CNN - 2D.ipynb

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      "display_name": "Python 3",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "2DCNN.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sid-vt/DL-Research/blob/main/CNN%20-%202D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZTOsA_Zf-Qf"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "# import CV\n",
        "from PIL import Image\n",
        "from matplotlib import pyplot as plt\n",
        "# import scikit\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import pandas  as pd\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout, BatchNormalization\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import matplotlib.pyplot as plt\n",
        "import h5py\n",
        "import sklearn as sk\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "pd.options.display.max_rows = 3000\n",
        "import torchvision"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "lXbX2qBZgPkr",
        "outputId": "ddefe8b3-e14f-4f80-b246-6f89b3fa6950"
      },
      "source": [
        "# from google.colab import files\r\n",
        "# uploaded = files.upload()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c0f153b8-8cf9-4b1e-8a9b-0c99ef5fa0c6\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c0f153b8-8cf9-4b1e-8a9b-0c99ef5fa0c6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving results.txt to results.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmymEeqXf-Qr",
        "outputId": "a8982f8f-0171-4be6-dbb7-34a2cf774a71"
      },
      "source": [
        "X = np.load('ImgDS1.npy')\r\n",
        "X.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 64, 65, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbAz1fKef-Qt"
      },
      "source": [
        "# Scaling with min max\n",
        "\n",
        "X_min = X.min()\n",
        "X_max = X.max()\n",
        "\n",
        "X = (X - X_min)/(X_max - X_min)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "wWWHTlr_f-Qu",
        "outputId": "c73f5aa3-b53a-448b-a1c1-8f975bc3b841"
      },
      "source": [
        "from PIL import Image\n",
        "\n",
        "data = X[10]\n",
        "# data[0:256, 0:256] = [255, 0, 0] # red patch in upper left\n",
        "img = Image.fromarray(data, 'RGB')\n",
        "# img.save('my.png')\n",
        "# img.show()\n",
        "plt.imshow(data, interpolation='nearest')\n",
        "plt.show()\n",
        "X[10].mean()"
      ],
      "execution_count": 52,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "0.5143671442936147"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "COMbmvOBf-Qx",
        "outputId": "a65fdb62-fe0a-4dca-a4f1-032774a06243"
      },
      "source": [
        "from PIL import Image\n",
        "\n",
        "data = X[9]\n",
        "# data[0:256, 0:256] = [255, 0, 0] # red patch in upper left\n",
        "img = Image.fromarray(data, 'RGB')\n",
        "# img.save('my.png')\n",
        "# img.show()\n",
        "plt.imshow(data, interpolation='nearest')\n",
        "plt.show()\n",
        "X[11].mean()"
      ],
      "execution_count": 53,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "0.5638555806938159"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJ-5uRn0f-Qy"
      },
      "source": [
        "# y = []\n",
        "# for i in range (X.shape[0]):\n",
        "#     temp = X[i].mean()\n",
        "#     y.append(temp)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Sk5sv2Zf-Q1"
      },
      "source": [
        "# X = np.array(y)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yaA_gJIf-Q2"
      },
      "source": [
        "y = np.loadtxt('results.txt')"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dapzGjZwf-Q3"
      },
      "source": [
        "y  = (y - y.min())/(y.max()-y.min())"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoFQeqBPf-Q4"
      },
      "source": [
        "# plt.scatter(X,y)"
      ],
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTcGNt6ef-Q5"
      },
      "source": [
        " X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.1)"
      ],
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0PM9nInf-Q6",
        "outputId": "7464af61-5fcf-4b29-c711-bac8294e9e78"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 287,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4500, 64, 65, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 287
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMr-AT9Ef-Q7",
        "outputId": "7659e895-8df6-4370-a256-515b4284e953"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4500,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKgL1IT7f-Q8",
        "outputId": "38c37059-7b41-417b-9a81-60120102396c"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64, kernel_size=(2,2 ), activation='relu', kernel_initializer='glorot_uniform', input_shape=(64,65,3)))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# model.add(BatchNormalization(axis = 1))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "# model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# model.add(MaxPooling2D(pool_size=(2, 2,)))\n",
        "# model.add(Dropout(0.1))\n",
        "\n",
        "# model.add(Conv2D(128, kernel_size=(2, 2), activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# model.add(MaxPooling2D(pool_size=(2, 2,)))\n",
        "# model.add(Dropout(0.5))\n",
        "\n",
        "# model.add(Conv3D(256, kernel_size=(2, 2, 2), activation='relu', kernel_initializer='glorot_uniform'))\n",
        "# model.add(MaxPooling3D(pool_size=(2, 2, 2)))\n",
