",
    "    - output size: $13 \\times 13$\n",
    "    - No parameter\n",
    "\n",
    "3. Passing ```conv2d_1``` layer with 3*3 filter and 64 output.\n",
    "    - convolutioned size $= 11 \\times 11 \\quad (\\because \\frac{13-3}{1}+1=11)$\n",
    "    - parameters from weight: $3 \\times 3 \\times 32 \\times 64 = 18432$ (32 input, 9 from filter, 64 output)\n",
    "    - parameters from bias: $64$ (64 output)\n",
    "    - total parameter in this layer: $18432 + 64 = 18496$\n",
    "\n",
    "4. Passing ```max_pooling2d_1``` layer with 2*2 pool_size.\n",
    "    - output size: $5 \\times 5$\n",
    "    - No parameter\n",
    "\n",
    "5. Passing ```flatten``` layer\n",
    "    - size: $1600 \\quad (\\because 5 \\times 5 \\times 64 = 1600)$\n",
    "    - No parameter\n",
    "\n",
    "6. Passing ```dense``` layer\n",
    "    - size: 10\n",
    "    - 10 Parameters (Weight)\n",
    "\n",
    "Total 34826 Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 80861,
     "status": "ok",
     "timestamp": 1620980326089,
     "user": {
      "displayName": "Swan Kim",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwZWbqO16KjgBErU6MHgxM-ERfzGrNpw3JmeSo=s64",
      "userId": "01255046753553274360"
     },
     "user_tz": -540
    },
    "id": "zF9OZA8xvDQs",
    "outputId": "e4b4c6a0-202f-4f47-fb03-02cf90c30348"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-19 17:13:32.235647: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 188160000 exceeds 10% of free system memory.\n",
      "2021-07-19 17:13:32.423049: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-07-19 17:13:32.441346: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2400000000 Hz\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 56s 29ms/step - loss: 0.1502 - accuracy: 0.9561\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 54s 29ms/step - loss: 0.0506 - accuracy: 0.9846\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0365 - accuracy: 0.9888\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0289 - accuracy: 0.9910\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0237 - accuracy: 0.9923\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f763481af40>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_y_onehot, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2586,
     "status": "ok",
     "timestamp": 1620980648311,
     "user": {
      "displayName": "Swan Kim",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwZWbqO16KjgBErU6MHgxM-ERfzGrNpw3JmeSo=s64",
      "userId": "01255046753553274360"
     },
     "user_tz": -540
    },
    "id": "ozeeqRzUxRpf",
    "outputId": "43bc3592-f30e-4fdd-dc4c-267818546ab3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 2s - loss: 0.0349 - accuracy: 0.9887\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.034927040338516235, 0.9886999726295471]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_images,  test_y_onehot, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ```conv2d_1``` layer's weight and bias\n",
    "## traim_images\\[0\\] on ```conv2d_1``` layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "executionInfo": {
     "elapsed": 581,
     "status": "ok",
     "timestamp": 1595827624075,
     "user": {
      "displayName": "Swan Kim",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwZWbqO16KjgBErU6MHgxM-ERfzGrNpw3JmeSo=s64",
      "userId": "01255046753553274360"
     },
     "user_tz": -540
    },
    "id": "FR-8AUUTYiOp",
    "outputId": "852fb5fd-8b32-4cea-ec5d-d83c20b38c02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv2d_1\n",
      "[array([[[[-0.03716338, -0.01726284, -0.14643697, ..., -0.02180024,\n",
      "          -0.01967259, -0.09055096],\n",
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      "          -0.05824013, -0.03185489],\n",
      "         [-0.00370399,  0.05150616, -0.21472225, ..., -0.10343672,\n",
      "           0.02133155, -0.09109882]],\n",
      "\n",
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      "         [ 0.04594893,  0.1453767 , -0.19981037, ..., -0.075119  ,\n",
      "          -0.17247814,  0.12401858]]],\n",
      "\n",
      "\n",
      "       [[[-0.00672277,  0.08271505, -0.05492253, ..., -0.13443506,\n",
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      "         [ 0.03855154,  0.11637758,  0.21684827, ..., -0.02789041,\n",
      "           0.09537826,  0.10166467],\n",
      "         ...,\n",
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      "           0.00478687,  0.02508012],\n",
      "         [-0.04056131, -0.10585655, -0.13261138, ...,  0.0518285 ,\n",
