mnist test.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"mnist test.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1yaRBvxyqCs8zhouOP_cukdVlD1vo_8Ip","authorship_tag":"ABX9TyMY7igGgphyrISb96qswlBM"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"f_uDVQjI-j0t","executionInfo":{"status":"ok","timestamp":1653949538140,"user_tz":-540,"elapsed":407,"user":{"displayName":"문지영","userId":"17671481004354526232"}},"outputId":"d3d34efc-7ffd-473d-fb3f-d3376d5f094a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2.8.0'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":2}],"source":["import tensorflow as tf\n","tf.__version__"]},{"cell_type":"markdown","source":["mnist 데이터셋으로 훈련한 모델(MNIST_conver.ipynb에서 훈련시킨 모델)을 내 데이터셋으로 평가하기"],"metadata":{"id":"4Kl3i3lFatyi"}},{"cell_type":"code","source":["import os, shutil\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","from keras.preprocessing.image import ImageDataGenerator"],"metadata":{"id":"I7RiMx67-ufz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 원본 데이터셋 디렉터리 경로\n","original_dataset_dir = './mydataset'\n","os.getcwd()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"t47TlWHZ-zez","executionInfo":{"status":"ok","timestamp":1653949540306,"user_tz":-540,"elapsed":5,"user":{"displayName":"문지영","userId":"17671481004354526232"}},"outputId":"b307fa51-b17f-422a-9d6d-42d63524971b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["os.chdir('/content/drive/MyDrive/Colab Notebooks/2022-DL/')"],"metadata":{"id":"Nbl0Ci7b-5ZD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def invert(image):\n","    new_img = 255 - image\n","    return new_img\n","\n","test_datagen = ImageDataGenerator(rescale=1./255, preprocessing_function=invert)\n","\n","test_generator = test_datagen.flow_from_directory(\n","        original_dataset_dir,\n","        target_size=(28, 28),\n","        color_mode='grayscale',\n","        batch_size=5,\n","        class_mode='categorical')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IIEdkBbd_L5l","executionInfo":{"status":"ok","timestamp":1653949542262,"user_tz":-540,"elapsed":368,"user":{"displayName":"문지영","userId":"17671481004354526232"}},"outputId":"768226d4-eb2b-4581-d021-48248688b559"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 303 images belonging to 10 classes.\n"]}]},{"cell_type":"code","source":["from tensorflow.python.keras.models import load_model\n","model = load_model('mnist_convnet.h5')"],"metadata":{"id":"Du4Uj8p0BCHv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_generator.reset()\n","\n","print(\"-- Evaluate --\")\n","scores = model.evaluate(test_generator, steps=60)\n","print(\"%s: %.2f%%\" %(model.metrics_names[1], scores[1]*100))\n","\n","print(\"-- Predict --\")\n","\n","wrong_predict = []\n","for i in range(10):\n","  wrong_predict.append([])\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rpyTZrV2_NvW","executionInfo":{"status":"ok","timestamp":1653950174920,"user_tz":-540,"elapsed":1129,"user":{"displayName":"문지영","userId":"17671481004354526232"}},"outputId":"210cf318-bd55-4f03-cbbf-3a66f82d6e40"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["-- Evaluate --\n","60/60 [==============================] - 1s 10ms/step - loss: 0.7362 - accuracy: 0.8367\n","accuracy: 83.67%\n","-- Predict --\n"]}]},{"cell_type":"code","source":["for j in range(60):\n","  x_val,y_val = test_generator.next()\n","  y_pred = model.predict(x_val)\n","  for i in range(len(y_pred)):\n","    predict = np.argmax(y_pred[i])\n","    y_true = np.argmax(y_val[i])\n","    if predict != y_true:\n","      wrong_predict[y_true].append(predict)"],"metadata":{"id":"lk1Q4FLzHOdB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for idx, pred in enumerate(wrong_predict):\n","  print(idx,'에 대한 오답 수:', len(pred))\n","  if pred:\n","    print(' - 오답들: ', pred)\n","  print()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rf0eOFktG0Pe","executionInfo":{"status":"ok","timestamp":1653950521892,"user_tz":-540,"elapsed":282,"user":{"displayName":"문지영","userId":"17671481004354526232"}},"outputId":"781bc7c9-2c63-4bc0-938f-8a0f4b846192"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0 에 대한 오답 수: 0\n","\n","1 에 대한 오답 수: 0\n","\n","2 에 대한 오답 수: 0\n","\n","3 에 대한 오답 수: 4\n"," - 오답들:  [8, 7, 7, 7]\n","\n","4 에 대한 오답 수: 4\n"," - 오답들:  [5, 5, 6, 5]\n","\n","5 에 대한 오답 수: 1\n"," - 오답들:  [9]\n","\n","6 에 대한 오답 수: 4\n"," - 오답들:  [5, 5, 5, 5]\n","\n","7 에 대한 오답 수: 10\n"," - 오답들:  [1, 1, 1, 1, 2, 1, 1, 0, 1, 1]\n","\n","8 에 대한 오답 수: 13\n"," - 오답들:  [5, 2, 9, 5, 5, 5, 3, 5, 5, 4, 6, 5, 6]\n","\n","9 에 대한 오답 수: 13\n"," - 오답들:  [7, 1, 2, 1, 4, 2, 2, 7, 1, 1, 1, 1, 1]\n","\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"9u46YZ7ZIDZq"},"execution_count":null,"outputs":[]}]}