4장. 3절. CNN 알고리즘 - 3.2. AlexNet.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e015573",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "\n",
    "class AlexNet:\n",
    "    @staticmethod\n",
    "    def build(input_shape=(224,224,3), activation='relu', class_num=1000):\n",
    "        model = Sequential()\n",
    "        \n",
    "        model.add(Conv2D(96, (11,11), strides=(4,4), input_shape=input_shape, \n",
    "                         activation=activation, padding=\"same\"))\n",
    "        model.add(MaxPooling2D(pool_size=(3,3), strides=2))\n",
    "        model.add(BatchNormalization())\n",
    "\n",
    "        model.add(Conv2D(256, (5,5), activation=activation, \n",
    "                         padding=\"same\"))\n",
    "        model.add(MaxPooling2D(pool_size=(3,3), strides=2))\n",
    "        model.add(BatchNormalization())\n",
    "\n",
    "        model.add(Conv2D(384, (3,3), activation=activation, padding=\"same\"))\n",
    "        model.add(Conv2D(384, (3,3), activation=activation, padding=\"same\"))\n",
    "        model.add(Conv2D(256, (3,3), activation=activation, padding=\"same\"))\n",
    "\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(4096, activation=activation))\n",
    "        model.add(Dense(4096, activation=activation))\n",
    "        model.add(Dense(class_num, activation='softmax'))\n",
    "\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5f7e1d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 56, 56, 96)        34944     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 27, 27, 96)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 27, 27, 96)       384       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 27, 27, 256)       614656    \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 13, 13, 256)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 13, 13, 256)      1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 13, 13, 384)       885120    \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 13, 13, 384)       1327488   \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 13, 13, 256)       884992    \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 43264)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 4096)              177213440 \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4096)              16781312  \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1000)              4097000   \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 201,840,360\n",
      "Trainable params: 201,839,656\n",
      "Non-trainable params: 704\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = AlexNet.build(input_shape=(224,224,3))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6611ed6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AlexNet.build(input_shape=(32,32,3), class_num=10)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c14a79a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "250/250 [==============================] - 257s 1s/step - loss: 1.6222 - accuracy: 0.3970 - val_loss: 1.7321 - val_accuracy: 0.4036\n",
      "Epoch 2/10\n",
      "250/250 [==============================] - 262s 1s/step - loss: 1.3121 - accuracy: 0.5282 - val_loss: 1.4434 - val_accuracy: 0.4795\n",
      "Epoch 3/10\n",
      " 63/250 [======>.......................] - ETA: 3:21 - loss: 1.1518 - accuracy: 0.5937"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import cifar10\n",
    "(train_X, train_y), (test_X, test_y) = cifar10.load_data()\n",
    "history = model.fit(train_X, train_y,\n",
    "                    validation_data=(test_X, test_y),\n",
    "                    batch_size=200, epochs=10, \n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c1013e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss, accurracy = model.evaluate(test_X, test_y, verbose=1)\n",
    "print(f\"Loss: {loss}, Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e01e6b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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