5.Cifar10 l2 regularization with dropout_notebook.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python [conda env:tensorflow_p36]",
      "language": "python",
      "name": "conda-env-tensorflow_p36-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    },
    "colab": {
      "name": "5.+Cifar10_l2_dropout_notebook.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMv3EMSRZJfh",
        "colab_type": "code",
        "colab": {},
        "outputId": "55338417-5e47-4ba6-ce5d-b1711664f88b"
      },
      "source": [
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.datasets import cifar10\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.regularizers import l2\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  from ._conv import register_converters as _register_converters\n",
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZeqsaLtZJfp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Defining the parameters\n",
        "batch_size = 32\n",
        "num_classes = 10\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DBwp0cFZJfv",
        "colab_type": "code",
        "colab": {},
        "outputId": "f0caa505-a2c0-4995-cdc5-460cdcd80106"
      },
      "source": [
        "# Splitting the data between train and test\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (50000, 32, 32, 3)\n",
            "50000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eq0ZTbwZJf1",
        "colab_type": "code",
        "colab": {},
        "outputId": "704aa3ac-0e72-422c-b3f3-8262e373449b"
      },
      "source": [
        "# plotting some random 10 images\n",
        "\n",
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "\n",
        "fig = plt.figure(figsize=(8,3))\n",
        "for i in range(num_classes):\n",
        "    ax = fig.add_subplot(2, 5, 1 + i, xticks=[], yticks=[])\n",
        "    idx = np.where(y_train[:]==i)[0]\n",
        "    features_idx = x_train[idx,::]\n",
        "    img_num = np.random.randint(features_idx.shape[0])\n",
        "    im = (features_idx[img_num,::])\n",
        "    ax.set_title(class_names[i])\n",
        "    plt.imshow(im)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x216 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1zxePJtZJf7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert class vectors to binary class matrices.\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwQEPNxLZJgB",
        "colab_type": "code",
        "colab": {},
        "outputId": "1ff2fc89-2f33-4e32-ff1b-91c1dbd937f4"
      },
      "source": [
        "# Printing sample data\n",
        "print(y_train[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pwRoMddZJgG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), padding='same',\n",
