0116_1750.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1uPeqW35oBR4y8YXCA9T3ZyG6gOU1Kl-k",
      "authorship_tag": "ABX9TyN8Pvuk0ZmUr9WMkEpnaf2C",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/seiji0906/machine/blob/main/0116_1750.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # 使うデータ\n",
        "# import cv2\n",
        "# from google.colab.patches import cv2_imshow\n",
        "\n",
        "# path = '/content/drive/MyDrive/Colab Notebooks/相転移/'\n",
        "\n",
        "# files = []\n",
        "\n",
        "# files.append(path + \"beta=0.06938775510204082_step=99.jpg\")\n",
        "# files.append(path + \"beta=0.3989795918367347_step=99.jpg\")\n",
        "# files.append(path + \"beta=0.6510204081632653_step=99.jpg\")\n",
        "\n",
        "# for num, file in enumerate(files):\n",
        "#   img = cv2.imread(file)\n",
        "#   img = cv2.resize(img,dsize=(180,180))\n",
        "#   print(\"image\" + str(num))\n",
        "#   cv2_imshow(img)"
      ],
      "metadata": {
        "id": "SbF6s9YLlUxy"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "import glob as glob\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "path = '/content/drive/MyDrive/Colab Notebooks'\n",
        "\n",
        "\n",
        "folder = ['常磁性', '強磁性2']\n",
        "\n",
        "X_image = []\n",
        "Y_label = []\n",
        "\n",
        "for label,class_name in enumerate(folder):\n",
        "  files = glob.glob(path + \"/\" +  class_name + \"/*.jpg\")\n",
        "  for file in files:\n",
        "    img = cv2.imread(file)\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    img = cv2.resize(img,dsize=(180,180))\n",
        "    X_image.append(img)\n",
        "    Y_label.append(label)\n",
        "\n",
        "X_image = np.array(X_image)\n",
        "X_image = X_image.astype('float32')\n",
        "X_image /= 255.0\n",
        "\n",
        "Y_label = np.array(Y_label)\n",
        "Y_label = np_utils.to_categorical(Y_label, len(folder))\n",
        "Y_label[:5]\n",
        "\n",
        "\n",
        "\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X_image,Y_label,test_size=0.2, shuffle=True)"
      ],
      "metadata": {
        "id": "JHb-NVoCX89o"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.python import metrics\n",
        "from warnings import filters\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "inputs = keras.Input(shape=(180, 180, 1))\n",
        "\n",
        "x = layers.Rescaling(1./255)(inputs)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(2, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=\"rmsprop\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hvi_yd58aYow",
        "outputId": "9fdd4dc1-b796-40ba-ef84-357188bbfbf4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 180, 180, 1)]     0         \n",
