ex04_이미지_다중분류(손글씨데이터).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"mount_file_id":"1O96pYuK9HWKuVt8UIjOxbbe5H0mPGI2b","authorship_tag":"ABX9TyPb381/3Kd8zRLjtSZsrrem"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["## 목표\n","- 손글씨 데이터(이미지)를 활용한 다중 분류 딥러닝 모델 구현"],"metadata":{"id":"IDH3-ASGB6dG"}},{"cell_type":"markdown","source":["### 데이터 불러오기"],"metadata":{"id":"70ZShHklCA-5"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras import Sequential\n","from tensorflow.keras.layers import InputLayer, Dense, Flatten"],"metadata":{"id":"8v-dpCQY8XsO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%cd /content/drive/MyDrive/Colab\\ Notebooks/2022GJAI_DL/GJAI_DL"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hkCXMW2gEWEa","executionInfo":{"status":"ok","timestamp":1665473692329,"user_tz":-540,"elapsed":5,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"624c15eb-7e26-42a5-9f5a-32fe72b02f95"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Colab Notebooks/2022GJAI_DL/GJAI_DL\n"]}]},{"cell_type":"code","source":["!pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"luL-dB_dEjc3","executionInfo":{"status":"ok","timestamp":1665473692329,"user_tz":-540,"elapsed":4,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"eb162178-dcaf-46e3-f597-4a2658268291"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Colab Notebooks/2022GJAI_DL/GJAI_DL\n"]}]},{"cell_type":"code","source":["data = pd.read_csv('./data/digit-recognizer/train.csv')\n","X = data.iloc[:, 1:]\n","y = data['label']\n","\n","X.shape, y.shape"],"metadata":{"id":"hLJ87aeK9B-B","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665473697171,"user_tz":-540,"elapsed":4844,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"1c6739c1-688e-4fb2-8a8e-a0ba65c5287d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((42000, 784), (42000,))"]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","source":["- X는 2차원 형태인 이미지 데이터를 1차원 형태로 변형된 상태임\n","- X 데이터를 원래의 형태(2차원)로 변경해주기"],"metadata":{"id":"AKalF-e_HB7h"}},{"cell_type":"code","source":["X = X.values.reshape(-1, 28, 28)\n","\n","X.shape, y.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WOwSUs4WF9b7","executionInfo":{"status":"ok","timestamp":1665473697171,"user_tz":-540,"elapsed":10,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"f4a3ef31-64ec-42bc-8b41-258191d35eb6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((42000, 28, 28), (42000,))"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["- 이미지 데이터 출력해보기"],"metadata":{"id":"_clqJlYsIFaw"}},{"cell_type":"code","source":["plt.imshow(X[2], cmap='gray')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"hvNOXmMDF99T","executionInfo":{"status":"ok","timestamp":1665473697171,"user_tz":-540,"elapsed":8,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"f85a9c82-1246-43e9-e4ee-799e5ad1e9c3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f13e8320990>"]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALMklEQVR4nO3dX4hc9RnG8edpjOC/i6TSZYlLtZIbKTSWJVQqNUUiaW6iN2IuSmqF9cIUhV402AuFUpBQ7YUXgRVD0mIVQcUgpWYbQtPeSFZJY/6gSSViljWL5MIohNTN24s5KWPcmdnMOWfOZN/vB4Y58/vNnvNy9Mn5O+fniBCApe9bTRcAYDAIO5AEYQeSIOxAEoQdSOKaQS7MNqf+gZpFhBdqL7Vlt73B9ge2T9reVmZeAOrlfq+z214m6UNJ6yWdlnRQ0uaIONblb9iyAzWrY8u+VtLJiPgoIi5IekXSphLzA1CjMmFfJemTts+ni7avsT1he9r2dIllASip9hN0ETEpaVJiNx5oUpkt+4yksbbPtxRtAIZQmbAflLTa9m22r5X0kKQ91ZQFoGp978ZHxFe2t0p6W9IySTsj4mhllQGoVN+X3vpaGMfsQO1quakGwNWDsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkBjpkMwZv2bJlXfu3b9/etf/ixYtd+7dt6z547/z8fNd+DA5bdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IglFcl7jrrruua/+XX35Zav7XX3991/7z58+Xmj+uXKdRXEvdVGP7lKRzkuYlfRUR42XmB6A+VdxB99OI+KyC+QCoEcfsQBJlwx6S9tp+1/bEQl+wPWF72vZ0yWUBKKHUCTrbqyJixvZ3JE1J+lVEHOjyfU7QDRgn6PLpdIKu1JY9ImaK9zlJb0haW2Z+AOrTd9ht32D7pkvTku6TdKSqwgBUq8zZ+BFJb9i+NJ+/RMTfKqkKQOX6DntEfCTpBxXWAqBGXHoDkiDsQBKEHUiCsANJEHYgCR4ljVIefvjhrv07duwYUCXohS07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBo6SXuLqfVLN3796u/Rs2bCg1f1y5Wp5UA+DqQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgiZ5ht73T9pztI21tK21P2T5RvK+ot0wAZS1my75L0uWPG9kmaV9ErJa0r/gMYIj1DHtEHJB09rLmTZJ2F9O7Jd1fcV0AKtbvWG8jETFbTH8qaaTTF21PSJroczkAKlJ6YMeIiG4PkoyISUmTEg+cBJrU79n4M7ZHJal4n6uuJAB16DfseyRtKaa3SHqzmnIA1KXnbrztlyWtk3Sz7dOSnpL0jKRXbT8i6WNJD9ZZJPo3Pz/ftX9qaqpr//r166ssBw3qGfaI2Nyh696KawFQI+6gA5Ig7EAShB1IgrADSRB2IInSd9BhuF24cKFr/65du7r2c+lt6WDLDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJcJ19ibvmmu7/ie+6664BVYKmsWUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSS4zr7ELV++vGv/1q1bB1QJmsaWHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEj3Dbnun7TnbR9ranrY9Y/tQ8dpYb5kAylrMln2XpA0LtP8xItYUr79WWxaAqvUMe0QckHR2ALUAqFGZY/attg8Xu/krOn3J9oTtadvTJZYFoKR+w75D0u2S1kialfRspy9GxGREjEfEeJ/LAlCBvsIeEWciYj4iLkp6QdLaassCULW+wm57tO3jA5KOdPougOHQ8/fstl+WtE7SzbZPS3pK0jrbaySFpFOSHq2xRgAV6Bn2iNi8QPOLNdQCoEbcQQckQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEj2fLour2/PPP990CRgSbNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAmusy9xY2NjXfttD6gSNK3nlt32mO39to/ZPmr78aJ9pe0p2yeK9xX1lwugX4vZjf9K0q8j4g5JP5L0mO07JG2TtC8iVkvaV3wGMKR6hj0iZiPivWL6nKTjklZJ2iRpd/G13ZLur6tIAOVd0TG77Vsl3SnpHUkjETFbdH0qaaTD30xImui/RABVWPTZeNs3SnpN0hMR8Xl7X0SEpFjo7yJiMiLGI2K8VKUASllU2G0vVyvoL0XE60XzGdujRf+opLl6SgRQhcWcjbekFyUdj4jn2rr2SNpSTG+R9Gb15aFuEVHqhavHYo7Zfyzp55Let32oaHtS0jOSXrX9iKSPJT1YT4kAqtAz7BHxL0md7ry4t9pyANSF22WBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUjCg/xNsm1+AD1g99xzT9f+/fv3l5r/unXruvYfOHCg1Pxx5SJiwV+psmUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSS4zg4sMVxnB5Ij7EAShB1IgrADSRB2IAnCDiRB2IEkFjM++5jt/baP2T5q+/Gi/WnbM7YPFa+N9ZcLoF89b6qxPSppNCLes32TpHcl3a/WeOxfRMQfFr0wbqoBatfppprFjM8+K2m2mD5n+7ikVdWWB6BuV3TMbvtWSXdKeqdo2mr7sO2dtld0+JsJ29O2p0tVCqCURd8bb/tGSf+Q9PuIeN32iKTPJIWk36m1q//LHvNgNx6oWafd+EWF3fZySW9Jejsinlug/1ZJb0XE93vMh7ADNev7hzC2LelFScfbg16cuLvkAUlHyhYJoD6LORt/t6R/Snpf0sWi+UlJmyWtUWs3/pSkR4uTed3mxZYdqFmp3fiqEHagfvyeHUiOsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kETPB05W7DNJH7d9vrloG0bDWtuw1iVRW7+qrO27nToG+nv2byzcno6I8cYK6GJYaxvWuiRq69egamM3HkiCsANJNB32yYaX382w1jasdUnU1q+B1NboMTuAwWl6yw5gQAg7kEQjYbe9wfYHtk/a3tZEDZ3YPmX7/WIY6kbHpyvG0JuzfaStbaXtKdsnivcFx9hrqLahGMa7yzDjja67poc/H/gxu+1lkj6UtF7SaUkHJW2OiGMDLaQD26ckjUdE4zdg2P6JpC8k/enS0Fq2t0s6GxHPFP9QroiI3wxJbU/rCofxrqm2TsOM/0INrrsqhz/vRxNb9rWSTkbERxFxQdIrkjY1UMfQi4gDks5e1rxJ0u5ierda/7MMXIfahkJEzEbEe8X0OUmXhhlvdN11qWsgmgj7KkmftH0+reEa7z0k7bX9ru2JpotZwEjbMFufShppspgF9BzGe5AuG2Z8aNZdP8Ofl8UJum+6OyJ+KOlnkh4rdleHUrSOwYbp2ukOSberNQbgrKRnmyymGGb8NUlPRMTn7X1NrrsF6hrIemsi7DOSxto+31K0DYWImCne5yS9odZhxzA5c2kE3eJ9ruF6/i8izkTEfERclPSCGlx3xTDjr0l6KSJeL5obX3cL1TWo9dZE2A9KWm37NtvXSnpI0p4G6vgG2zcUJ05k+wZJ92n4hqLeI2lLMb1F0psN1vI1wzKMd6dhxtXwumt8+POIGPhL0ka1zsj/R9Jvm6ihQ13fk/Tv4nW06dokvazWbt1/1Tq38Yikb0vaJ+mEpL9LWjlEtf1ZraG9D6sVrNGGartbrV30w5IOFa+NTa+7LnUNZL1xuyyQBCfogCQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJ/wH1OL8A4UWmOwAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["- 