        "                 input_shape=x_train.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512,kernel_regularizer=l2(0.01)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation('softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opoMMUPKZJgN",
        "colab_type": "code",
        "colab": {},
        "outputId": "2487487d-bfb7-423c-911c-cffd481613e4"
      },
      "source": [
        "# summary of the model\n",
        "print(model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 30, 30, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 15, 15, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 13, 13, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1180160   \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                5130      \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 1,251,626\n",
            "Trainable params: 1,251,242\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wW_5tsOzZJgS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# compile\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='sgd',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "\n",
        "# Normalizing the input image\n",
        "x_train /= 255\n",
        "x_test /= 255\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpuwTd7HZJgY",
        "colab_type": "code",
        "colab": {},
        "outputId": "5dfb731d-0537-4d48-a032-11ec0427312a"
      },
      "source": [
        "# Training the model\n",
        "history = model.fit(x_train, y_train,\n",
        "              batch_size=batch_size,\n",
        "              epochs=epochs,\n",
        "              validation_data=(x_test, y_test),\n",
        "              shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "50000/50000 [==============================] - 23s 452us/step - loss: 7.9335 - acc: 0.4032 - val_loss: 5.7975 - val_acc: 0.5304\n",
            "Epoch 2/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 4.6396 - acc: 0.5439 - val_loss: 3.5245 - val_acc: 0.6147\n",
            "Epoch 3/50\n",
            "50000/50000 [==============================] - 21s 419us/step - loss: 2.9245 - acc: 0.6172 - val_loss: 2.2809 - val_acc: 0.6780\n",
            "Epoch 4/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 1.9990 - acc: 0.6656 - val_loss: 1.6542 - val_acc: 0.6985\n",
            "Epoch 5/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 1.4911 - acc: 0.6982 - val_loss: 1.3814 - val_acc: 0.6927\n",
            "Epoch 6/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 1.2096 - acc: 0.7219 - val_loss: 1.1681 - val_acc: 0.7086\n",
            "Epoch 7/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 1.0576 - acc: 0.7362 - val_loss: 1.2807 - val_acc: 0.6512\n",
            "Epoch 8/50\n",
            "50000/50000 [==============================] - 21s 419us/step - loss: 0.9708 - acc: 0.7489 - val_loss: 0.9518 - val_acc: 0.7489\n",
