ex05_이미지_다중분류(패션데이터).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"mount_file_id":"1O96pYuK9HWKuVt8UIjOxbbe5H0mPGI2b","authorship_tag":"ABX9TyNQH7tbLgVwf1JcRuKxSZR/"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["## 목표\n","- fashion 데이터(이미지)를 활용한 다중 분류 딥러닝 모델 구현"],"metadata":{"id":"IDH3-ASGB6dG"}},{"cell_type":"markdown","source":["### 데이터 불러오기\n","- keras에서 제공하는 fashion_mnist 데이터 사용"],"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\n","from tensorflow.keras.datasets import fashion_mnist"],"metadata":{"id":"8v-dpCQY8XsO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"],"metadata":{"id":"Ztj6CCJkuCbO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train.shape, y_train.shape, X_test.shape, y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p1KMbL0cuCuN","executionInfo":{"status":"ok","timestamp":1665473093384,"user_tz":-540,"elapsed":4,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"e5d40d22-f598-4097-b5c6-4b3c26abe68e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"]},"metadata":{},"execution_count":53}]},{"cell_type":"markdown","source":["> 훈련 데이터 샘플 출력해보기\n","- X데이터, y데이터 출력"],"metadata":{"id":"hymTzCA4wzeD"}},{"cell_type":"code","source":["plt.imshow(X_train[59999], cmap='gray')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"lE0wWz-CuC2A","executionInfo":{"status":"ok","timestamp":1665473098737,"user_tz":-540,"elapsed":5,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"fc6274fb-d65f-4c97-fcdb-6ec4c380740c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fc18e38a090>"]},"metadata":{},"execution_count":54},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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다중 분류를 위해 정답 데이터 원핫인코딩 하기\n","- 10개의 클래스 존재"],"metadata":{"id":"9OJ7XXzNxOyg"}},{"cell_type":"code","source":["y_train_onehot = pd.get_dummies(y_train)\n","y_test_onehot = pd.get_dummies(y_test)"],"metadata":{"id":"3zNh-d3HwzEI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_train_onehot.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"u3EqtucWxMri","executionInfo":{"status":"ok","timestamp":1665473104979,"user_tz":-540,"elapsed":8,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"5adad4da-a2aa-414e-b93d-77edd820d299"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   0  1  2  3  4  5  6  7  8  9\n","0  0  0  0  0  0  0  0  0  0  1\n","1  1  0  0  0  0  0  0  0  0  0\n","2  1  0  0  0  0  0  0  0  0  0\n","3  0  0  0  1  0  0  0  0  0  0\n","4  1  0  0  0  0  0  0  0  0  0"],"text/html":["\n","  <div id=\"df-40c33fdc-36a1-44ad-bc0c-4c3c0df74f37\">\n","    <div 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":57}]},{"cell_type":"markdown","source":["### 모델링"],"metadata":{"id":"tnuh9FSYxOWI"}},{"cell_type":"markdown","source":["#### 1. 모델 설계"],"metadata":{"id":"g-qRnppnxt_8"}},{"cell_type":"code","source":["model1 = Sequential()\n","\n","model1.add(InputLayer(input_shape=(28, 28)))\n","model1.add(Flatten())\n","model1.add(Dense(64, activation='relu'))\n","model1.add(Dense(128, activation='relu'))\n","model1.add(Dense(64, activation='relu'))\n","model1.add(Dense(32, activation='relu'))\n","model1.add(Dense(10, activation='softmax'))"],"metadata":{"id":"Wpyk9VExy19n"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 2. 모델 컴파일"],"metadata":{"id":"zJ5NLNoV8xb2"}},{"cell_type":"code","source":["model1.compile(loss=\"categorical_crossentropy\",\n","               optimizer='adam',\n","               metrics=['accuracy']\n","               )"],"metadata":{"id":"hWZ_nBBG0OpI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 3. 