Image_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Image_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tnleang/machineLearning/blob/master/Image_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwuAlDbww8kb",
        "colab_type": "text"
      },
      "source": [
        "## Into to TensorFlow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8twguwHmCy5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "# import tensorflow\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrBV_dJEmd6b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load the MNIST data\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "# convert data into floating-point\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train/ 225.0, x_test / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TouM6NNsLSI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# build a model\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "             loss='sparse_categorical_crossentropy',\n",
        "             metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wo9G-dGZvqCN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.fit(x_train, y_train, epochs=5)\n",
        "\n",
        "model.evaluate(x_test,  y_test, verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuxgEYzqxHXu",
        "colab_type": "text"
      },
      "source": [
        "## Basic Classification\n",
        "\n",
        "Classify images of clothing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejBu7zX0ysod",
        "colab_type": "text"
      },
      "source": [
        "install tensorflow 2.0.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0B4WiFEyQEu",
        "colab_type": "code",
        "outputId": "1b7ce494-8621-4f0e-b7a1-c354620e5024",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "!pip install tensorflow==2.0.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow==2.0.0 in /usr/local/lib/python3.6/dist-packages (2.0.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.1.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.0.8)\n",
            "Requirement already satisfied: tensorflow-estimator<2.1.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (2.0.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.16.5)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.12.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.7.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.8.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.15.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.1.7)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.33.6)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.8.0)\n",
            "Requirement already satisfied: tensorboard<2.1.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (2.0.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.11.2)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.2.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==2.0.0) (41.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.16.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YombA5ZoxSaz",
        "colab_type": "code",
        "outputId": "cbec0b81-6869-4c92-c81d-3330c69a3303",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# some import are done in the first code cell\n",
        "\n",
        "# import keras\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.15.0-rc3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgtAqBpJ1_xZ",
        "colab_type": "code",
        "outputId": "dfd5e751-87b8-4930-da85-9a7540fc3f7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        }