            "Epoch 9/50\n",
            "50000/50000 [==============================] - 21s 420us/step - loss: 0.9210 - acc: 0.7571 - val_loss: 1.0113 - val_acc: 0.7272\n",
            "Epoch 10/50\n",
            "50000/50000 [==============================] - 21s 422us/step - loss: 0.8813 - acc: 0.7700 - val_loss: 0.8884 - val_acc: 0.7646\n",
            "Epoch 11/50\n",
            "50000/50000 [==============================] - 21s 423us/step - loss: 0.8604 - acc: 0.7761 - val_loss: 0.9186 - val_acc: 0.7579\n",
            "Epoch 12/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8475 - acc: 0.7828 - val_loss: 0.8757 - val_acc: 0.7792\n",
            "Epoch 13/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8348 - acc: 0.7909 - val_loss: 0.8337 - val_acc: 0.7915\n",
            "Epoch 14/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8281 - acc: 0.7960 - val_loss: 0.9048 - val_acc: 0.7759\n",
            "Epoch 15/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8227 - acc: 0.8017 - val_loss: 0.8591 - val_acc: 0.7940\n",
            "Epoch 16/50\n",
            "50000/50000 [==============================] - 21s 419us/step - loss: 0.8162 - acc: 0.8053 - val_loss: 0.8646 - val_acc: 0.7904\n",
            "Epoch 17/50\n",
            "50000/50000 [==============================] - 21s 427us/step - loss: 0.8117 - acc: 0.8098 - val_loss: 0.8469 - val_acc: 0.8034\n",
            "Epoch 18/50\n",
            "50000/50000 [==============================] - 21s 425us/step - loss: 0.8123 - acc: 0.8129 - val_loss: 1.0159 - val_acc: 0.7560\n",
            "Epoch 19/50\n",
            "50000/50000 [==============================] - 21s 427us/step - loss: 0.8170 - acc: 0.8165 - val_loss: 0.8696 - val_acc: 0.8016\n",
            "Epoch 20/50\n",
            "50000/50000 [==============================] - 21s 426us/step - loss: 0.8141 - acc: 0.8205 - val_loss: 0.9103 - val_acc: 0.7920\n",
            "Epoch 21/50\n",
            "50000/50000 [==============================] - 21s 426us/step - loss: 0.8119 - acc: 0.8222 - val_loss: 0.8688 - val_acc: 0.8099\n",
            "Epoch 22/50\n",
            "50000/50000 [==============================] - 21s 425us/step - loss: 0.8144 - acc: 0.8255 - val_loss: 0.8833 - val_acc: 0.8082\n",
            "Epoch 23/50\n",
            "50000/50000 [==============================] - 21s 426us/step - loss: 0.8163 - acc: 0.8260 - val_loss: 0.8870 - val_acc: 0.8119\n",
            "Epoch 24/50\n",
            "50000/50000 [==============================] - 21s 425us/step - loss: 0.8085 - acc: 0.8327 - val_loss: 0.8928 - val_acc: 0.8114\n",
            "Epoch 25/50\n",
            "50000/50000 [==============================] - 21s 420us/step - loss: 0.8077 - acc: 0.8353 - val_loss: 0.9017 - val_acc: 0.8082\n",