모델 학습\n","- batch_size : 한번에 데이터를 학습시키는 최소 단위 (디폴트 값은 32)"],"metadata":{"id":"KbuoaJU465Qx"}},{"cell_type":"code","source":["history1 = model1.fit(X_train, y_train_onehot, epochs=50, validation_split=0.2, batch_size=128)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F_EBw5320c4e","executionInfo":{"status":"ok","timestamp":1665473234843,"user_tz":-540,"elapsed":65072,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"18a0de0b-5da3-459d-dd59-d544ea111880"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","375/375 [==============================] - 2s 4ms/step - loss: 1.8085 - accuracy: 0.6742 - val_loss: 0.6583 - val_accuracy: 0.7642\n","Epoch 2/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.5644 - accuracy: 0.7996 - val_loss: 0.5193 - val_accuracy: 0.8141\n","Epoch 3/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.4839 - accuracy: 0.8284 - val_loss: 0.4782 - val_accuracy: 0.8296\n","Epoch 4/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.4392 - accuracy: 0.8426 - val_loss: 0.4624 - val_accuracy: 0.8344\n","Epoch 5/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.4174 - accuracy: 0.8486 - val_loss: 0.4568 - val_accuracy: 0.8409\n","Epoch 6/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3866 - accuracy: 0.8589 - val_loss: 0.4149 - val_accuracy: 0.8516\n","Epoch 7/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3749 - accuracy: 0.8620 - val_loss: 0.4103 - val_accuracy: 0.8524\n","Epoch 8/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3641 - accuracy: 0.8669 - val_loss: 0.4358 - val_accuracy: 0.8424\n","Epoch 9/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3524 - accuracy: 0.8717 - val_loss: 0.4154 - val_accuracy: 0.8543\n","Epoch 10/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3422 - accuracy: 0.8736 - val_loss: 0.3899 - val_accuracy: 0.8648\n","Epoch 11/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3403 - accuracy: 0.8765 - val_loss: 0.4021 - val_accuracy: 0.8672\n","Epoch 12/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3241 - accuracy: 0.8800 - val_loss: 0.3793 - val_accuracy: 0.8697\n","Epoch 13/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3141 - accuracy: 0.8830 - val_loss: 0.3825 - val_accuracy: 0.8638\n","Epoch 14/50\n","375/375 [==============================] - 2s 5ms/step - loss: 0.3117 - accuracy: 0.8851 - val_loss: 0.3758 - val_accuracy: 0.8725\n","Epoch 15/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3066 - accuracy: 0.8872 - val_loss: 0.3680 - val_accuracy: 0.8701\n","Epoch 16/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.3055 - accuracy: 0.8878 - val_loss: 0.4184 - val_accuracy: 0.8581\n","Epoch 17/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2961 - accuracy: 0.8893 - val_loss: 0.3541 - val_accuracy: 0.8773\n","Epoch 18/50\n","375/375 [==============================] - 1s 4ms/step - loss: 0.2877 - accuracy: 0.8935 - val_loss: 0.3720 - val_accuracy: 0.8735\n","Epoch 19/50\n","375/375 [==============================] - 2s 6ms/step - loss: 0.2884 - accuracy: 0.8929 - val_loss: 0.3966 - val_accuracy: 0.8612\n","Epoch 20/50\n","375/375 [==============================] - 3s 7ms/step - loss: 0.2851 - accuracy: 0.8942 - val_loss: 0.4128 - val_accuracy: 0.8612\n","Epoch 21/50\n","375/375 [==============================] - 2s 4ms/step - loss: 0.2762 - accuracy: 0.8965 - val_loss: 0.3722 - val_accuracy: 0.8755\n","Epoch 22/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2670 - accuracy: 0.9013 - val_loss: 0.3768 - val_accuracy: 0.8757\n","Epoch 23/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2679 - accuracy: 0.9001 - val_loss: 0.3697 - val_accuracy: 0.8756\n","Epoch 24/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2668 - accuracy: 0.9025 - val_loss: 0.3747 - val_accuracy: 0.8758\n","Epoch 25/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2606 - accuracy: 0.9036 - val_loss: 0.3634 - val_accuracy: 0.8796\n","Epoch 26/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2622 - accuracy: 0.9029 - val_loss: 0.3619 - val_accuracy: 0.8804\n","Epoch 27/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2585 - accuracy: 0.9048 - val_loss: 0.3586 - val_accuracy: 0.8790\n","Epoch 28/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2475 - accuracy: 0.9078 - val_loss: 0.3642 - val_accuracy: 0.8837\n","Epoch 29/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2408 - accuracy: 0.9110 - val_loss: 0.3757 - val_accuracy: 0.8839\n","Epoch 30/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2368 - accuracy: 0.9112 - val_loss: 0.3561 - val_accuracy: 0.8835\n","Epoch 31/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2459 - accuracy: 0.9089 - val_loss: 0.3740 - val_accuracy: 0.8819\n","Epoch 32/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2348 - accuracy: 0.9125 - val_loss: 0.3767 - val_accuracy: 0.8795\n","Epoch 33/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2340 - accuracy: 0.9122 - val_loss: 0.3696 - val_accuracy: 0.8832\n","Epoch 34/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2268 - accuracy: 0.9157 - val_loss: 0.3747 - val_accuracy: 0.8825\n","Epoch 35/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2266 - accuracy: 0.9161 - val_loss: 0.3701 - val_accuracy: 0.8821\n","Epoch 36/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2216 - accuracy: 0.9172 - val_loss: 0.3798 - val_accuracy: 0.8796\n","Epoch 37/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2249 - accuracy: 0.9160 - val_loss: 0.3757 - val_accuracy: 0.8791\n","Epoch 38/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2220 - accuracy: 0.9171 - val_loss: 0.3708 - val_accuracy: 0.8841\n","Epoch 39/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2123 - accuracy: 0.9213 - val_loss: 0.3995 - val_accuracy: 0.8726\n","Epoch 40/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2099 - accuracy: 0.9214 - val_loss: 0.3664 - val_accuracy: 0.8866\n","Epoch 41/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2047 - accuracy: 0.9239 - val_loss: 0.3847 - val_accuracy: 0.8749\n","Epoch 42/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2123 - accuracy: 0.9212 - val_loss: 0.4002 - val_accuracy: 0.8778\n","Epoch 43/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2067 - accuracy: 0.9224 - val_loss: 0.3871 - val_accuracy: 0.8844\n","Epoch 44/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2122 - accuracy: 0.9216 - val_loss: 0.4092 - val_accuracy: 0.8770\n","Epoch 45/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.2061 - accuracy: 0.9236 - val_loss: 0.3907 - val_accuracy: 0.8838\n","Epoch 46/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1980 - accuracy: 0.9271 - val_loss: 0.4024 - val_accuracy: 0.8827\n","Epoch 47/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1941 - accuracy: 0.9281 - val_loss: 0.3739 - val_accuracy: 0.8863\n","Epoch 48/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1931 - accuracy: 0.9281 - val_loss: 0.4080 - val_accuracy: 0.8827\n","Epoch 49/50\n","375/375 [==============================] - 1s 3ms/step - loss: 0.1975 - accuracy: 0.9273 - val_loss: 0.4166 - val_accuracy: 0.8721\n","Epoch 50/50\n","375/375 [==============================] - 2s 4ms/step - loss: 0.1925 - accuracy: 0.9285 - val_loss: 0.4288 - val_accuracy: 0.8808\n"]}]},{"cell_type":"markdown","source":[">학습 현황 시각화"],"metadata":{"id":"Sjr8G8sq8hlT"}},{"cell_type":"code","source":["plt.figure(figsize=(15, 5))\n","plt.plot(history1.history['accuracy'], label='acc', c='blue', marker='.')\n","plt.plot(history1.history['val_accuracy'], label='val_acc', c='orange', marker='.')\n","plt.xlabel('epochs')\n","plt.ylabel('acc')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":334},"id":"SRMQ_lKy0jDo","executionInfo":{"status":"ok","timestamp":1665471767170,"user_tz":-540,"elapsed":1140,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"22b4c36c-5282-4935-f889-a9c58ae89b62"},"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"}}]},{"cell_type":"markdown","source":["### 4. 모델 평가"],"metadata":{"id":"v19n-S3w8tco"}},{"cell_type":"code","source":["model1.evaluate(X_test, y_test_onehot)"],"metadata":{"id":"68qbYksb2lgH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665473367943,"user_tz":-540,"elapsed":819,"user":{"displayName":"민윤기","userId":"13288389921392377597"}},"outputId":"6ae6653b-45f1-4c05-b4f0-150ce591e87c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 [==============================] - 1s 2ms/step - loss: 0.4579 - accuracy: 0.8724\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.4579177796840668, 0.8723999857902527]"]},"metadata":{},"execution_count":66}]},{"cell_type":"markdown","source":["### 결론"],"metadata":{"id":"GiFNC3Wb9b28"}},{"cell_type":"markdown","source":["- 손글씨 데이터에 비해 결과가 비교적으로 낮게 나옴을 확인할 수 있다.\n","- fashion 이미지 데이터가 손글씨보다 많은 정보(픽셀값)들이 담겨져 있었고, 사람의 눈으로 보기에도 쉽게 분류되지 않은 이미지들도 존재했다.\n","- 입력 데이터에 더 많은 전처리와 튜닝이 필요하다고 판단할 수 있다."],"metadata":{"id":"1STi_mtB8m_f"}}]}