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      "           0.14968324,  0.10808838]],\n",
      "\n",
      "        [[ 0.09024234,  0.03030722, -0.06639366, ..., -0.1678423 ,\n",
      "           0.07820772, -0.02212222],\n",
      "         [ 0.00939479, -0.02899844, -0.08740856, ..., -0.18312393,\n",
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      "           0.07513638,  0.02769767]]],\n",
      "\n",
      "\n",
      "       [[[ 0.1128071 ,  0.11219765, -0.03765777, ...,  0.0501106 ,\n",
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      "\n",
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      "         [-0.27259427, -0.32665735,  0.15325159, ..., -0.05757033,\n",
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      "         [-0.06687272, -0.05955574,  0.05854886, ..., -0.03327926,\n",
      "           0.05883277,  0.0332817 ]],\n",
      "\n",
      "        [[ 0.16582808,  0.09129435,  0.05284847, ..., -0.11963327,\n",
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      "         [ 0.18938082, -0.0370188 ,  0.01433221, ..., -0.19436213,\n",
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      "         [-0.02789189, -0.19225273,  0.10874588, ..., -0.15799776,\n",
      "          -0.04269526, -0.18093151],\n",
      "         ...,\n",
      "         [-0.07200459, -0.07042751,  0.18894923, ...,  0.04724158,\n",
      "          -0.11618482, -0.07945514],\n",
      "         [ 0.21232244,  0.04303483,  0.2213723 , ..., -0.32069126,\n",
      "           0.12181795,  0.08463143],\n",
      "         [-0.04699901,  0.06778947, -0.01717822, ..., -0.1916563 ,\n",
      "          -0.01823779, -0.03058162]]]], dtype=float32), array([-0.10665254, -0.09063317, -0.07319518, -0.12446057, -0.04581743,\n",
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      "       -0.06910221, -0.14254348, -0.08880764, -0.09732224,  0.03440643,\n",
      "       -0.09436501, -0.02000814, -0.1439752 ,  0.00307353, -0.04794649,\n",
      "       -0.05618883, -0.04808604, -0.03466814, -0.04606806, -0.09489881,\n",
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      "       -0.00221232, -0.18575765, -0.06051625, -0.05367337,  0.01954546,\n",
      "       -0.05883934, -0.09388632, -0.06851212, -0.08751638, -0.10471346,\n",
      "       -0.00639205, -0.21795067, -0.05087401, -0.08955174,  0.02212336,\n",
      "       -0.02395128, -0.05134357, -0.06419254, -0.01476529, -0.02065953,\n",
      "       -0.01099799, -0.10619862, -0.03901656, -0.11310323, -0.1143325 ,\n",
      "       -0.00846521, -0.04673978, -0.17967224,  0.08035478, -0.02370092,\n",
      "       -0.0856052 ,  0.02104353, -0.11254144, -0.09765598], dtype=float32)]\n",
      "(3, 3, 32, 64) (64,)\n"
     ]
    }
   ],
   "source": [
    "print(model.layers[2].name)\n",
    "idx = 2\n",
    "print(model.layers[idx].get_weights())\n",
    "print(model.layers[idx].get_weights()[0].shape, model.layers[idx].get_weights()[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "executionInfo": {
     "elapsed": 610,
     "status": "ok",
     "timestamp": 1595826743918,
     "user": {
      "displayName": "Swan Kim",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwZWbqO16KjgBErU6MHgxM-ERfzGrNpw3JmeSo=s64",
      "userId": "01255046753553274360"
     },
     "user_tz": -540
    },
    "id": "uarh_nRILzln",
    "outputId": "ed2ba398-6d37-4c83-ef9c-37f78dc8c1e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11, 11, 64)\n",
      "(11, 11)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "h = model.layers[idx].output\n",
    "activation_model = tf.keras.models.Model(inputs = model.input, outputs = h)\n",
    "activations = activation_model.predict(np.expand_dims(train_images[0], axis = 0))\n",
    "print(activations[0].shape)\n",
    "feature = activations[0].sum(axis = 2)\n",
    "print(feature.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "executionInfo": {
     "elapsed": 659,
     "status": "ok",
     "timestamp": 1595826991981,
     "user": {
      "displayName": "Swan Kim",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiwZWbqO16KjgBErU6MHgxM-ERfzGrNpw3JmeSo=s64",
      "userId": "01255046753553274360"
     },
     "user_tz": -540
    },
    "id": "SdCqWlfHL7M4",
    "outputId": "e3e413b0-fa78-43af-ee7c-2b5db4080b08"
   },
   "outputs": [
    {
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   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(train_images[0].reshape(28,28))\n",
    "plt.show()\n",
    "plt.imshow(feature)\n",
    "plt.show()"
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