            "                                                                 \n",
            " rescaling (Rescaling)       (None, 180, 180, 1)       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 178, 178, 32)      320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 89, 89, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 87, 87, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 43, 43, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 41, 41, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 20, 20, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 18, 18, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 9, 9, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 20736)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 41474     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 429,314\n",
            "Trainable params: 429,314\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 学習データで学習\n",
        "model.fit(X_train, Y_train, epochs=10, batch_size=32)\n",
        "\n",
        "#テストデータで精度確認\n",
        "score = model.evaluate(X_test, Y_test, batch_size=32)\n",
        "\n",
        "print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j1gLsWHXYIXC",
        "outputId": "2d51d7f5-6d30-4b5d-c24b-24243d4304b0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "112/112 [==============================] - 244s 2s/step - loss: 0.6797 - accuracy: 0.5724\n",
            "Epoch 2/10\n",
            "112/112 [==============================] - 242s 2s/step - loss: 0.6678 - accuracy: 0.5663\n",
            "Epoch 3/10\n",
            "112/112 [==============================] - 240s 2s/step - loss: 0.4795 - accuracy: 0.7900\n",
            "Epoch 4/10\n",
            "112/112 [==============================] - 240s 2s/step - loss: 0.3014 - accuracy: 0.8753\n",
            "Epoch 5/10\n",
            "112/112 [==============================] - 242s 2s/step - loss: 0.2081 - accuracy: 0.9676\n",
            "Epoch 6/10\n",
            "112/112 [==============================] - 240s 2s/step - loss: 0.1003 - accuracy: 0.9841\n",
            "Epoch 7/10\n",
            "112/112 [==============================] - 240s 2s/step - loss: 0.2881 - accuracy: 0.9256\n",
            "Epoch 8/10\n",
            "112/112 [==============================] - 240s 2s/step - loss: 0.1372 - accuracy: 0.9645\n",
            "Epoch 9/10\n",
            "112/112 [==============================] - 242s 2s/step - loss: 0.1510 - accuracy: 0.9541\n",
            "Epoch 10/10\n",
            "112/112 [==============================] - 239s 2s/step - loss: 0.0372 - accuracy: 0.9947\n",
            "28/28 [==============================] - 16s 579ms/step - loss: 2.1465e-04 - accuracy: 1.0000\n",
