Clothes_Classification_using_ANN_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Clothes Classification using ANN model",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPKOr5MNfOZV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqiE3G4DfQKX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "cf498774-3395-4e4d-bd69-c133a445e03e"
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": null,
      "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": "mCPRvBPfflD5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "4559d65e-4e51-486f-b435-05b0fca67dfd"
      },
      "source": [
        "img_index = 3\n",
        "img = train_images[img_index]\n",
        "print(\"Image Label: \" + str(train_labels[img_index]))\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image Label: 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc1059092e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zB_x9jNA2wBV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6060d4af-fe58-42f1-b326-169fe15c6537"
      },
      "source": [
        "print(train_images.shape)\n",
        "print(test_images.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXPOViWf2-W4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential([keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5yVRWjN3sMl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=tf.optimizers.Adam(), loss='sparse_categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZ8hnZ4R35tM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f5a521cb-07c3-4d35-9504-f0d8fcd16cd0"
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=5, batch_size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 2.8270 - accuracy: 0.6776\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7145 - accuracy: 0.7190\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6125 - accuracy: 0.7628\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5467 - accuracy: 0.8037\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5262 - accuracy: 0.8150\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc0fdbc54e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vU_PVFFv4uEc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2f807c8d-cb7d-4288-ac32-157a9e1fe591"
      },
      "source": [
        "model.evaluate(test_images, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 0.5321 - accuracy: 0.8199\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5320835113525391, 0.8198999762535095]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Me0d_Od5JYm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "63d65539-1845-4aa7-d638-aae3d19d05cd"
