4장. 2절. 손글씨 숫자 인식을 위한 케라스 CNN 모형.ipynb

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   "metadata": {
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     "end_time": "2021-12-01T06:19:27.546998Z",
     "start_time": "2021-12-01T06:17:42.155399Z"
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   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">401,536</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m401,536\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,290\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">421,642</span> (1.61 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m421,642\u001b[0m (1.61 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">421,642</span> (1.61 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m421,642\u001b[0m (1.61 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras import Sequential, layers\n",
    "model = Sequential([\n",
    "    layers.Input(shape=(28,28,1)),\n",
    "    layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
    "    layers.MaxPooling2D(),\n",
    "    layers.Dropout(0.1),\n",
    "    layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
    "    layers.MaxPooling2D(),\n",
    "    layers.Dropout(0.25),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(128, activation='relu'),\n",
    "    layers.Dropout(0.5),\n",
    "    layers.Dense(10, activation='softmax')\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a150c2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "X_train = X_train.reshape(-1, 28, 28, 1) / 255.0\n",
    "X_test = X_test.reshape(-1, 28, 28, 1) / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12fb5075-9d83-4d05-9043-4410334320dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 32ms/step - accuracy: 0.7914 - loss: 0.6373 - val_accuracy: 0.9798 - val_loss: 0.0595\n",
      "Epoch 2/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 35ms/step - accuracy: 0.9643 - loss: 0.1153 - val_accuracy: 0.9861 - val_loss: 0.0410\n",
      "Epoch 3/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 33ms/step - accuracy: 0.9770 - loss: 0.0761 - val_accuracy: 0.9885 - val_loss: 0.0368\n",
      "Epoch 4/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 32ms/step - accuracy: 0.9796 - loss: 0.0659 - val_accuracy: 0.9894 - val_loss: 0.0333\n",
      "Epoch 5/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 31ms/step - accuracy: 0.9829 - loss: 0.0575 - val_accuracy: 0.9904 - val_loss: 0.0280\n",
      "Epoch 6/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 32ms/step - accuracy: 0.9847 - loss: 0.0515 - val_accuracy: 0.9916 - val_loss: 0.0269\n",
      "Epoch 7/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 32ms/step - accuracy: 0.9861 - loss: 0.0453 - val_accuracy: 0.9928 - val_loss: 0.0224\n",
      "Epoch 8/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 34ms/step - accuracy: 0.9869 - loss: 0.0429 - val_accuracy: 0.9919 - val_loss: 0.0249\n",
      "Epoch 9/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 32ms/step - accuracy: 0.9879 - loss: 0.0372 - val_accuracy: 0.9937 - val_loss: 0.0197\n",
      "Epoch 10/10\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 31ms/step - accuracy: 0.9886 - loss: 0.0364 - val_accuracy: 0.9931 - val_loss: 0.0225\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "hist = model.fit(X_train, y_train,\n",
    "                 validation_data=(X_test, y_test),\n",
    "                 batch_size=128, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03097072-71f9-42be-ad10-cf0d113e2335",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.022469, Accuracy: 0.993100\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f'Loss: {loss:.6f}, Accuracy: {accuracy:.6f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "successful-allen",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T06:19:27.749160Z",
     "start_time": "2021-12-01T06:19:27.547867Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, loss_ax = plt.subplots()\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 = loss_ax.twinx()\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='lower left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d835dd9-5a47-4f4f-9656-e950356849a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
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