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Bqy7-JR2gYF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeMlOltZ2v5I",
        "colab_type": "code",
        "outputId": "1348da53-b1a0-4fd7-ac7f-7b01d0f52cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TkQYygiuA11rmkZi7P2tmc0e+KyLSEHI2JlbNvZM3mNmrZrbMzKo79haRxpKjI7HhJrG7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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtkUbeKi3Zuj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# converting to floating-point\n",
        "train_images, test_images = train_images / 225.0, test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Apw1zM73oRF",
        "colab_type": "code",
        "outputId": "1095391a-6cbb-4ba0-ad87-462a4a858bf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ddwdQXmGF1l7M35vnxPp3cPXo2LWP+Y+khWfWlDTfEg7Ft8Ts4LVxTg0F/q72mtTV51qf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\nnw2p5fuF/V4LFy5MHV+kk3U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QaWkn7D3Fru0sSAMkhBBCiNyhByAhhBBC5A49AAkhhBAi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jen1XMcGF97QBpH5OeBelc172yWL18ezw+fQyCNneDzNnfu3KQdz5kzZsyIti/ZwdXyfbVuTi3n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\n/H/4B+F2RTSX5QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NooHczQw3424",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Training the model\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "             loss='sparse_categorical_crossentropy',\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TotdDH9i4c6o",
        "colab_type": "code",
        "outputId": "c0ff4b50-47c7-4301-f2ad-f1bc739c0218",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 1.0277 - acc: 0.6785\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.6124 - acc: 0.7794\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.5424 - acc: 0.8069\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.5034 - acc: 0.8233\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 0.4788 - acc: 0.8318\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.4604 - acc: 0.8379\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 4s 71us/sample - loss: 0.4470 - acc: 0.8427\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 4s 73us/sample - loss: 0.4360 - acc: 0.8464\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 4s 71us/sample - loss: 0.4268 - acc: 0.8494\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 4s 71us/sample - loss: 0.4180 - acc: 0.8536\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f595a781e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2gfzTSP4v-w",
        "colab_type": "code",
        "outputId": "708e93fa-b6f5-40c9-a7d4-651488c42de1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
        "\n",
        "print('Test accuracy: ', test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 - 0s - loss: 0.5045 - acc: 0.8190\n",
            "Test accuracy:  0.819\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PN8gG-qc5HGQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GXjHx5BBNxG",
        "colab_type": "code",
        "outputId": "011dfe5c-8ada-42d0-9eb6-dbfdf413b7ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.9627508e-06, 6.4510812e-07, 3.3091521e-05, 4.0214865e-05,\n",
              "       2.7790857e-05, 4.1670507e-01, 8.2446801e-05, 4.0030372e-01,\n",
              "       8.0895154e-03, 1.7471054e-01], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-U7aHq7u5PEn",
        "colab_type": "code",
        "outputId": "45dc5923-94e7-417b-f8e4-95f227176f5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.argmax(predictions[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Zu-N3g05Wri",