            "[0.00021465045574586838, 1.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/Colab Notebooks'\n",
        "\n",
        "\n",
        "folder = ['相転移9']\n",
        "\n",
        "X_image = []\n",
        "\n",
        "for label,class_name in enumerate(folder):\n",
        "  files = glob.glob(path + \"/\" +  class_name + \"/*.jpg\")\n",
        "  for file in files:\n",
        "    img = cv2.imread(file)\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    img = cv2.resize(img,dsize=(180,180))\n",
        "    X_image.append(img)\n",
        "\n",
        "X_image = np.array(X_image)\n",
        "X_image = X_image.astype('float32')\n",
        "X_image /= 255.0\n",
        "\n",
        "print(len(X_image))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDWQJmhgYN-m",
        "outputId": "1bacedb2-6402-45f4-accf-795689c474e0"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.predict(X_image)\n",
        "# plt.plot(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jg4NhT8zYRUr",
        "outputId": "cf660f91-d64c-4bcb-fa22-54ddb6c32028"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32/32 [==============================] - 18s 561ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(history)\n",
        "import matplotlib.pyplot as plt\n",
        "from statistics import mean\n",
        "\n",
        "# print(history)\n",
        "plt.plot(history)\n",
        "\n",
        "# print(history)\n",
        "\n",
        "predict = []\n",
        "\n",
        "\n",
        "\n",
        "# for i in range():\n",
        "\n",
        "\n",
        "for i in range(len(history)):\n",
        "  predict.append(history[i][1])\n",
        "\n",
        "# print(predict)\n",
        "\n",
        "# print(mean(predict[0:9]))\n",
        "\n",
        "# result = []\n",
        "\n",
        "# for i in range(50):\n",
        "#   result.append(mean(predict[10*i:10*i+9]))\n",
        "\n",
        "\n",
        "\n",
        "# print(result)\n",
        "\n",
        "\n",
        "\n",
        "# betas=np.linspace(0.2, 0.6, 50).tolist()\n",
        "\n",
        "\n",
        "# fig, ax = plt.subplots()\n",
        "# ax.errorbar(betas, predict, capsize=4, marker='.')\n",
        "# plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "TYzBCBwv0dKZ",
        "outputId": "b0e5894e-3484-4407-fd8c-b53c366a2710"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwcVb338c9verYsk8k2xJA9JCwRw2JEEBGQiAEVXFDJvSr3quB9FMQVQbzoxZUHF9SLPiIqj9wriMuFPBiMgiigJDLsWYgMCQkDJJns6+zn+aNqMp2ZXqpnqru6qr/v12ugu/pM1enUzLfPnDp1jjnnEBGR+KuKugIiIhIOBbqISEIo0EVEEkKBLiKSEAp0EZGEqI7qwBMnTnQzZ86M6vAiIrH06KOPbnXONWV6LbJAnzlzJs3NzVEdXkQklsxsQ7bX1OUiIpIQCnQRkYRQoIuIJIQCXUQkIRToIiIJkTfQzeynZrbFzFZmed3M7Htm1mJmT5nZieFXU0RE8gnSQr8FWJTj9XOAuf7XJcAPh18tEREpVN5x6M65B8xsZo4i5wM/d948vMvNbKyZTXbOvRxSHQ/xyPPbefAfbcXYdazU16Zo7+zp32DW/xBwwPqt+5g9cdSg7x00YbJztO44wIwJo9iwfR8zxo/ipZ0HGF1fzebd7bzy8Ea27+ugqaGOli17md00GoCxI2r40zNbaO/uZf6URkbUpujucdRUGzVVVbR39dDe3UNddYqeXkd3by+9Dtq7epg4uo6unl7eccIUHvhHGxu27ee0I5vYuqeDZ7fsZcyIaiaOrqNtTwfHTR3LcdMa2dfRw+72Ljq7e5kwupYH/J8D5+Bdr55KTc8Bdm1az+a6GUwZO4KXdh7gsbXrOH16Dakxh7OiZQtv7lxGzbFvh7HToKcbVt8JoyfBrNO8f4sDO+GB66FzL5z+Odi0EqqqoPVROPx46O2ByfOhdhQ0/wyqUjByArz0BDQdBTueh72boWYETDsZdqz39ltdD3s2eWV7u6BtrVemcSpMPQmefwjqGuDADti+DiYcAfu3Q3UtNBwOVtV/5vqmvDaDHRtg3Axv2/bnYPwR/ed1z8tQ3+jVNYihTKXdtR+q66AqS5Roeu7MjloEU14d+m7DuLFoCvBC2vNWf9ugQDezS/Ba8UyfPn1IB3tsww6+f3/LkL43KdJ/R8yK/ztz91P5P5sfGOKH7A33Pnvw8c0PrR/SPgC27evkY61X0PjcfZzYfisNI+vZub+LJ+ouZqztA6C753XUpP4G934BrtkOD30b7v8qAPv+5U9c21zDV3dfRfXGh7ydPnpL9gOe+AF47Oe5K5Xv9Vxa/pinQN/HdqGv5WP5ixwiyHEK3WcFaHhF2QZ6YM65m4CbABYsWDCkn7iPnH4EHzn9iPwFE6y317G3s5uGumrMMv+yfOHOp/mv5Rt56HNnMnXcyJz7+8Ttj3PnEy/x2lnjWbF+O19/56s4bupYLr/9cZ7dspf//KcT+Mrdazjz6CYmjKrj1DkTaWqo4y3fe5CObq+V/e9vncfI2hQ793ext6OLb9zzDO88cSqj66o5enIDdz7+Il9b+kzB73UE7QAcoD5nuXVt+2D9XwAwHDv3dwEcDHOAY2zjwcftf/4W7VvWMdZ//sBfH+SXT8/lKyMD3r2cKawv+n9QMwpufuMhm91R52Lv/LFXvy1rvJbz9FPg8Vth3Z/7C55zPdzzWe/xm78Gyz7vPf73rZCqGXy8h26Ae78I886HzatgWwu888cw/z2w7Tn4vn8560u7gr2noejc77XOq2uLdwwJLIxAfxGYlvZ8qr9NiqSqyhhTn+EXPM01b30li0+anjfMAb7+zvl8+LTZ/OiBdQDUVVcx7/AxXLHoaC7+eTMnzRzP8s+fNej71n7lnEHbXtGYAuq5+aLXHLK9viaV9fjXXzCfz/76KQBed8QE/vbctoOvran/IN2uijkd/5XzPew60AW93TnL7E/7UKh/4Kv8T/eZLPZ/Axo6NgNzqeltz7mPnCYe6bW8Zr0B1j9wcPPa9rEcXTcajn6L99XnVRf0P375SZj0qv5An3Rs/2uZwhy8bpyBqvx/52xdIGGrzf/zJaUTxrDFJcAH/NEuJwO7itV/LsHVVlfxysMbA5UdUZvi2CmNfOQNs2lqqOO0ud68P2+aN4nnv/EWDhuTu3UcRF119h+1xhH9gTV13IhBr1dbb569Ow4/sHaoVQOg1g0jyPtU12XcvLejJ+P2Q0w+zuur75MtxNNVZfiQrPK/r1SBLmUlyLDF24CHgaPMrNXMPmRm/2Zm/+YXWQqsA1qAHwMfLVptpaiOndLII1cvpKkhczANR22WQL/hvcez8JhJnDx7PAAja/uD6P7PnBFo32+pWsG1m/p/7IL22B7a5xdCP2+mFvNQpQJ0YWQK/ZQCvZIFGeWyOM/rDvhYaDWSREpVZQ70t58wBYDT5jaxfN126mr6y40fNTjUmtjJVsbg0toi86ueC1QHy3EBz8wwcvwlcNg82LJ68PZj3wUrf+M9ToX4QRiohZ7h11ct9IqmO0WlLBwzuQGAE6aNO7ht4PXeyWzjkfqPcnn1bw9uu/kDC9jsxg/7+GbGCDqzF5h4ZObtdQ39j7N8aLlCWv+fWAmfeLo/mHPJVCblB3mm7hhJPAW6lES+SHvj0ZP4y2fPYNGxr8haZpLtAOCMqicPbls4bxKzAi+UkquFDtXk6uvO8r0W8q/Q2GkwdnqwLhf1ocsACnQpiSBjVGdMOPQGmMDtWjs02HJ1rWTdhRmpnIGexav/NcjeC99vKkAgZwp09aFXNJ11qRi5YnX83mf559SOwnc6KuNKYMMXpIWeSV+QK9Arks66lEQp7xUM2kI/par/IufMtvv5TIBu68EHM7jsMe92/zANNdAPttDVh16JFOhSEguPmcTZ8ybxh9X5g+/eT72BuupU1rtgBwoa4APLzaoKI4TNm3dlQva7l13A93GIIC3sTHM+9PWhD+WYEnvqQ5eSGFGb4qYPLAhUds5hDUwbH5M7EIsVnIW20PvCXS3ziqZAl7IV/KLooSXPrHqCyWwbXGzIE1ZFIMg49EwfJprdsKKpy0US5//U3sB2N5oTO24qwdGK1EKvqobG6XDG54qzf0kkBbrEnmUI1fG2N9yDZGv5FqvLxQw++XRh5YtZH4kFdblI2QqeTUEvig7D6y4b8l4LulO0EC5twYv33AonvA/Gzy7OsSQWFOgiQUw7yZsRsZzMONX7/8n/CybNg/Nv1EXRCqcuFylbmbpSMpcb2rDFgvVmmLwr0J8RRWqhN0wq7uIVEjtqoYsE9e6fDZpmQKScKNAl9oL2tQ+7nTxxLnxy1dAOHoW3fQ/e/z9R10JKSIEuZStwUBdj6PUr5sNpnx68fcxkmDA39/e+5uJDnhbtomg+r74Ijnhj/nKSGAp0qRgF9aHXNcBZ12TZUXpAZwjreefBZ4MtuiESJgW6JECEd0dm/TPCApQRCZcCXSpIWMGfp4UOCnGJhAJdYq/k2bl1bYkPKBKMAl3KViwauQEqqemypFQU6BJ7QXN/yJ8POceeq8tFyofuFJWSGsM+RtDBZsbnLRv0TtFC2sA9VJEiwx2f2VzVSs6PgiAXRUVKRIEuJfVg3eU02n5mtv+ioO/7YOoeerOEZPAWumNX9URGdO9iBB3BvqmuIeDeBx5MgS6lp0CXkmq0/QCcNnciJ04fF/j7rqm5Netr0S5coRa6lA8FukTim+8+jklj6nOWCbuR6wW/hXvnZrZKqoUuEdBFUUmAKMNTwS3lQ4EukQhz0tng0+eWkoJeSk+BLhUl9N52dblIGVGgSzTC7MbWRVERQIEuZcxCbuUaDhf2RdGsB1OgS+kFCnQzW2Rma82sxcyuzPD6dDO738weN7OnzOzc8KsqSRL8pqEg+yqshV6SUS5qoUsE8ga6maWAG4FzgHnAYjObN6DYF4A7nHMnABcCPwi7olJ5wo7EvmGLYe9VpFwEaaGfBLQ459Y55zqB24HzB5RxwBj/cSPwUnhVFMkt7K6ZAg9e2HaRIgoS6FOAF9Ket/rb0n0JeJ+ZtQJLgcsy7cjMLjGzZjNrbmtrG0J1JSnCzbtynM9QgS6lF9ZF0cXALc65qcC5wK1mNmjfzrmbnHMLnHMLmpqaQjq0JFUxGrmOsONfLXQpH0EC/UVgWtrzqf62dB8C7gBwzj0M1AMTw6igJFOYcRd0keiiDG/URVEpI0EC/RFgrpnNMrNavIueSwaU2QicBWBmx+AFuvpUZFiK0TfujXApxSgXkdLLG+jOuW7gUmAZsAZvNMsqM7vWzM7zi30auNjMngRuA/7FOVeOHZtSJkIN64C7KukNSAp6iUCg2Radc0vxLnamb7sm7fFq4NRwqyYSTCFB7axENxapy0UioDtFJRKJjzu10CUCCnSJvYLWFHUlGuSoQJcIKNAlEpHlnXJWEkyBLrEXfD50XaeXZFOgSyTCnJyrECWbbVEkAgp0ib2wVzYSiSsFukQjorlc1DqXJFOgS+wVNMqloO8QiRcFuiRAoQtciCSTAl0iEcWwRbNiLHAhUj4U6BJ7hc4Lo350SSoFukRieJE6sNOkkIuiIsmlQJfYU3tbxKNAl0gMb/rcAd9bwEzNurFIkkyBLhVDNxZJ0inQJRKhLkEXdA06kYRToEvFMFz4S9CJlBEFukQi3HHowXamGJekU6BL7My3dbw3df/B55b1ouih270WOjgluySUAl0iMZzpc6vMcV3Nj9N3FvCY6muXZFOgS+xly/PMAa5hi5JcCnSpGIpxSToFukSiFJNzDTxEX4tdLXRJKgW6xF7wvnGnMJdEU6BLxVCUS9Ip0CX2srXQB27vG7YoklQKdIlENDcW9fWhiySTAl1iL3gLHd36L4mmQJdIDOfGIhHJTIEuiaVhi1JpAgW6mS0ys7Vm1mJmV2Yp8x4zW21mq8zsF+FWU5ImzD70oMMWvXIBD9wweegVEolIdb4CZpYCbgTeBLQCj5jZEufc6rQyc4GrgFOdczvM7LBiVVhkqIJEeWvVFKa+/Utw1DnFro5I6IK00E8CWpxz65xzncDtwPkDylwM3Oic2wHgnNsSbjUlaUJd4CLgRVH6ZlvMt8P574G6hhBqJlJaQQJ9CvBC2vNWf1u6I4EjzeyvZrbczBZl2pGZXWJmzWbW3NbWNrQaiwxRkA8R9a9LnIV1UbQamAucASwGfmxmYwcWcs7d5Jxb4Jxb0NTUFNKhJY6Gt0h0wGNkvLFIsy1KcgUJ9BeBaWnPp/rb0rUCS5xzXc659cA/8AJeJDLnHJvtwqYCXZIpSKA/Asw1s1lmVgtcCCwZUOZOvNY5ZjYRrwtmXYj1FMkqWzxffe7RA8r1tdh1r6gkU95Ad851A5cCy4A1wB3OuVVmdq2ZnecXWwZsM7PVwP3AZ51z24pVaYm/cNvIWS6KDjiI7hSVpMs7bBHAObcUWDpg2zVpjx3wKf9LpKSCxnOQ8epqu0uc6U5RiUS410QLm21RoS1JpUCXihF0TkaRuFKgSyTCGLY401729pUthF2mG4sU2JJcCnSJrXtqr/IfZbsoqvCWyqJAl9gaYZ1Arv74wX3o+ah/XeJMgS7xN6hrxTN4+lzd2i/JpkCXihF0ml2RuFKgS+wVNh+6SHIp0CWxMnW55KMuGYkzBbrEXtCLolWWf9iiAl3iTIEu8ZelJ0ULUUulUaBL7AVtoZfaw7M+FunxpfIo0CUBsjbRM5RUq12SS4EuIpIQCnRJrgw3HGngoiSZAl1iz7L1uAyhd0VdMhJnCnSJvwIuiuYfthie9Vv3hbg3kfwU6BJ72e4Ajbqt3br9QMQ1kEqjQJfkyjJpl0hSKdAl/rL2oQ9uoyviJckU6CKHiLqjRmToFOgSe1lHs2QctqjAluRSoEsClOdFUZFSU6BL7GUP7swt9FxBH6SPvc01BiglUnoKdIm/bEvQFamJfkHnF4uzY5FhUqBLxRnuSJcOVxNKPUTCpkCXaIQ4Rtyy3PufbUqA4dKFVSlXCnSRNEHCWoEu5UqBLtEIs4WeJWBTGx4YfFiFsSSYAl3iL8uHQ+2dHy7O4YqyV5HhU6BLRMLsQy/gqC7fsEW14CW+AgW6mS0ys7Vm1mJmV+Yo9y4zc2a2ILwqiuRT6jazQl/KU95AN7MUcCNwDjAPWGxm8zKUawAuB1aEXUkREckvSAv9JKDFObfOOdcJ3A6cn6Hcl4HrgPYQ6ydJFdHUts5yt+eD1ErdMlKuggT6FOCFtOet/raDzOxEYJpz7ne5dmRml5hZs5k1t7W1FVxZkYw077kIEMJFUTOrAr4NfDpfWefcTc65Bc65BU1NTcM9tMRamBdFg7eYnctfdl9HNy7Hh4Q+PqRcBQn0F4Fpac+n+tv6NADHAn82s+eBk4ElujAqpRNexHb1OF75xWXc8rfncxxNXS5SnoIE+iPAXDObZWa1wIXAkr4XnXO7nHMTnXMznXMzgeXAec655qLUWJIhqj50cnei9/iv3bNyU2kqJBKivIHunOsGLgWWAWuAO5xzq8zsWjM7r9gVFMnHSvzhoC4XKVfVQQo555YCSwdsuyZL2TOGXy1JPsWiSNh0p6jEXkF3ig7zda+M+tClPCnQJRqhdpOotS8CCnSpMGG0rtVCl3KlQJeIhDgOXTcWiQAKdKkwLu2/mQVZ4EKkPCnQJRphtqqLtRp09gOW+HgiwSjQJfZMbWYRQIEuFSbfBU2tKSpxpkCXiIS5pqha6CKgQJcKE86wRZHypECXaIR5UVTDFkUABbpUGIdWLJLkUqBLROI8bFGkPCnQJfZKfVFULXQpVwp0iUaUC1wM43WvjEh5UqBL7GkuFxGPAl0iElELPYTDqstFypUCXeLPwvtwUFhLnCnQJRpR9aGHMCJGoS/lSoEusWcKWBFAgS5JoIuiIoACXSIT4uRchSwS7fINWxSJLwW6xF+JW+jqQ5dypUCXaER2Y1EY+1CgS3lSoIukUVhLnCnQJfYKmctF86FLkinQJf7KsFHtNPJGIqBAl2iEGHgh3igaSKAJvJTnEgEFulQUR+4umrD60JXnEgUFukQkzMgrv2GL6nKRKAQKdDNbZGZrzazFzK7M8PqnzGy1mT1lZveZ2YzwqyoVpwih6DCNZJHEyhvoZpYCbgTOAeYBi81s3oBijwMLnHPzgV8D/zvsikrCxLgFm17zbC3x+L47ibMgLfSTgBbn3DrnXCdwO3B+egHn3P3Ouf3+0+XA1HCrKRUpYOgXNmxx+NJb+NmqGOPPK4mxIIE+BXgh7Xmrvy2bDwH3ZHrBzC4xs2Yza25rawteS5EwhZi22fbk1EaXCIR6UdTM3gcsAK7P9Lpz7ibn3ALn3IKmpqYwDy2xE+fA07BFKU/VAcq8CExLez7V33YIM1sIXA2c7pzrCKd6UtmKc1F0OK8PKu+8gZAi5SBIC/0RYK6ZzTKzWuBCYEl6ATM7AfgRcJ5zbkv41ZTECfPGotD2VDg1xKWc5A1051w3cCmwDFgD3OGcW2Vm15rZeX6x64HRwK/M7AkzW5JldyLBBQ79Ai6K5pkPvVC6KCrlJEiXC865pcDSAduuSXu8MOR6SeKVZws9rMDXRVGJgu4UlQQobLbFMG8syhbcaqFLFBToEo1AiVecVMw9l0tw21xD9i6XwqokEopAXS4i5ayQG4vCcmnnZTzh5nBvyY8skp0CXSISIISLMpdLbr0Bu2Pu7j0l93HU5yIRUJeLROPbx8BNZ0Zdi0F6C/yVUJeLlBMFukTnpcdKfsjQbyzSRVEpIwp0KWNBJ+cK7kBXT86wDdrl0ifrvhToEgEFuiRAeLMtDuxy2bm/k10HuoZQIyW6lJ4uikr5iqDfYmCgH3/tHwF4/htvyVg+UA2V7VIiaqGLpAnS5TK7adTBx1kXuFCISwQU6BJ7hS1wkTuwgwR6egl1oUs5UaBLGYuiyyWkuVzSmugKdykVBbrEXwGJmX/YovcrsftAF19asirv4TJ1raxYt41Xf0X3kErp6aKolK8IOqJ7/EB/ZtMentm0Z9DrzjnWte1L2zB4H9+979liVU8kJ7XQJfbCXCQ6V5fLPU+/zKyrDplFuqyHJ575zT/zmV89GXU1pIQU6CJpcgX6DffGq+W9fus+fv1oa9TVkBJSoEsZK33r1+X4laipHhz2Gp4o5USBLglQSKrmGbaYY8Wi2tTgX5co83zTrnZmXvk7Hnp2KyvWbeMdP/grnd29EdZIoqaLolK+AjZ/w1yCLluXy7Ob9/DYxp2DtgeZJrdYU+k2b9gOwH+v2MDaTXtYt3UfL+zYzxFNo4tyPCl/aqFLAgQPzC6XGrTtjI5v8YNub73zbIH+nXv/McwjF8Y5x8wrf8dPHlpfpCNIEinQpaJ0MzjQn3eTeclNAHL3oZdSd6/3UfHlu1dnLWP+h4+F+SeKxFp5/PSKZBR++7crSy9j39DH0KbPHabunsJ2rGuzAupDl1JqbQ5WbscGqK6D2lH5yxZoYKA/1zsZgKqhBnqRorSzZ2gXN3t7Fe2VTC10KY2NK+Dms4KV/e58+NZRRWn+dpPi7+6og88Xdl4PQBVegBa6BF2xmsbdBQZ638dQV4Ete0kWBbqUxq4XirZrKyD4u0jx+a4PH3zeN7dLXwu90CXoAH7+8POh35EZJJgz/XXQ3et/MKmlXpEU6FJRul2KDmrTtngBftKMsUD2LhfLsr3HOa65a1Xod2R2DbHLpe+DoFuBXpEU6FLGgoZS8PDqznLZaESN96tQaJfLHY/kD/KhROtQA72vq6ZHgV6RFOhSHnZuhO8cC9sLHHf91B1M23hn4OJdGYYtApjrAQoP9M172gsqn8+e9i72tHcFamFnCu2+bX1dL1JZNMpFysOTv/T62R/7v/3bgvSN//bigg6TaRy6fzAge5fLpt2Zg/uQhSycw4Y5KHz+f/wB5+Duy16ft2ymfvauvkDXxdGKpBa6lEa2cG7+GfT2wm6/66K7M/2bAu9+ee8xfLf7HXnLZQt0OzjKJXMgP7phR8btt/29/2JvGCNM+v6ZgnS5pI