            "Epoch 26/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8052 - acc: 0.8365 - val_loss: 0.8822 - val_acc: 0.8197\n",
            "Epoch 27/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8058 - acc: 0.8378 - val_loss: 0.8871 - val_acc: 0.8154\n",
            "Epoch 28/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8081 - acc: 0.8388 - val_loss: 0.8879 - val_acc: 0.8147\n",
            "Epoch 29/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8056 - acc: 0.8408 - val_loss: 0.9298 - val_acc: 0.8042\n",
            "Epoch 30/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8054 - acc: 0.8441 - val_loss: 0.8848 - val_acc: 0.8200\n",
            "Epoch 31/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8094 - acc: 0.8441 - val_loss: 0.9194 - val_acc: 0.8095\n",
            "Epoch 32/50\n",
            "50000/50000 [==============================] - 21s 417us/step - loss: 0.8080 - acc: 0.8461 - val_loss: 0.9200 - val_acc: 0.8158\n",
            "Epoch 33/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7999 - acc: 0.8495 - val_loss: 0.9510 - val_acc: 0.8066\n",
            "Epoch 34/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8026 - acc: 0.8489 - val_loss: 0.9605 - val_acc: 0.8021\n",
            "Epoch 35/50\n",
            "50000/50000 [==============================] - 21s 419us/step - loss: 0.7990 - acc: 0.8508 - val_loss: 0.8990 - val_acc: 0.8242\n",
            "Epoch 36/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.8005 - acc: 0.8512 - val_loss: 0.9263 - val_acc: 0.8151\n",
            "Epoch 37/50\n",
            "50000/50000 [==============================] - 21s 421us/step - loss: 0.7994 - acc: 0.8525 - val_loss: 0.9044 - val_acc: 0.8236\n",
            "Epoch 38/50\n",
            "50000/50000 [==============================] - 21s 419us/step - loss: 0.8011 - acc: 0.8528 - val_loss: 0.9597 - val_acc: 0.8091\n",
            "Epoch 39/50\n",
            "50000/50000 [==============================] - 21s 417us/step - loss: 0.7994 - acc: 0.8561 - val_loss: 0.9412 - val_acc: 0.8125\n",
            "Epoch 40/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7958 - acc: 0.8562 - val_loss: 0.9226 - val_acc: 0.8229\n",
            "Epoch 41/50\n",
            "50000/50000 [==============================] - 21s 417us/step - loss: 0.7951 - acc: 0.8563 - val_loss: 0.9507 - val_acc: 0.8151\n",
            "Epoch 42/50\n",
            "50000/50000 [==============================] - 21s 417us/step - loss: 0.7992 - acc: 0.8572 - val_loss: 0.9132 - val_acc: 0.8288\n",
            "Epoch 43/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7976 - acc: 0.8579 - val_loss: 0.9291 - val_acc: 0.8247\n",
            "Epoch 44/50\n",