      },
      "source": [
        "predictions = model.predict(test_images[:5])\n",
        "#Print the predicted labels\n",
        "print(np.argmax(predictions, axis=1))\n",
        "#Print the actual labels\n",
        "print(test_labels[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9 2 1 1 6]\n",
            "[9 2 1 1 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFu_dzWN5d1p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1ccf3cad-cea5-4eb0-857c-af4974080e12"
      },
      "source": [
        "for i in range(0,5):\n",
        "  first_image = test_images[i]\n",
        "  first_image = np.array(first_image, dtype='float')\n",
        "  pixels = first_image.reshape((28, 28))\n",
        "  plt.imshow(pixels, cmap='gray')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAR/UlEQVR4nO3da2yVZbYH8P8SWqC0XCoICAaGigIxEU8IOURDNOYYh8TUSdQMH0ZOYk4ncYxDMh/GcD5ojCZ4cpzJfDhO7HgZPJnjOMmMkXgdpgHMROUaRISRi0GnpBcQgq1cS9f50FdTte9aZb/vu9+t6/9Lmu7u1Wfv1d3+uy/Pft5HVBVE9P13WdkNEFF1MOxEQTDsREEw7ERBMOxEQYyt5pWJCF/6r8Bll9n/kydOnJha6+vry7udS9LQ0JBau3jxojn23LlzebcTgqrKSOdnCruI3A7gNwDGAHhGVddluTwamRVmAFi2bFlqraOjI+92LsnChQtTa/39/ebYAwcO5N1OaBU/jBeRMQD+B8APASwGsEpEFufVGBHlK8tz9mUADqnqx6p6HsAfAbTm0xYR5S1L2GcD+OewrzuT875GRNpEZIeI7MhwXUSUUeEv0KlqO4B2gC/QEZUpyz37UQBXDft6TnIeEdWgLGHfDmCBiPxAROoB/BjAhnzaIqK8VfwwXlUHROQBAG9haOrtOVX9MLfOvkPGjx9v1tesWWPWV61aZdanTp1q1qdPn55aO336tDm2ubnZrGd19uzZ1NqZM2fMsd48/JYtW8z6M888k1p78803zbHfR5mes6vq6wBez6kXIioQ3y5LFATDThQEw04UBMNOFATDThQEw04UhFTz6LLf5bfLPvHEE6m1trY2c2xTU5NZ9+abvfqFCxdSaxMmTDDH1tXVmfUxY8aY9fPnz5t1a57fW6c/btw4s+79bFbv7777rjl2xYoVZr2Wpa1n5z07URAMO1EQDDtREAw7URAMO1EQDDtREJx6S3jTZ08//XRqrbu72xw7MDBQUU+jVV9fn1rzlol6vL+PwcFBs+5N7WW5bu92tX72OXPmmGPfeOMNs37HHXeY9TJx6o0oOIadKAiGnSgIhp0oCIadKAiGnSgIhp0oCM6zJ3p6esy6dbhobzdSbynnzJkzzbrn5MmTqTVv22NvrtrbQdY7jPZnn32WWvOWz3rvEfCWwIqMON0MwF+a29jYaNZbWlrM+vHjx816kTjPThQcw04UBMNOFATDThQEw04UBMNOFATDThREpl1cv08mT55s1q356qzz6E899ZRZb29vN+s7d+5MrXV1dZljvXXdfX19Zv3TTz8161dccUVqzZvrnjVrllnv7Ow069bvbNKkSeZY7zDV8+fPN+tlzrOnyRR2ETkCoA/ARQADqro0j6aIKH953LPfoqq192+MiL6Gz9mJgsgadgXwVxHZKSIjHsRNRNpEZIeI7Mh4XUSUQdaH8Tep6lERuQLARhH5h6q+PfwbVLUdQDtQ2wthiL7vMt2zq+rR5HMvgJcBLMujKSLKX8VhF5GJItL05WkAtwHYm1djRJSvLA/jZwB4OVkzPBbA/6nqm7l0VQJvbfTZs2dTa9a66dFYu3atWT916pRZt9aFNzQ0mGM3b95s1m+55Raz7tm3b19qbdGiReZYby78wQcfNOuPPfZYau3YsWPmWO+9EzfeeKNZ37Ztm1kvQ8VhV9WPAVyfYy9EVCBOvREFwbATBcGwEwXBsBMFwbATBRHmUNLWtsaAf8hl63DN3tTblClTzPqGDRvMemtrq1nP8jv0en/00UfN+ueff27WN27cmFprbm42x/b29pp173d28ODB1Jp1iGsAaGpqMusvvfSSWb/33nvNepF4KGmi4Bh2oiAYdqIgGHaiIBh2oiAYdqIgGHaiIMIcSvrKK6/MNH5wcDC15h122DN79uxM4y133313pvEvvPCCWbeW/gL28tv333/fHOsdStrbKrtICxYsKO26K8V7dqIgGHaiIBh2oiAYdqIgGHaiIBh2oiAYdqIgwsyzT5s2rbDLrqurM+sXLlww6948u3dYY8uWLVsqHgsAb731lln3ti621o2vXLnSHLtp0yaz7s3TW/Pw3m06MDBg1r1tuGsR79mJgmDYiYJg2ImCYNiJgmDYiYJg2ImCYNiJgggzzz5nzpxM47Nsy3z69Gmz7s3ZWmvpAbu3a6+91hy7bt06s97S0mLWPfv370+tLVy40Bw7d+5cs37//feb9eXLl6fWTpw4YY49f/68WS/yGARFce/ZReQ5EekVkb3DzmsWkY0icjD5PLXYNokoq9E8jP89gNu/cd5DADpUdQGAjuRrIqphbthV9W0A33zM0wpgfXJ6PYA7c+6LiHJW6XP2GaralZzuBjAj7RtFpA1AW4XXQ0Q5yfwCnaqqtWGjqrYDaAfK3diRKLpKp956RGQWACSf7e02iah0lYZ9A4DVyenVAF7Jpx0iKor7MF5EXgRwM4BpItIJ4GEA6wD8SUTuA/AJgHuKbDIP06dPzzTemuu2jo0+mrp3/PPHH3/crFvr6W+77TZz7PXXX2/Wr7vuOrPu7WNuzaV7c/zeHuhLliwx6xbvd+K9t8E7hkEtcsOuqqtSSrfm3AsRFYhvlyUKgmEnCoJhJwqCYScKgmEnCiLMEldv+1+PNRXjHZbYm6Y5deqUWV+7dq1Zz3LZPT09Zn3x4sUVXzcAdHd3p9a86VBvO2iPavobNrNOvXm8y7948WKmy68E79mJgmDYiYJg2ImCYNiJgmDYiYJg2ImCYNiJgggzz551iavFO+xwR0eHWV+xYoVZ7+zsNOvWnG19fb05duxY+0+gr6/PrHus9xhYc/AAMH78eLPu9Wa9x8BbHmttNT0a8+bNM+uHDx/OdPmV4D07URAMO1EQDDtREAw7URAMO1EQDDtREAw7URBh5tmnTJmSaXxjY2NqzZsHX79+vVlfuXKlWfe2fLZ4a+29rai9eXiPtabcW+c/btw4sz4wMGDWn3/++dRalsNQj8a0adPMOufZiagwDDtREAw7URAMO1EQDDtREAw7URAMO1EQYebZm5ubzbo1HwwADQ0NqbVjx46ZY0+ePGnWPd56eWu+2vu5ipbl2O1e795a/a1bt5r1LNd95swZs+69f6EM7j27iDwnIr0isnfYeY+IyFER2Z182O8KIaLSjeZh/O8B3D7C+b9W1SXJx+v5tkVEeXPDrqpvAzhRhV6IqEBZXqB7QET2JA/zp6Z9k4i0icgOEdmR4bqIKKNKw/5bAC0AlgDoAvBk2jeqaruqLlXVpRVeFxHloKKwq2qPql5U1UEAvwOwLN+2iChvFYVdRIbvf/wjAHvTvpeIaoM7zy4iLwK4GcA0EekE8DCAm0VkCQAFcATATwvsMRfeevZz586ZdesY5v39/ebYRYsWmXWPt5e3N99sKXoe3ppv9q7bq3u/0yw/mzdP7h0noMh9Cirlhl1VV41w9rMF9EJEBeLbZYmCYNiJgmDYiYJg2ImCYNiJggizxDXrckrLRx99ZNZbWloqvmzA782aBvLGFr0UM8sSV286dPLkyWa9t7fXrFu83rzbzTuUdBl4z04UBMNOFATDThQEw04UBMNOFATDThQEw04URJh5dm/rYW8ZqeXAgQNmfcWKFRVfNpBt22RvPtirZ10Ca12+t0zU25LZY22l7W2zffnll2e67qampkzji8B7dqIgGHaiIBh2oiAYdqIgGHaiIBh2oiAYdqIgwsyze1vsZplnHxwcNOsLFy406xcuXDDr3nx0mbzerHl673bL8jsBgKuvvjq11t3dbY6dOXOmWfe20ba2+C5L7f4VEVGuGHaiIBh2oiAYdqIgGHaiIBh2oiAYdqIgwsyze3O23nHCLd56c29t9OnTp816lt6yKnJLZ2+ePevP3dramlo7cuSIOfaGG24w617vU6dONetlcO/ZReQqEdkkIvtE5EMR+XlyfrOIbBSRg8nn2vvpiOgro3kYPwDgF6q6GMC/AviZiCwG8BCADlVdAKAj+ZqIapQbdlXtUtVdyek+APsBzAbQCmB98m3rAdxZVJNElN0lPWcXkXkAbgCwFcAMVe1KSt0AZqSMaQPQVnmLRJSHUb8aLyKNAP4MYI2qfj68pkOv4oz4So6qtqvqUlVdmqlTIspkVGEXkToMBf0PqvqX5OweEZmV1GcBqHzLTCIqnPswXoaOBfwsgP2q+qthpQ0AVgNYl3x+pZAOc+JNvY0fP77iy160aJFZr6+vN+ve1sTe1J41DZR1S+YyD0Wddept3rx5qbU9e/aYY++6665M111XV5dpfBFG85z9RgA/AfCBiOxOzluLoZD/SUTuA/AJgHuKaZGI8uCGXVX/DiDt3/et+bZDREXh22WJgmDYiYJg2ImCYNiJgmDYiYIIs8TVO/RvlvlobznjhAkTzLrXm7ecsqixgD9PnqWedQ7/1KlTZn358uWpNW+bbY/3c3u/8zLwnp0oCIadKAiGnSgIhp0oCIadKAiGnSgIhp0oiDDz7N62yN6Wzo2Njam1J5980hx766324kBvTjbr1sWWrPPoWd6f4K1X937uSZMmmfXNmzen1l599VVz7MMPP2zWvd68YxiUgffsREEw7ERBMOxEQTDsREEw7ERBMOxEQTDsREGEmWdvaGgw6968qTVP782pHj9+3KwvWLDArB8+fNisX3ZZcf+zizzuvLfWfmBgwKw3Nzeb9d7e9H1LvN+Jx/t7mTt3bqbLLwLv2YmCYNiJgmDYiYJg2ImCYNiJgmDYiYJg2ImCGM3+7FcBeAHADAAKoF1VfyMijwD4DwDHkm9dq6qvF9VoVu+8845Zt44xDgBnz55NrXnHIL/mmmvMOlXf/PnzzXpfX59ZHzdunFnfvn37JfdUtNG8qWYAwC9UdZeINAHYKSIbk9qvVfW/i2uPiPIymv3ZuwB0Jaf7RGQ/gNlFN0ZE+bqk5+wiMg/ADQC2Jmc9ICJ7ROQ5ERlxDyQRaRORHSKyI1OnRJTJqMMuIo0A/gxgjap+DuC3AFoALMHQPf+IB2JT1XZVXaqqS3Pol4gqNKqwi0gdhoL+B1X9CwCoao+qXlTVQQC/A7CsuDaJKCs37DK0bOlZAPtV9VfDzp817Nt+BGBv/u0RUV5G82r8jQB+AuADEdmdnLcWwCoRWYKh6bgjAH5aSIc52bZtm1n3lsBa2ypn3RaZqq+urs6se1Nr3rLm/v7+S+6paKN5Nf7vAEZalFyzc+pE9G18Bx1REAw7URAMO1EQDDtREAw7URAMO1EQYQ4l3dnZadZ37dpl1q0lrl988UVFPX1p7Fj71+Adtjjr4Z6/q7yf27rdDh06ZI597bXXzPrkyZPN+nvvvWfWy8B7dqIgGHaiIBh2oiAYdqIgGHaiIBh2oiAYdqIgRFWrd2UixwB8MuysaQCy7Z1bnFrtrVb7AthbpfLsba6qTh+pUNWwf+vKRXbU6rHparW3Wu0LYG+VqlZvfBhPFATDThRE2WFvL/n6LbXaW632BbC3SlWlt1KfsxNR9ZR9z05EVcKwEwVRSthF5HYR+UhEDonIQ2X0kEZEjojIByKyu+z96ZI99HpFZO+w85pFZKOIHEw+j7jHXkm9PSIiR5PbbreIrCypt6tEZJOI7BORD0Xk58n5pd52Rl9Vud2q/pxdRMYAOADg3wB0AtgOYJWq7qtqIylE5AiApapa+hswRGQFgH4AL6jqdcl5/wXghKquS/5RTlXVX9ZIb48A6C97G+9kt6JZw7cZB3AngH9Hibed0dc9qMLtVsY9+zIAh1T1Y1U9D+CPAFpL6KPmqerbAE584+xWAOuT0+sx9MdSdSm91QRV7VLVXcnpPgBfbjNe6m1n9FUVZYR9NoB/Dvu6E7W137sC+KuI7BSRtrKbGcEMVe1KTncDmFFmMyNwt/Gupm9sM14zt10l259nxRfovu0mVf0XAD8E8LPk4WpN0qHnYLU0dzqqbbyrZYRtxr9S5m1X6fbnWZUR9qMArhr29ZzkvJqgqkeTz70AXkbtbUXd8+UOusnn3pL7+UotbeM90jbjqIHbrsztz8sI+3YAC0TkByJSD+DHADaU0Me3iMjE5IUTiMhEALeh9rai3gBgdXJ6NYBXSuzla2plG++0bcZR8m1X+vbnqlr1DwArMfSK/GEA/1lGDyl9zQfwfvLxYdm9AXgRQw/rLmDotY37AFwOoAPAQQB/A9BcQ739L4APAOzBULBmldTbTRh6iL4HwO7kY2XZt53RV1VuN75dligIvkBHFATDThQEw04UBMNOFATDThQEw04UBMNOFMT/Azs/zyPR2w4PAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uZjczhnZ378tufyKpvZnNjKFwGe9G+soy4y1z7GpZ/rws3qC7Upe73yfpO5J+kD1dbUk+/BqslcZOx7WMd6OMscz4l5p57Gpd/rysZoT9qKQFI37+WratJbj70ex7v6QNar2lqI99sYJu9r2/yf18qZWW8R5rmXG1wLFr5vLnzQj7Tkm3mdnXzaxN0nclbWpCH1cws+nZGycys+mSvq3WW4p6k6RV2e1VkjY2sZdRWmUZ77xlxtXkY9f05c/dveFfkh7W8Dvy+yX9czN6yOlrkaT/yb7eaXZvktZq+GndoIbf23ha0l9J2iLpA