+EcQO2qQ+9MinQpd+eTbDkMug6EP6+ezoyb7/7E7DmLnj0Fu95V9riEdfNCrz7SWNH8Z3ud+ctl+3Gor4kHc4SdO3dPUP+3oGCBHKmMn0fKupDr0yBAt3MFpnZWjNrMbMrM7xeZ2a/9F9fYWYzw66oFMg56M4SogP19sL+7XDftfDYz+GZ33nbX34Kbn4TdAxeuadg3Tn6mjv39z/euyXtheChNKOpMVg1/Llctroxh2xP+S304dz639F1aKt6DyMAaK/Kf4PUzv2dhzzvCjBrYnoLve9jqK/vXH3olSlvH7qZpYAbgTcBrcAjZrbEOZc+ycSHgB3OuTlmdiFwHfDeYlRYAvr7TXDPFXDFehg5PnfZ+78CD34L5iz0nvd0QfsuuOdz0Pp32PA3aLkPZr7ea73PWehNIHLLW6FzD5x5NRx3Yf/+ujtg2dXwqnfDyAkwcc6ArpQB7vpo/+O1S7OXy6FqxDiqq/K3rvsuin5/zs0c0d0Ca73tY+q8IDcbeqC3d/Ww60AXO/d3AfCLnrOopoeqwxazEFjXtpeXd7WzfV8np86ZyB3NL/D7lZt4edcBNu/u4D//6YSD+9rfmb+1n6mF3q0WekWzfFN7mtkpwJecc2/2n18F4Jz7elqZZX6Zh82sGtgENLkcO1+wYIFrbg54K3i6x26Fv32/8O9Lkt5u6NwL9Wmt0oH/1Nv81XUap0F1/eB9HLx4Z7B1bYaDGAdbyKm6wV0mqVroSQvpqmoYczjsbfPKut7sZYvhdR/nrKfP4g+7307Ksv9Mf6zz4/yu92Se+fIirl+29uBshne+9zCOv2shF436IX/Zlr+1f/Ls8Sxftz1w9SY31vPyrqGNiJncWM+IWu+D6OBZM6Nly14AalJ2sKvlsIY6xoyooaunlw3bvL985hxWvOl097Z3k6oy6muyfxDmu1BciXOLffysubztuMOH9L1m9qhzbkGm14KMcpkCpN/m1wq8NlsZ51y3me0CJgBbB1TkEuASgOnTpweq/CAjJ8Bhxwzte5OkZqTf3+z/OhzyS2Nw2NGw/kGY+poM3+wHXt+HwIQ58GKz1wJf9xeYfQZsXgkjJ8Kmp+Dot8KWVTD+CNj4MMx6gxfg3e2w8jdQVQNHnQM1I7wPm1Qt7N0MI8Z7Lfr6Rug+4A1NrBsDB7ZDbYP3V8A534DVd3nzvLzqAq97Z91foG40jGqCfVth8nHe/jt2w+6XoLcH6hrguT95j2vq4fQruO2UGpav+SOnjtvl/Yxseopdq/5A6/jXMaZpCo0bltG+8TTufttrqK9J8emzj8SAaeNHctzxM+CEXXxt5wHe/5MVdPc4rnvXfB7buIMtu9t5ftt+jp82ll7nOHveK5gwupZv/mEtKTOqU8YTL+xiwYxxPLtlD1v2dLBtbyevnzuRDdv2MW3cSOprUjzZupNJDfXs2N/J1r2dbN3bwTGTx3D2vEn8fuUm5kwaTev2/TzZuotTZk/guba9TG6sZ+q4kZildUD5D46a1MDydds4efYEenody9dv4zUz+/8aS1UZE0fV0dRQF9IP3WD7Orupq66iOsPiH+l1zaac12QtpsYRNUXZb5AW+gXAIufch/3n7wde65y7NK3MSr9Mq//8Ob/M1kz7hGG00EVEKliuFnqQDsMXgWlpz6f62zKW8btcGoFthVdVRESGKkigPwLMNbNZZlYLXAgsGVBmCXCR//gC4E+5+s9FRCR8efvQ/T7xS4FlQAr4qXNulZldCzQ755YAPwFuNbMWYDte6IuISAkFuvXfObcUWDpg2zVpj9uB/Hd1iIhI0ehOURGRhFCgi4gkhAJdRCQhFOgiIgmR98aioh3YrA3YMMRvn8iAu1ArgN5zZdB7rgzDec8znHNNmV6ILNCHw8yas90plVR6z5VB77kyFOs9q8tFRCQhFOgiIgkR10C/KeoKREDvuTLoPVeGorznWPahi4jIYHFtoYuIyAAKdBGRhIhdoOdbsDquzGyamd1vZqvNbJWZXe5vH29mfzSzZ/3/j/O3m5l9z/93eMrMToz2HealLCwAAAOLSURBVAyNmaXM7HEzu9t/PstfaLzFX3i81t+eiIXIzWysmf3azJ4xszVmdkoFnONP+j/TK83sNjOrT+J5NrOfmtkWf8Gfvm0Fn1szu8gv/6yZXZTpWNnEKtDTFqw+B5gHLDazedHWKjTdwKedc/OAk4GP+e/tSuA+59xc4D7/OXj/BnP9r0uAH5a+yqG4HFiT9vw64DvOuTnADrwFyCFtIXLgO365OPou8Hvn3NHAcXjvPbHn2MymAB8HFjjnjsWbgrtvIfmknedbgEUDthV0bs1sPPBFvGU+TwK+2PchEIhzLjZfwCnAsrTnVwFXRV2vIr3Xu4A34a1LP9nfNhlY6z/+EbA4rfzBcnH5wlv96j7gjcDdeAukbgWqB55vvPn4T/EfV/vlLOr3UOD7bQTWD6x3ws9x33rD4/3zdjfw5qSeZ2AmsHKo5xZYDPwobfsh5fJ9xaqFTuYFq6dEVJei8f/MPAFYAUxyzr3sv7QJmOQ/TsK/xQ3AFUCv/3wCsNM51+0/T39PhyxEDvQtRB4ns4A24Gd+N9PNZjaKBJ9j59yLwDeBjcDLeOftUZJ9ntMVem6Hdc7jFuiJZ2ajgd8An3DO7U5/zXkf2YkYZ2pmbwW2OOcejbouJVQNnAj80Dl3ArCP/j/BgWSdYwC/u+B8vA+zw4FRDO6WqAilOLdxC/QgC1bHlpnV4IX5fzvnfutv3mxmk/3XJwNb/O1x/7c4FTjPzJ4HbsfrdvkuMNZfaBwOfU9JWIi8FWh1zq