            "50000/50000 [==============================] - 21s 417us/step - loss: 0.7897 - acc: 0.8618 - val_loss: 0.9307 - val_acc: 0.8209\n",
            "Epoch 45/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7893 - acc: 0.8611 - val_loss: 0.9265 - val_acc: 0.8266\n",
            "Epoch 46/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7889 - acc: 0.8618 - val_loss: 0.9316 - val_acc: 0.8240\n",
            "Epoch 47/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7930 - acc: 0.8621 - val_loss: 0.9620 - val_acc: 0.8156\n",
            "Epoch 48/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7902 - acc: 0.8628 - val_loss: 0.9289 - val_acc: 0.8255\n",
            "Epoch 49/50\n",
            "50000/50000 [==============================] - 21s 418us/step - loss: 0.7937 - acc: 0.8621 - val_loss: 0.9521 - val_acc: 0.8232\n",
            "Epoch 50/50\n",
            "50000/50000 [==============================] - 21s 421us/step - loss: 0.7943 - acc: 0.8622 - val_loss: 0.9165 - val_acc: 0.8311\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FphukCSZJgd",
        "colab_type": "code",
        "colab": {},
        "outputId": "e8efbcb1-fbfc-40ab-83f0-f0d5076fbfc8"
      },
      "source": [
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VIy1gZqvMbLWZrW5vbx9faXWzC4ZXCCdfHVArX0QEiD/wL3D3c4BLgI+a2VuHL+DuN7n7cndf3tzcPL7S6lo0vIKIyBHEGvjuvi2atgH3ACviLI/a2SMMr6DAFxGBGAPfzGrMrC4/D7wLWBdXeUBo4ffuh4GD1FerhS8iUijOo3RagHvMLF/Ot939v2IsL4yYCdC1i/qqJkBXvRIRyYst8N39VeCNca1/RAXDK8xoCOGvFr6ISFA6h2XCIcMrTNePtiIihyixwB/q0kknE9ROS2k8HRGRSGkFflUDJFKHHKmjFr6ISFBagZ9IRFe+GjoWf7+ueiUiApRa4EM0vEI427ahpoLdXQp8EREoxcCvmzPYwp83o4rWfQeLXCERkePDqALfzK4zs+kW3Gxma8zsXXFX7pjUDV3MfGFjNbu7+jjYny1ypUREim+0Lfz/4e4HCGfLzgSuBb4QW63Go3Y2HNwLmX7mz6wCYOu+niJXSkSk+EYb+BZNLwX+3d2fL3js+JK/EErXLhY2VAOwda8CX0RktIH/tJndTwj8H0dj5ByfA83XDZ1tuyAK/C0KfBGRUQ+t8GFgGfCqu/eYWQPwofiqNQ610dm2XTtpnL+c6ookW/fqh1sRkdG28M8DXnT3DjO7Bvg0sD++ao1DQQvfzFgws1otfBERRh/4NwI9ZvZG4OPAK8C/xVar8ahpBksMXvlqQUM1rfrRVkRk1IGfcXcHrgC+5u5fB+riq9Y4JJJQM2vw5KsFDVVs2dtDqL6ISPkabeB3mtkNhMMxf2hmCSAdX7XGqW5oeIWFDdX09GfZ260zbkWkvI028N8P9BGOx98JzAf+NrZajVfdnKEW/kwdqSMiAqMM/CjkbwPqzezdQK+7H599+BANoBYCf2FjdCy+hlgQkTI32qEVrgKeBH4buAp4wszeF2fFxqVuNnTvhmxmsIWvk69EpNyN9jj8/wO82d3bAMysGXgQuDOuio1LbQvg0N1G1fS5NNVOU+CLSNkbbR9+Ih/2kT2jfa2ZJc3sV2b2gzHX7ljlr3yVH0QtOlJHRKScjbaF/19m9mPg9uj++4H7Rvna64D1wPQx1u3YDV7bNhyps6ChmjVb9k1a8SIix6PR/mh7PXATcFZ0u8ndP/l6rzOz+cBlwDfGU8kxq80PoBb9cNtQzfaOXjLZ43P4HxGRyTDaFj7ufhdw1xjX/2XgExzlJC0zWwWsAli4cOEYV38EtbMAG2rhz6wmm3N27O8dHFBNRKTcHLWFb2adZnZghFunmR14nde+G2hz96ePtpy73+Tuy919eXNz8zFswgiSaahpGuzD16iZIiKv08J39/EMn3A+cLmZXQpUAtPN7FZ3v2Yc6xy92tnQle/DDxdC2bK3h/MnpXARkeNPbNe0dfcb3H2+uy8CPgD8dNLCHsKx+NHJV3Pqq0glTIdmikhZK72LmOfVDZ1tm0wY82bq0EwRKW+j/tF2PNz9YeDhyShrUO1s6G6DXBYSSRY2VGt4BREpayXcwp8NngtDLADzZ1arS0dEylppBz4UnG1bzd7ufrr6MkWslIhI8ZRu4A+efHXokTpq5YtIuSrdwB8cXmHobFtQ4ItI+SrdwK+NAr9r6Gxb0MlXIlK+SjfwU9OgqmGwD39GdZq6aSladaSOiJSp0g18iE6+Ci18M2N+Q7Va+CJStko78Ovnw75Ng3cXNlSpD19EylZpB/6sJbD7Jcj0A6Eff+u+Hty9yBUTEZl8pR34s8+E3ADs2QiEC5r3DuRo7+orcsVERCZfaQd+yxlhuut5AF3QXETKWmkHfuNJkKyAnc8BQ+Pib92rI3VEpPyUduAn09B86mALf/7MoXHxRUTKTWkHPkDL0sHAr0wnaZk+TV06IlKWyiPwu3YOjpq5YKaOxReR8lQGgX/oD7cLG6p1tq2IlKUyCPylYbprHQDzG6rZvv8g/ZlcESslIjL5Sj/wa5uhZtYhLXx32N6hVr6IlJfSD3wI3TpRC3+BjtQRkTIVW+CbWaWZPWlmz5jZ82b2+bjKel2zl0LbBshmWNgYHYu/T4EvIuUlzhZ+H3CRu78RWAasNLNzYyzvyFqWQrYP9r5CS10lFcmEWvgiUnZiC3wPuqK76ehWnFHL8kfq7HyORMKYP7OKVp1tKyJlJtY+fDNLmtlaoA14wN2fGGGZVWa22sxWt7e3x1ORplMgkRo641bj4otIGYo18N096+7LgPnACjNbOsIyN7n7cndf3tzcHE9FUtNC6A8eqVPF5j3dGiZZRMrKpByl4+4dwEPAyskob0QFQyycMbeeA70ZXtvdXbTqiIhMtjiP0mk2sxnRfBXwTmBDXOW9rpYz4EArHNzHisUNADz52t6iVUdEZLLF2cKfAzxkZs8CTxH68H8QY3lHN3jG7Quc2FRDU22FAl9EykoqrhW7+7PA2XGtf8wGx9RZhy06nxWLG3hCgS8iZaQ8zrQFqJsNVQ2DZ9yuWNTAto6DtOoELBEpE+UT+GbhjNvoh9sVixsBeGqTWvkiUh7KJ/Ah9OO3rYdcltNm1zG9MqV+fBEpG2UW+GfAQA/s20QiYaEf/1UFvoiUh/ILfBi8qPmKxQ28urubts7eIlZKRGRylFfgN58Glji8H/+1fcWslYjIpCivwE9XQePJBWfcTqe6IsmTr+0pcsVEROJXXoEPh1wMJZ1M8KYTZup4fBEpC+UZ+B2bofcAEI7Hf3FXJx09/UWumIhIvMow8KMhFtpeAMIPt+6wepP68UWktJVh4A8NsQDwxgUzqEgmeFInYIlIiSu/wK+fD5X1gz/cVqaTLFswQ/34IlLyyi/wzQ4ZGx/gLSc2sG7bfrr6MkWsmIhIvMov8CEE/s51MBBOuFqxuIFszlmzWf34IlK6yjPwT10JA92w8X4Azlk4k2TCNK6OiJS08gz8xW+D2hZ47g4AaqalWDqvXoEvIiWtPAM/kYSl74OXfgwHQzfOWxY3sHZrB70D2SJXTkQkHuUZ+ABnXQXZfnjhe0A4Aas/m+OZrR1FrpiISDzivIj5AjN7yMxeMLPnzey6uMo6JnPeCE2nwLOhW+fNixow04XNRaR0xdnCzwAfd/clwLnAR81sSYzljY1ZaOVv/jl0bKW+Os2pLXU6AUtESlZsge/uO9x9TTTfCawH5sVV3jE587fD9LnvAqEf/+nN+xjI5opYKRGReExKH76ZLQLOBp4Y4blVZrbazFa3t7dPRnWGzFwEC86FZ78D7pz3hkZ6+rP8/OXdk1sPEZFJEHvgm1ktcBfwMXc/MPx5d7/J3Ze7+/Lm5ua4q3O4s66C9g2w8zkuOq2F5rpp3PLzTZNfDxGRmMUa+GaWJoT9be5+d5xlHbMzfgsSaXjuDipSCf7buSfwyEvtbNzVWeyaiYhMqDiP0jHgZmC9u38prnLGrboBTn4XPHcn5LJcfe4JTEsluOXnrxW7ZiIiEyrOFv75wLXARWa2NrpdGmN5x+6s34bOHbDpURpqKnjvOfO5e8029nbroigiUjriPErnMXc3dz/L3ZdFt/viKm9cTlkJ06YPHpP/4QsW0ZfJcdsvNxe5YiIiE6d8z7QtlK6CJZfDC/fCwEFOmlXH205p5t9+uZm+jIZaEJHSoMDPO/Mq6O+EF38EwIcvWEx7Zx/ff2ZHkSsmIjIxFPh5iy6AurmD3Tq/fnITp7TUcvNjr+HuRa6ciMj4KfDzEkk4833w8gPQsRUz48MXLGb9jgP84tU9xa6diMi4KfALLf8QpCrhO9dAfw9XLJtHY00FtzymQzRFZOpT4BdqOBGu/AbseAbu/V9UphJcfe4J/GRDG6+2dxW7diIi46LAH+7US+Diz8K6u+DR/8e1555AOpHgXzXcgohMcQr8kVzwx2EkzZ/+Bc3bHuTyZXO58+lWOnp0IpaITF0K/JGYweVfhbnnwN2r+J+n93JwIMtf37deR+yIyJSlwD+SdBV84DaoqOXEB3+fT/x6E3esbuW2J7YUu2YiIsdEgX800+fCB74NnTv5w7Y/5x2nzOTz33+e1boqlohMQQr81zP/TXDF17DNj3Fj5ddYVJ/iD29bw64DvcWumYjImCjwR+Osq2DlF0m/9EP+s/FrZPu6+cNbn9Y4OyIypSjwR+vcP4DLv0bN1kd4cNZXeGnLdv7s3heOvPzmx2Hrk5NXPxGR16HAH4tzroX33UzD3rU80PglfvTk83x7+I+4rz0K/3oZ/Osl8K3LYc8rxamriMgwCvyxWnolvP9WZve+wvfr/oav3vsYv3hlD2x6LAT9t94Ne16Gd/wZpCrgnj+AnLp+RKT4FPjH4tRLsKvvYD5t3FHxF9i33g3fvAzfsxFWfhGuWxtO3rr076D1SXj8K8WusYiIAv+YnXghdu1/Mr+iiyUVO/n8wLVckfw