        "colab_type": "code",
        "outputId": "60349b64-da03-4392-92ac-7840d5a5caae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_labels[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYV2F0hj5qfT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'white'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array, true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10), color='white')\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6B1Dzxm5jQv",
        "colab_type": "code",
        "outputId": "cbe7f95e-a820-4cf2-a40d-1c71effca56f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "i = 1\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADFCAYAAABjLIjfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE7IkZcKELEmZMCFLUiZMyJKUif8DlGzqi62SW3gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RhGw3k7CJvk",
        "colab_type": "code",
        "outputId": "be6ade18-b79b-4b57-cf72-a950aa2b11b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(50,60):\n",
        "  plt.figure(figsize=(6,3))\n",
        "  plt.subplot(1,2,1)\n",
        "  plot_image(i, predictions[i], test_labels, test_images)\n",
        "  plt.subplot(1,2,2)\n",
        "  plot_value_array(i, predictions[i],  test_labels)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OHmqW1NFnK45L+ZwDddz00gMizGxofkgfN+kpk0bmXzebdGz+ebtJzRVea7hJXzfp2l7a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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mp8xLi730mJduz2IzvLQk+15ZdjyzvDTXS//gpW5eWprlTvHSd1o4jmffPG7pnV5a0eSc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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uIpZ21rGFmJ95kfiw+yHR5naeJA46O44I8Q8Sw0kHlYEsHR6OISaWP8XwI0RHs59YobOY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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vhOz9XoWEc4ukTkn/WsyA+32n+OcqSS2Rqzwi7+0X2g1WV9zr9yT9jqRPybkLiq9dWST+\n6yR9XM5dVYzvs0Vy/ytJiyV9Ws59VM49LOfKs+Aw4+88JvZMcY1zJX1V0meL+FRJj8i5NZK+IOlX\n3xiL9K3i/mskvXTM9ToUZs0AMjO+E7IkeX9Y3v+wWFf9hMJf+fv1zzQPK748k/rG0C2Svifve4ql\nkR9LmluMYWvxzw5Jj0m6dFBP5+ZJcgpLCB+Q9x+U9FY597Zj7rFfYQki5glJVxX/vljSD+T9JZLe\nM0S/gfqXODA8S+k3ov1qcc9afI/HNb4TsnOtxyS1t6v65sxA3QrrtJbvSrpSztUV67SXSVov5xrl\n3JuK8TRK+m1Ja47pu1hh+WCipAlF7IjC2vJALys+k5fC7PcXxb9PVVj3lqQPD2jzP5I+WIznYkm/\n9sZXwjp0k6SNx7kHEnjvT+gPJf3yuWctvsehjPdNvdMlfamogDiksOF1yzCu92+SHpVz79Oxm3re\nr5dzTyksARyRdL+8XyPnzpf0mMLD8OskfUPeP/VGP+dukLRS3rcXv39Bzq2W9JK8H/xkfe975Nwv\n5Nwceb+hiPavITuFdeiPFvHPSvpasfTx7wOucl8RX6ewzry26CeFJZefyPtDJ/wJARg147vs7WTk\n3O9Leoe8t9aYU/pPkDRR3h+Qc2+V9H1JrfK+T859UdIT8v6/Rm7AAEbK+F6yOBl5/5iGt6TQIGm5\nnHtRYT37Y/K+r/jaGpLxsC1U2AvYIKnKk4AeUNhQPXY5ayjnSvqBpHUKf9u5LbHfZEnPSXqx6Hdn\nxftOUKgQerJCn42SVkt6QdLK4zcd5ExJjyr8jW69pMsT+rQW9+n/tUdS/MDVYH+h8JmskfRNpe+/\n3Fb0WVvhXpUwQwbSTVBY579WYZP3p5I+pJAsh3KVpL2Svq5QI55qVvHrZwr7G6sk3ZBwTyepsbjn\nREnLFRLKTxLv+5eS5ks6Q9L1iX02Fn2qHrb4mkJ9/P2S6hUmFbsq9J+gsJ9ymYbeQzpH4bO4WGFz\n+2GF8tRlQ/S7RKF66VJJfZKekvRn6j8XMEKYIQPpLlX4A/h/Cn8ovyXpfYl9fyxp5wncc5tCMpbC\npvN6haQyFK+QjKWQkCcWsRTNCuWd96cP84RNVfhh9ZXi932qlowl6V0Km92pG/p1kqYU/2yQ1J7Q\n5yJJKyTtU9iv+pGk91cc55BIyEC6cyRtHvD7LUpLjiOlReFU6YrE9hMU/jrfIek/K/T7J0l/rbB5\nXUU4IBVm8amb6+cp1N5/VWGJ5H6FmX0Vf6iw9JBiq6TPKxy06j+w9XRCvzWSFkh6s0IS/12F5aQR\nRUIGxofTJX1bYe1yT2KfwwqloM0Ks/uUpZLrFRL4qhMY45WSjh6QOlozfzx1RZ8vK/yw6VG1tfl6\nSe+V9Ehi+2kKf6s5T+GAVKOkGxP6rZd0j0LyfkrhB13KgbJKSMhAuq0aPCtq1tFa8NE0USEZ/4uO\nnj6tYpfCxuDChLbvVEhwGxWWZH5L0kOJ9+n/LOwDUrYtxa/+2fujCgk61XUKSzqvJ7Z/t6RXFWbl\nBxU+z/gTHgf7ikLp6FWSuhT2E0YUCRlI91NJb1OYXdUr/FX5iVG+p1NIBOsVnmyY6iyF6gUprJde\nq1DFMJS/UfhB06Lw/f230maQjTp6qCp2QMrymsIyUGvx+3cpbZO034eUvlwhhaWK31BYdnDF/dYn\n9u1/9O1shfXjb1S4b5LxfjAEGEuHFI7nf09hffYBhRKoFN+UdLXCoxu3SPqMjm