3wn/8aL+CTeo4BFgLrnXNtzrku4Ld45z7J5zldoed2WOc8boEeZMHqWDIzw1ubdY1z7ttpL6UvwH0RXt963/YP+FfLTwZ2pf1pV/acc1c556Y652bincc/Oef+Gbgfb6FxGPx+Y70QuXNuE/CCmR3lbzoLWE1Cz7FvI3CymY30f8b73nNiz/MAhZ7bZcDZZjbO/+vmbH9bMFFfRBjCRYdzgX8AzwFXR12fEN/X6/H+HHsKeML/Ohev//A+4FngXmC8X97wRvw8BzyNN4og8vcxxPd+BnC3/3g28HegBfgVUOdvr/eft/ivz4663kN8r8cDzf55vhMYl/RzDPwH8AywErgVqEvieQZuw7tO0IX319iHhnJugQ/6778F+NdC6qBb/0VEEiJuXS4iIpKFAl1EJCEU6CIiCaFAFxFJCAW6iEhCKNBFRBJCgS4ikhD/HywMrFeHx746AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# history\n",
        "print(len(history))\n",
        "\n",
        "# predictを20要素ずつ平均を取る"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Pkm_8hgY0id",
        "outputId": "284c035e-84e6-4bbd-f46f-3afbd1bfeeef"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for i in range(len(predict)):\n",
        "#   print(predict[i])\n",
        "from statistics import mean\n",
        "\n",
        "result = []\n",
        "\n",
        "p = 0\n",
        "\n",
        "for i in range(50):\n",
        "  for j in range(20):\n",
        "    # print(i*20 + j)\n",
        "    # mean(predict[])\n",
        "    p += predict[i*20+j]\n",
        "  result.append(p / 20)\n",
        "  p = 0\n",
        "\n",
        "\n",
        "# print(result)\n",
        "\n",
        "# plt.plot(result)\n",
        "\n",
        "betas=np.linspace(0.3, 0.6, 50).tolist()\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.errorbar(betas, result, capsize=4, marker='.', linestyle=\"\")\n",
        "# mpl.rcParams['lines.linestyle'] = ''\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "OquT3WC7aEZm",
        "outputId": "ae7e24f8-b077-4a11-f26b-6fb4f757e41a"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "# from statistics import mean\n",
        "\n",
        "# # print(history)\n",
        "# # plt.plot(history)\n",
        "\n",
        "# print(history)\n",
        "\n",
        "# predict = []\n",
        "\n",
        "\n",
        "\n",
        "# # for i in range():\n",
        "\n",
        "\n",
        "# for i in range(len(history)):\n",
        "#   predict.append(history[i][1])\n",
        "\n",
        "# # print(predict)\n",
        "\n",
        "# # print(mean(predict[0:9]))\n",
        "\n",
        "# result = []\n",
        "\n",
        "# for i in range(50):\n",
        "#   result.append(mean(predict[10*i:10*i+9]))\n",
        "\n",
        "\n",
        "\n",
        "# # print(result)\n",
        "\n",
        "\n",
        "\n",
        "# betas=np.linspace(0.4, 0.5, 50).tolist()\n",
        "\n",
        "\n",
        "# fig, ax = plt.subplots()\n",
        "# ax.errorbar(betas, result, capsize=4, marker='.')\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "nygoSU91YRx8"
      },
      "execution_count": 42,
      "outputs": []
    }
  ]
}