6Lyz8YDiGH8LZuqdfDg/9Nex6vqjVFRFR4I/HwrdgH3uW6Z9cz/lXf4bt3cYVX3+Mr/10I5lsLpyx++6/h8p6uPsjkNHQDCJSPLEFvpndYmZtZrYurjKOC1UzIV3JO5a08MAfv5WVS+fwd/e/xJU3Ps76HQegpgl+8x9g13Pwsy8Wu7YiUsbibOF/E1gZ4/qPOzNrKvjq75zN1z94Dlv29nDJPzzKlTc+zh2dZzFw5gfgsS9B6+piV7N4+ruLXQORshZb4Lv7I0BZjkFw2Vlz+MnHL+RPLz2Njp5+PnHXs5z/zG+wL9VE73d/H+/vCQtmB2DLE/DwF+GWS+D/ngj3fwYOdhR3AyZaXxfcvQr+Zn7YvoGDxa6RSFmyOEd/NLNFwA/cfelRllkFrAJYuHDhmzZv3hxbfYrB3VmzZR/feWore579MTcn/orHk8upr67glJ5nSGe7cQyb80aYPg9evA+qZsBbr4c3/x6kphV7E8Zn1/Nwx+/C3ldg8dvg1Yeg4Q1hNNJF5xe7dqWrrwv2vQYdW6CvM3y7GuiB/p4wHTgITSfDGy4KF/4xm7iye/aGEw+3PR2uIFfTCNVNoXuzpjnMV82ExOu0N3M56G4L29DfBY0nQ/38ia3raLlDVxvs2xTdXoPOHeE62I1vgMaTwnRa3aGvy2agux06t0PnzvD3aD41fNYnaDvM7Gl3Xz6qZYsd+IWWL1/uq1eXbpdHV1+G7bf/b07Z9G22MJtHMmfwWO5Mnk2fxYkL5nP2whm8pWo752z8MtVbHoYZC+Hiz8EZ7x36cORy4Y227zXYtxkqamDOWTBz8fjeQLlsuK7vRHGHX90K910fPgTvuxkWvxVefRju/SPo2Bx2aO/4s8M/JPn67NsUfvCuaZq4ehVTLgt7X4Odz8LO56B9A9S2wNxlMGcZzFoSzt0YrvcA7NkIu18OATj4mfWh+Uxv+HvtfTWU0d125HokKyA5Dfo7w/0ZJ4Tgf8NF4X9UWR9Ce/8W6NgK+7eGaX9XqG/d7EOnqWmw9YlwLsqmn0NbdERaIgW5zMh1sARUNYT/bXXT0E4hlwkB37EllJsddqBDRV0IzFmnQfNpYWfV3wMH9xXc9oZvybkB8Fx0i/5WngtlJ1Ohfon00DwWyssOhNdmB8L9vs7wWRso7JI0qG6AnmHXu65tCXXK9MKBHdH/K3f49qdrwg636RRoPiVMT7/8mD7DCvzjWS4H3e147Sw27elhzeZ9/GrrPtZs7mDDzgPkon/HRel1fLriPzgx+yq7604jV9NC3cFWKrtasWzf4eudNh1mnwmzzwo7gNlnhQ9GMn3kunRsgfU/gA0/gC2/hBkLYO7ZQ7c5bwwf/rHq74YffhyeuT0EyHu/AXUthz7/07+EX94YWjqX/i1UTg/fBnY+F6Zt6yETdf3Uzo62bSm0LA3z1Y3hQ927H3r3Dc33d4XQyGWjaWbofqYPsn3haKlsdMv0hQ9nf3do9eZbvwMHw+G1Nc0hlGpnDc2na6JA6A8tuGx/dD/DUAgXTDO9YXt2vTAUGolU+KbTuRP69ofHkhXQckYIfzPYvTHcunaO7u8+fV4Im5mLwrRhcQjzyvrQMEhXh1syFeq291V45afh9toj4W9nydAKHRj2e0u6BqbVQvdu8COcSJiuhgUrYNEFcMIFMO+cEK49e0Mrt2d3eH3PnjAdfGxPNG0P5c9YOHSbeULYhlQl7H4p7CTb1kOMG/+TAAALBElEQVT7iyPv1KbVh2/IlfVhR2QJwMLUEuHv6rnwnsgHey4b5vFoZ5iOptF8uibUY+ai0LBqWAz1CyBdGd4ne18LJ1vueTl8k937Wnjv1M2JbrPDyLu1LeH91f5i9L+Npvu3hvf4n7w4uv/zMAr8Kap3IMvGXV2s33mAF3d28tKO/SzacR9XZ+4hS4ItPiu6tdCWnE13zQLmTOtjiW3ipOyrLBp4hTl9r1CRCyN5ZhNpuqafzMGG08k0h6CsqptJ9aYHmLbxPhK7ng0Fz1oCJ74dDmyD7WvCjiCv4Q3hg55v7WT7hwIzVRmCt7ohmka35+8JH863fRLe9okjf3PY+hR876PhjZ9X3RhCvWUpzDodejtg5zrYtS582I/UYjySRNR6s2RoPSenRdOC+XR1+ICmq4bmU1Xhw5kPpu62MN8/wrWNLRnWl0hFLTSDwYaahdBoOiXaaUW35tNCIOWDd8da2L42mj4T1tN0SmgFNp40NF83eyjEYKi8RGrkbwejlR2A1qfglYdCi3bGwtAAqF8Q5qtmhrJy2RDYnTuha1eY9neHcJ+zbHx1GKueveFbzbS6UL/K+qM3cI5XfV3h79h00jG9/LgIfDO7HbgQaAJ2AZ9z95uP9ppyD/wj2dvdz879vbR39dHeGW67u/po6+xj/8EBOnsH6OzN0Nk7QHdvPy0DrSyxzSxJbI6mm2i2A4es8+ncyfw4u5yf+Ap2pOYyLZUgnQy3pmQXZ/irnOYvc3LuFSrIkEukySUq8EQaT6YhUUEFA9RkO6jJ7qcm00F1Zj+VmQN0VzTy4Gl/QevMtwAMXiUs55DJ5hjIOQOZHJmckx3oZcm+h+lJTae9+iR6KppIJBKkEkYyYSQSRsIgaUbSMzT3bmJW90tUZLvoSU7nYLKO3mQtPYlaepK19CWqGfAkAyTJeYIckHPHHSpSCSrTSSpTSSrT0Xw6QcIMi75KGyHXzCBhRsKielioRzrXSyrXRy6RIpeoIGcpPDr2wYFszsm5D05z7uRyoUcuv66k5bcrrDOv8Nt8NgfZXI6BrJPJ5chknUy0TiO8LmFhxxLWS7RdSaalE1Slo/lUAgdyOSfrTiY7VL98fVJJI5UwUokEyYIK5f9uYTuG6hj+RqEOhoX9QLRM/jUe3Q9/x/zyQ/VOJPJlRuVGdbCCdebLyRvI5sjmwjYM5KL5nGMwuG4zG/zfVaQSVKYSpJKlfbrRcRH4x0KBPzEy2RzdfVk6+8KOoKsvQ9++7STanofu3WypX87+VBN9mRz9mRx9mSx9mRwD2Rz9GY+m0f1sfpmhZfuzOfoGwvP5D18+kJKEr/tZjvx7QEUyQSpp0Q4mhE4+WDK5KCxzQwGX8xCkR2LRDqFw55AouJ+IQqMvk6N3IEvmKOuS0pNK2ODOfVoqSToZ7dwLdiY2bCZ/38zw6H2ZKdj55nc+hTuvRLQzTybskPdyJju0c3IHJ7z/8tHrQFNtBU/86TuOafvGEvipYypBjmupZIL66gT11QVfbxc1AKFn7ddiKtcL3uQw1GItbLElE3bIB22s6w8t5/ChyX+4xrq+TDZHbxT+vQPZoQ9etN4wjVqtUcs4l+OQlruZFYRC2EYILfmRWvH5ndYhrf/c4R9+CGXnty2dNFLJ8I0nlQzry9cz56HlDpDJ+eD29A4MbVtfJjfY4h0eSo4P7qjD/y18o4CwvDH0zeTQ/8PQ3ykXtebz34LMDm39k/87FnxbyEbBN9hKz/pgIIblhv4HhX+b0EgwkonQUAjfSIjKGPqW4dH/Lezgc/Rmhv4ufQNZstH6C//e+fdX4X0KlklF5eX/D+lkYjDY8++RbC5/g2QCkgXfVAu/sQKHvHcAaqZNThQr8GXCmIUPZHoCD/YZvv5UcvyHsqWSCWqTCWon6UMmcrwo7c4tEREZpMAXESkTCnwRkTKhwBcRKRMKfBGRMqHAFxEpEwp8EZEyocAXESkTx9XQCmbWDhzrgPhNwO4JrM5Uoe0uL9ru8jKa7T7B3ZtHs7LjKvDHw8xWj3Y8iVKi7S4v2u7yMtHbrS4dEZEyocAXESkTpRT4NxW7AkWi7S4v2u7yMqHbXTJ9+CIicnSl1MIXEZGjUOCLiJSJKR/4ZrbSzF40s5fN7FPFrk+czOwWM2szs3UFjzWY2QNmtjGazixmHSeamS0ws4fM7AUze97MroseL+ntBjCzSjN70syeibb989Hji83sieg9/x0zm8Qrh08OM0ua2a/M7AfR/ZLfZgAz22Rmz5nZWjNbHT02Ye/1KR34ZpYEvg5cAiwBfsfMlhS3VrH6JrBy2GOfAn7i7icDP4nul5IM8HF3XwKcC3w0+h+X+nYD9AEXufsbgWXASjM7F/gi8PfufhKwD/hwEesYl+uA9QX3y2Gb897u7ssKjr+fsPf6lA58YAXwsru/6u79wH8AVxS5TrFx90eAvcMevgL4VjT/LeA9k1qpmLn7DndfE813EkJgHiW+3QAedEV309HNgYuAO6PHS27bzWw+cBnwjei+UeLb/Dom7L0+1QN/HrC14H5r9Fg5aXH3HdH8TqClmJWJk5ktAs4GnqBMtjvq2lgLtAEPAK8AHe6eiRYpxff8l4FPALnofiOlv815DtxvZk+b2arosQl7r+sqziXE3d3MSvI4WzOrBe4CPubuB0KjLyjl7Xb3LLDMzGYA9wCnFblKsTKzdwNt7v60mV1Y7PoUwQXuvs3MZgEPmNmGwifH+16f6i38bcCCgvvzo8fKyS4zmwMQTduKXJ8JZ2ZpQtjf5u53Rw+X/HYXcvcO4CHgPGCGmeUba6X2nj8fuNzMNhG6aC8C/oHS3uZB7r4tmrYRdvArmMD3+lQP/KeAk6Nf8CuADwD3FrlOk+1e4Hej+d8FvlfEuky4qP/2ZmC9u3+p4KmS3m4AM2uOWvaYWRXwTsJvGA8B74sWK6ltd/cb3H2+uy8ifJ5/6u5XU8LbnGdmNWZWl58H3gWsYwLf61P+TFszu5TQ55cEbnH3vypylWJjZrcDFxKGTN0FfA74T+AOYCFhaOmr3H34D7tTlpldADwKPMdQn+6fEvrxS3a7AczsLMKPdElC4+wOd/9zMzuR0PptAH4FXOPufcWraTyiLp0/cfd3l8M2R9t4T3Q3BXzb3f/KzBqZoPf6lA98EREZnanepSMiIqOkwBcRKRMKfBGRMqHAFxEpEwp8EZEyocAXmQBmdmF+ZEeR45UCX0SkTCjwpayY2TXRGPNrzeyfo8HJuszs76Mx539iZs3RssvM7Jdm9qyZ3ZMfh9zMTjKzB6Nx6teY2Rui1dea2Z1mtsHMbrPCAX9EjgMKfCkbZnY68H7gfHdfBmSBq4EaYLW7nwH8jHAGM8C/AZ9097MIZ/rmH78N+Ho0Tv2vAfmRDM8GPka4NsOJhHFhRI4bGi1TysnFwJuAp6LGdxVhIKoc8J1omVuBu82sHpjh7j+LHv8W8N1orJN57n4PgLv3AkTre9LdW6P7a4FFwGPxb5bI6CjwpZwY8C13v+GQB80+M2y5Yx1vpHBslyz6fMlxRl06Uk5+ArwvGms8f63QEwifg/xIjB8EHnP3/cA+M/v16PFrgZ9FV91qNbP3ROuYZmbVk7oVIsdILRApG+7+gpl9mnBFoQQwAHwU6AZWRM+1Efr5IQxF+09RoL8KfCh6/Frgn83sz6N1/PYkbobIMdNomVL2zKzL3WuLXQ+RuKlLR0SkTKiFLyJSJtTCFxEpEwp8EZEyocAXESkTCnwRkTKhwBcRKRP/HzPvVzVugZ/1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oQWlZGcZJgn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}