5kHc87Jf2xjpaT\nSdLfKlQGHM8sheqFCQoTr4dVrYStqpkKs2Kp/4BU+Kt9ikUKs/96hQ3TjyT2a1T4QfOn6cPUCoVZ\n+M8U/ns+r/Sj0N9WWEM+qLAkU3XzcUiUvQFAJliyAIBMkJABIBMkZADIBAkZADJBQgaATJCQASAT\nJGQAyMT/A6YbxGdI+NpIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qvbz/Aq4kEs03W+e+Enhx67V6iESe+TDRc7wYeIroSUP03j9I9K772wn8LNGT/RaRUO8B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\nMecmHrjAAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3vyub3b4o6Z9kyVeyGeVXJf2vpA/0OUenpAnZ7+sl7ch+f7Wkb2X//8eSGmSz2g5JY7Nf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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LCo+v3dDtdnfrH6XH1XPO4XiOuzKSAnlwDPlR4J+BXyeGFm4kxoe/B/waMcYM8B3gDmIo\n46u9/78medxTgP8mxnEPBP68V7+VCMnvEePM32X7YY8/6rV9jRhffkev7eeTc9xH9MA729QGx5AX\nEW8ug8vnPgl8m3hD+cE27S8hJh8fI8J92+fyLuDfkvNKqshIWva2OyYTww0TiVC8gPI64b1tARHo\nX9/N4ycSwxJdood8NvCLvf93X+/3VfmhkmowknrIu+Naohf6MPAlhi+MISYeJw7h+OOJ5/IYMXY9\nOFY9i5hQNIz3jPcSn5ieBPrZxXE9MXTW79DRIcRqnseB7xObj9oYD/wX8cnt+8R8Qz/GEBPZd/Zx\nzBLiU+CjxMqetqYRnzh/QAzf7TgXk3kjWz8RPwr8H/EpsY2PEa/JIuAm2s8pXdw75vt9nKsvo72H\nLPVjDDF0dRqxtPA7xCeRx1scezLxaewGYqVMWwf3fh4m5g8WErs6d3XODrE6Zx0x33A/ESgPtjzv\npcRQ336U51d2tKR3TL+bLT5HrBy6jlhfP5GYOG9rDLH66WfZ9QafOcRrcRTxifIW4CvEZPnOvAm4\nGXgLsImYaP8I8ca8x4z2HrLUj7cQ/wCfIv5R3szWYaFduY/Yet+vF9j6yW0t0YOc0+K4LhHGEIE8\ntldrYy5xS4Dr2l/mbptKvFl9pvfnTfQXxgDvIeZz2u62HCDmnAaI8N/ZHoFBRxJzNxuIpa3/wdb9\nCXuMgSy1N4fY0TnoOdqF454yj5j8/XbL9mOIj/PLiQ1LbY/7W+D36P/eKV3g34le/AUtj3kDsQb/\ns8QQyXVEz74fHyCGHtp4ntiNu5R4s1tDXPOuLCIm5mcQIX4GMZy0RxnI0uvDZGKe4xJivLSNLcTa\n+7lE777NUMn7iABfuBvXeBKxI/Z04mZYJ++8ORC91DcDVxNvNuvpb2x+HLHZ6ou7atgznfhU8wZi\ns9Qk4vYJu7IY+AsivL9GvNFt6eM6WzGQpfaeZ/te0dxebW8bS4TxjcTW+X6tJiYG29zx7+1EwC0h\nhmTeDfxTy/MMvhbLgX8h3gR25bnez2Dv/VYioNs6nRjSWday/anA00SvfDPxep7Y8tjPEJPnJxOT\n5D/s4zpbMZCl9r4D/BTRuxpHfFS+Yy+fs0MEwWJitUxbs4jVCxDjpaex/br1ksuIN5p5xPO7l3Y9\nyEnEpOPg7z9HuxUl/0sMA72x9+f30G6SdNDZtB+ugBiqeCsx7NDpnW/xTo/Y6oDef3+CGD/e8RYF\nQzZS7/Ym7Q2vEtvW7yLGZ68nlkC1cROxyWgm0SP8Y7ZOZO3M24k7+g0uJ4O4texXdnHcwcTqhTFE\nx+sW+lvC1q8DiV4xRK58gfho38ZFRO9/HDFh+qGWx00i3mh+o/1l8m2iF/4w8ff5CO23Qn+JGEPe\nTAzJ9Dv5uEsue5OkSjhkIUmVMJAlqRIGsiRVwkCWpEoYyJJUCQNZkiphIEtSJf4fMq7Ji6lI3JYA\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0kP9uH9BQvPYRwkjg94E7O7mSK4g9aAihAfgicA1me4tjvY7ZguQ1x/h3CGEpIfyCEP62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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v+AoRKA/VPO97iaG+RVTPrxxra3FM080WHyVWDt1OrK/vJSbO6+oiVj/9CBNv8FlOvBbn\nEJ8oPwl8jpgsH8+5wCeAVwHDxET7u4g35ikz23vIUhOvIv4D/hfxn/ITjA4LTeRBYut9UwOMfnLb\nS/Qgl9c4rk2EMUQgzynK6lhBXBLg9vrNPG6LiTerDxf3h2kWxgCXEPM5dXdbdhNzTt1E+I+3R2DE\n2cTczfeIpa3/xuj+hCljIEv1LSd2dI7YRr1wnCr9xOTvwzXrdxEf53cQG5bqHveXwO/R/NopbeBf\niF78lTWPeQmxBv8jxBDJ7UTPvom3EEMPdWwnduN+l3iz20O0eSKbiIn5U4gQ/yliOGlKGcjS/w8L\niXmOq4nx0joOE2vvVxC9+zpDJW8iAnz9cbTxR4kdsZcSF8NaO351IHqprwD+hnizeZ5mY/M9xGar\nT01UsXAy8anmJcRmqQXE5RMm8iRwKxHeXyDe6A43aGctBrJU33bG9opWFGXTbQ4RxncRW+eb2k1M\nDNa54t+FRMBtJYZkXg/8Xc3zjLwWO4B/JN4EJrKt+Bnpvd9DBHRdlxJDOoM16/848N9Er/wg8Xq+\ntuaxHyYmz9cSk+RPNWhnLQayVN8jwJlE76qH+Kj8mWk+Z4sIgieJ1TJ19RGrFyDGS9/A2HXrVa4l\n3mj6ief3r9TrQS4gJh1Hbv8E9VaUPEsMA51V3L+EepOkI95K/eEKiKGKVxPDDq3ifE+Oe8SoU4s/\nv48YPz72EgWTNlOv9iZNh0PEtvUvEuOz64glUHV8nNhktJToEd7A6ETWeC4krug3spwM4tKyn5vg\nuNOJ1QtdRMfrkzRbwtbUaUSvGCJX7iY+2tfxW0Tvv4eYMH1HzeMWEG80v16/mTxM9MIfI36f36D+\nVuhPE2PIB4khmaaTjxNy2ZskZcIhC0nKhIEsSZkwkCUpEwayJGXCQJakTBjIkpQJA1mSMvF/njgp\n66UdKAMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/IYLVZcRI9Erg94GvkI+CFzAw597FncAXiCuKwber+wYR3DcAjwKHJdveCvzfMO99KjGF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\nWZIKYUCWpEL8PyaXHyHiRwfCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}