0lvSJrTQr29LOltSXs1HKz5TeqtS8NP0fdK2pN9PdzsY5foqyHHjY/LAkHwBh0QBGEHgiDsQBCEHQiCsANBEHYgCMIOBPH/eT2vutYwnNAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ttd67S/qtmR01s12tbmYK3e4+VNy+IKm7lc1MIbuMdzN9YZnxtnntaln+vF6coPtTz7j7X0vaJOnbxdvVtuQTn8Haaex0Wst4N8sUy4z/UStfu1qXP69XK8I+KGn1pL9XFdvagrsPFr+HJb2t9luK+uK9FXSL38Mt7ueP2mkZ76mWGVcbvHatXP68FWH/UNJaM/uKmXVI+qak2i9BWiEzW1CcOJGZLZD0dbXfUtT7Je0obu+Q9E4Le7lPuyzjXbbMuFr82rV8+XN3b/qPpM2aOCP/f5L+uRU9lPT1mKT/Ln4+bnVvkt7SxNu6O5o4t7FT0hJJhyT1SXpP0uI26m2fpBOSejURrBUt6u0ZTbxF75V0vPjZ3OrXLtFXU143vi4LBMEJOiAIwg4EQdiBIAg7EARhB4Ig7EAQhB0I4v8BJCLolGkTUEMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fvmzGq6qqzLiXW2NjY2rsxIkT5lhva+H58+ebca9Xbt0D4M21zzqX3orPmzfPHOsZw34LmcaXAq/sREGw2ImCYLETBcFiJwqCxU4UBIudKAgWO1EQbp9dRBYB+C2AWgAKoEVVfyEiLwP4PoDzybe+pKo7S5VoqU2cONGMW73uxYsXm2O9PnpHR4cZHxwcNOOHDx9OjXlr0i9fvjzTsb39263z1tfXZ47Nen+CtS69taa8Nxbw17SvxD77WG6qGQDwE1X9UESmA/hARHYnsZ+r6r+VLj0iKpax7M/eBaAr+bxPRD4GsKDUiRFRcX2j39lFZAmAFQD+kjz0IxFpE5GtIjIzZUyziLSKSGumTIkokzEXu4hMA/AHAC+q6hUAvwTwLQBNGLry/2y0caraoqqrVHVVEfIlogKNqdhFZBKGCv13qvpHAFDVblW9paqDAH4FYHXp0iSirNxil6G3FX8N4GNV/fcRj9eN+LYNAA4WPz0iKhYZw1S9NQD+F8ABAMN9mJcAbMTQS3gFcALAD5I386yfVXnr6ya8paStFpQ3VXPTpk1m/MEHHzTjM2bMMOPWctDeMtZe7ufPnzfjM2eO+lbNF6wptDU1NeZYb5lqrzV34cKF1Nirr75qjt23b58Zr2SqOmrfbyzvxu8DMNrgv9qeOlFEvIOOKAgWO1EQLHaiIFjsREGw2ImCYLETBeH22Yt6sArus1ey+vp6M25NU/V61dXV1Wbcu//AYy3nPDAwYI49deqUGX/33XfN+NWrV834eJXWZ+eVnSgIFjtRECx2oiBY7ERBsNiJgmCxEwXBYicKotx99vMATo54aDaA9EnH+arU3Co1L4C5FaqYuS1W1TmjBcpa7F87uEhrpa5NV6m5VWpeAHMrVLly48t4oiBY7ERB5F3sLTkf31KpuVVqXgBzK1RZcsv1d3YiKp+8r+xEVCYsdqIgcil2EXlcRA6LyBER2ZxHDmlE5ISIHBCR/XnvT5fsodcjIgdHPFYjIrtFpCP5aC/cXt7cXhaRzuTc7ReRdTnltkhE/iwih0TkIxH5cfJ4rufOyKss563sv7OLyEQAnwD4OwBnALwPYKOqHiprIilE5ASAVaqa+w0YIvIwgKsAfquq304e+xcAvaq6JfmPcqaq/mOF5PYygKt5b+Od7FZUN3KbcQBPAvh75HjujLyeQhnOWx5X9tUAjqjqMVW9CeD3ANbnkEfFU9W9AHq/8vB6ANuSz7dh6MlSdim5VQRV7VLVD5PP+wAMbzOe67kz8iqLPIp9AYDTI74+g8ra710B7BKRD0SkOe9kRlE7YputcwDsdafKz93Gu5y+ss14xZy7QrY/z4pv0H3dGlVdCWAtgB8mL1crkg79DlZJvdMxbeNdLqNsM/6FPM9dodufZ5VHsXcCWDTi64XJYxVBVTuTjz0A3kTlbUXdPbyDbvKxJ+d8vlBJ23iPts04KuDc5bn9eR7F/j6AZSLSICJVAL4HYEcOeXyNiExN3jiBiEwF8F1U3lbUOwA8k3z+DIC3cszlSyplG++0bcaR87nLfftzVS37HwDrMPSO/FEA/5RHDil53Qng/5I/H+WdG4DXMfSyrh9D7208D2AWgD0AOgD8D4CaCsrtNQxt7d2GocKqyym3NRh6id4GYH/yZ13e587IqyznjbfLEgXBN+iIgmCxEwXBYicKgsVOFASLnSgIFjtRECx2oiD+H390cf1p5OgMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VyamEQh5w3I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}