정답 데이터 원핫인코딩 하기\n","    - 다중 분류 모델에 학습시키기 위해\n","    - 이중 분류에서는 sigmoid를 사용해서(0.5보다 높으면 1, 아니면 0) 답이 0과 1로 구분되어있으므로 원핫인코딩 할 필요가 없음\n","    - 다중 분류에서는 각 클래스를 0 ~ 1값으로 맞춰줘야 제대로 된 학습이 가능\n","    - 모델이 학습하면서 0 ~ 1사이의 확률 정보를 통해 오차를 비교하여 학습하는데, 클래스값 그대로 학습에 사용한다면 높은 값일 수록 오차가 높아져 제대로 된 학습이 불가\n","    - 신경망 출력층에서 10개의 클래스 값들에 대한 확률 정보와 비교하여 같은 스케일로 오차를 구하기 위해\n","\n","- pd.get_dummies : Pandas 에서 지원해주는 원핫인코딩 함수\n","- to_categorical : keras에서 지원해주는 원핫인코딩 함수"],"metadata":{"id":"nx-hQWWCmkZ8"}},{"cell_type":"code","source":["from tensorflow.keras.utils import to_categorical\n","\n","y_onehot = to_categorical(y)\n","y_onehot"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Yy66TbNYm1jx","executionInfo":{"status":"ok","timestamp":1665473697171,"user_tz":-540,"elapsed":5,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"a0703b91-8da2-460e-eb88-aca570b61c86"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 1., 0., ..., 0., 0., 0.],\n","       [1., 0., 0., ..., 0., 0., 0.],\n","       [0., 1., 0., ..., 0., 0., 0.],\n","       ...,\n","       [0., 0., 0., ..., 1., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 0.],\n","       [0., 0., 0., ..., 0., 0., 1.]], dtype=float32)"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","source":["### 모델링\n","- 1. 모델 구조 설계"],"metadata":{"id":"qe-kMX1HF-If"}},{"cell_type":"code","source":["model = Sequential()    # 뼈대 생성\n","\n","model.add(InputLayer(input_shape=(28, 28))) # 입력층\n","model.add(Flatten())    # 데이터를 1차원으로 변형\n","\n","model.add(Dense(units=64, activation='sigmoid'))\n","model.add(Dense(units=128, activation='sigmoid'))\n","model.add(Dense(units=64, activation='sigmoid'))\n","\n","model.add(Dense(units=10, activation='softmax')) # 출력층, 결과는 10개의 클래스에 대한 10개의 확률"],"metadata":{"id":"IqZxIW9SIIS4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["- 2. 모델 컴파일\n","    - 모델의 학습 및 평가 방법 설정"],"metadata":{"id":"GXt4qXNlJcDH"}},{"cell_type":"code","source":["model.compile(loss=\"categorical_crossentropy\",\n","              optimizer=\"SGD\",\n","              metrics=['accuracy'])"],"metadata":{"id":"7JPVJyu7J-AA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["- 3. 모델 학습"],"metadata":{"id":"9yUonipcJ44Z"}},{"cell_type":"code","source":["history = model.fit(X, y_onehot, epochs=50, validation_split=0.2, batch_size=128)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QpciYNaHKLrt","executionInfo":{"status":"ok","timestamp":1665473756453,"user_tz":-540,"elapsed":55900,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"6677661f-e430-494f-dcbe-2600d87ba7f3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","263/263 [==============================] - 6s 7ms/step - loss: 2.3046 - accuracy: 0.1243 - val_loss: 2.2796 - val_accuracy: 0.1685\n","Epoch 2/50\n","263/263 [==============================] - 1s 4ms/step - loss: 2.2702 - accuracy: 0.1967 - val_loss: 2.2578 - val_accuracy: 0.2064\n","Epoch 3/50\n","263/263 [==============================] - 1s 4ms/step - loss: 2.2475 - accuracy: 0.2702 - val_loss: 2.2339 - val_accuracy: 0.3475\n","Epoch 4/50\n","263/263 [==============================] - 1s 4ms/step - loss: 2.2223 - accuracy: 0.3634 - val_loss: 2.2067 - val_accuracy: 0.3670\n","Epoch 5/50\n","263/263 [==============================] - 1s 4ms/step - loss: 2.1928 - accuracy: 0.4287 - val_loss: 2.1739 - val_accuracy: 0.4349\n","Epoch 6/50\n","263/263 [==============================] - 1s 4ms/step - loss: 2.1565 - accuracy: 0.4576 - val_loss: 2.1328 - val_accuracy: 0.5761\n","Epoch 7/50\n","263/263 [==============================] - 1s 4ms/step - loss: 2.1103 - accuracy: 0.5337 - val_loss: 2.0796 - val_accuracy: 0.5261\n","Epoch 8/50\n","263/263 [==============================] - 1s 4ms/step - loss: 2.0517 - accuracy: 0.5433 - val_loss: 2.0128 - val_accuracy: 0.5600\n","Epoch 9/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.9773 - accuracy: 0.5652 - val_loss: 1.9295 - val_accuracy: 0.5864\n","Epoch 10/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.8863 - accuracy: 0.5969 - val_loss: 1.8293 - val_accuracy: 0.5882\n","Epoch 11/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.7814 - accuracy: 0.6141 - val_loss: 1.7188 - val_accuracy: 0.6330\n","Epoch 12/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.6687 - accuracy: 0.6429 - val_loss: 1.6034 - val_accuracy: 0.6585\n","Epoch 13/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.5540 - accuracy: 0.6701 - val_loss: 1.4878 - val_accuracy: 0.6883\n","Epoch 14/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.4410 - accuracy: 0.6954 - val_loss: 1.3771 - val_accuracy: 0.7114\n","Epoch 15/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.3329 - accuracy: 0.7241 - val_loss: 1.2721 - val_accuracy: 0.7364\n","Epoch 16/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.2320 - accuracy: 0.7461 - val_loss: 1.1743 - val_accuracy: 0.7636\n","Epoch 17/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.1392 - accuracy: 0.7686 - val_loss: 1.0892 - val_accuracy: 0.7852\n","Epoch 18/50\n","263/263 [==============================] - 1s 4ms/step - loss: 1.0549 - accuracy: 0.7925 - val_loss: 1.0095 - val_accuracy: 0.8012\n","Epoch 19/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.9783 - accuracy: 0.8065 - val_loss: 0.9398 - val_accuracy: 0.8176\n","Epoch 20/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.9114 - accuracy: 0.8189 - val_loss: 0.8770 - val_accuracy: 0.8287\n","Epoch 21/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.8517 - accuracy: 0.8315 - val_loss: 0.8241 - val_accuracy: 0.8332\n","Epoch 22/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.7994 - accuracy: 0.8417 - val_loss: 0.7745 - val_accuracy: 0.8374\n","Epoch 23/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.7529 - accuracy: 0.8490 - val_loss: 0.7322 - val_accuracy: 0.8476\n","Epoch 24/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.7108 - accuracy: 0.8568 - val_loss: 0.6978 - val_accuracy: 0.8557\n","Epoch 25/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.6748 - accuracy: 0.8621 - val_loss: 0.6615 - val_accuracy: 0.8595\n","Epoch 26/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.6407 - accuracy: 0.8678 - val_loss: 0.6329 - val_accuracy: 0.8650\n","Epoch 27/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.6100 - accuracy: 0.8726 - val_loss: 0.6036 - val_accuracy: 0.8633\n","Epoch 28/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.5812 - accuracy: 0.8757 - val_loss: 0.5795 - val_accuracy: 0.8727\n","Epoch 29/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.5578 - accuracy: 0.8792 - val_loss: 0.5554 - val_accuracy: 0.8748\n","Epoch 30/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.5316 - accuracy: 0.8848 - val_loss: 0.5348 - val_accuracy: 0.8792\n","Epoch 31/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.5093 - accuracy: 0.8882 - val_loss: 0.5110 - val_accuracy: 0.8837\n","Epoch 32/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.4909 - accuracy: 0.8929 - val_loss: 0.4971 - val_accuracy: 0.8863\n","Epoch 33/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.4710 - accuracy: 0.8953 - val_loss: 0.4768 - val_accuracy: 0.8908\n","Epoch 34/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.4563 - accuracy: 0.8990 - val_loss: 0.4623 - val_accuracy: 0.8898\n","Epoch 35/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.4402 - accuracy: 0.9004 - val_loss: 0.4484 - val_accuracy: 0.8935\n","Epoch 36/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.4262 - accuracy: 0.9025 - val_loss: 0.4367 - val_accuracy: 0.8981\n","Epoch 37/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.4124 - accuracy: 0.9030 - val_loss: 0.4252 - val_accuracy: 0.8974\n","Epoch 38/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.4003 - accuracy: 0.9064 - val_loss: 0.4154 - val_accuracy: 0.8986\n","Epoch 39/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3873 - accuracy: 0.9089 - val_loss: 0.4042 - val_accuracy: 0.9005\n","Epoch 40/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3781 - accuracy: 0.9109 - val_loss: 0.3950 - val_accuracy: 0.8975\n","Epoch 41/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3697 - accuracy: 0.9106 - val_loss: 0.3897 - val_accuracy: 0.9019\n","Epoch 42/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3604 - accuracy: 0.9134 - val_loss: 0.3828 - val_accuracy: 0.9025\n","Epoch 43/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3517 - accuracy: 0.9154 - val_loss: 0.3729 - val_accuracy: 0.9057\n","Epoch 44/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3429 - accuracy: 0.9157 - val_loss: 0.3626 - val_accuracy: 0.9105\n","Epoch 45/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3366 - accuracy: 0.9179 - val_loss: 0.3551 - val_accuracy: 0.9082\n","Epoch 46/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3268 - accuracy: 0.9199 - val_loss: 0.3493 - val_accuracy: 0.9117\n","Epoch 47/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3215 - accuracy: 0.9207 - val_loss: 0.3440 - val_accuracy: 0.9115\n","Epoch 48/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3142 - accuracy: 0.9216 - val_loss: 0.3422 - val_accuracy: 0.9105\n","Epoch 49/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3100 - accuracy: 0.9233 - val_loss: 0.3353 - val_accuracy: 0.9123\n","Epoch 50/50\n","263/263 [==============================] - 1s 4ms/step - loss: 0.3053 - accuracy: 0.9241 - val_loss: 0.3290 - val_accuracy: 0.9157\n"]}]},{"cell_type":"markdown","source":["- 학습 현황 시각화"],"metadata":{"id":"122b2O8kJ6rR"}},{"cell_type":"code","source":["plt.figure(figsize=(15, 5))\n","plt.plot(history.history['accuracy'], label='acc', c='blue', marker='.')\n","plt.plot(history.history['val_accuracy'], label='val_acc', c='orange', marker='.')\n","plt.xlabel('epochs')\n","plt.ylabel('accuracy')\n","plt.legend()\n","plt.show()"],"metadata":{"id":"-wU1L2qKJ7R2","colab":{"base_uri":"https://localhost:8080/","height":334},"executionInfo":{"status":"ok","timestamp":1665473756453,"user_tz":-540,"elapsed":8,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"951ed36f-650a-4299-8940-3dcc53495586"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}