        "# model.add(BatchNormalization())\n",
        "\n",
        "# model.add(BatchNormalization(center=True, scale=True))\n",
        "# model.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "# model.add(Dense(256, activation='relu', kernel_initializer='glorot_uniform'))\n",
        "model.add(Dense(256, activation='relu', kernel_initializer='glorot_uniform'))\n",
        "model.add(Dense(1, activation='linear'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_27 (Conv2D)           (None, 63, 64, 64)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_27 (MaxPooling (None, 31, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_27 (Dropout)         (None, 31, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_23 (Flatten)         (None, 63488)             0         \n",
            "_________________________________________________________________\n",
            "dense_46 (Dense)             (None, 256)               16253184  \n",
            "_________________________________________________________________\n",
            "dense_47 (Dense)             (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 16,254,273\n",
            "Trainable params: 16,254,273\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoBGbYIBf-Q9",
        "outputId": "6d579158-7737-4bce-e223-3c55adcef4ac"
      },
      "source": [
        "# Compile the model\n",
        "model.compile( loss=tf.keras.losses.MeanSquaredError(),\n",
        "              optimizer=keras.optimizers.Adam(lr=0.0001),\n",
        "              metrics=['mae'])\n",
        "\n",
        "# Fit data to model\n",
        "history = model.fit(X_train ,y_train, validation_data=(X_test,y_test),\n",
        "            batch_size=75,\n",
        "            epochs=100,\n",
        "            verbose=1)"
      ],
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "60/60 [==============================] - 1s 17ms/step - loss: 0.0492 - mae: 0.1829 - val_loss: 0.0448 - val_mae: 0.1734\n",
            "Epoch 2/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0450 - mae: 0.1744 - val_loss: 0.0518 - val_mae: 0.1950\n",
            "Epoch 3/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0473 - mae: 0.1795 - val_loss: 0.0470 - val_mae: 0.1835\n",
            "Epoch 4/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0452 - mae: 0.1777 - val_loss: 0.0493 - val_mae: 0.1896\n",
            "Epoch 5/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0448 - mae: 0.1746 - val_loss: 0.0462 - val_mae: 0.1807\n",
            "Epoch 6/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0449 - mae: 0.1772 - val_loss: 0.0509 - val_mae: 0.1933\n",
            "Epoch 7/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0452 - mae: 0.1775 - val_loss: 0.0457 - val_mae: 0.1696\n",
            "Epoch 8/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0464 - mae: 0.1769 - val_loss: 0.0458 - val_mae: 0.1796\n",
            "Epoch 9/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0436 - mae: 0.1736 - val_loss: 0.0459 - val_mae: 0.1803\n",
            "Epoch 10/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0438 - mae: 0.1739 - val_loss: 0.0471 - val_mae: 0.1839\n",
            "Epoch 11/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0435 - mae: 0.1742 - val_loss: 0.0480 - val_mae: 0.1860\n",
            "Epoch 12/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0446 - mae: 0.1757 - val_loss: 0.0483 - val_mae: 0.1870\n",
            "Epoch 13/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0436 - mae: 0.1733 - val_loss: 0.0472 - val_mae: 0.1832\n",
            "Epoch 14/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0451 - mae: 0.1769 - val_loss: 0.0467 - val_mae: 0.1814\n",
            "Epoch 15/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0438 - mae: 0.1739 - val_loss: 0.0449 - val_mae: 0.1757\n",
            "Epoch 16/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0482 - mae: 0.1822 - val_loss: 0.0463 - val_mae: 0.1808\n",
            "Epoch 17/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0449 - mae: 0.1759 - val_loss: 0.0474 - val_mae: 0.1842\n",
            "Epoch 18/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0433 - mae: 0.1729 - val_loss: 0.0449 - val_mae: 0.1699\n",
            "Epoch 19/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0435 - mae: 0.1733 - val_loss: 0.0448 - val_mae: 0.1729\n",
            "Epoch 20/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0450 - mae: 0.1765 - val_loss: 0.0449 - val_mae: 0.1756\n",
            "Epoch 21/100\n",
            "60/60 [==============================] - 1s 17ms/step - loss: 0.0449 - mae: 0.1761 - val_loss: 0.0455 - val_mae: 0.1721\n",
            "Epoch 22/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0446 - mae: 0.1758 - val_loss: 0.0448 - val_mae: 0.1719\n",
            "Epoch 23/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0438 - mae: 0.1747 - val_loss: 0.0448 - val_mae: 0.1740\n",
            "Epoch 24/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0443 - mae: 0.1737 - val_loss: 0.0451 - val_mae: 0.1767\n",
            "Epoch 25/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0445 - mae: 0.1738 - val_loss: 0.0512 - val_mae: 0.1739\n",
            "Epoch 26/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0453 - mae: 0.1759 - val_loss: 0.0470 - val_mae: 0.1747\n",
            "Epoch 27/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0438 - mae: 0.1735 - val_loss: 0.0452 - val_mae: 0.1773\n",
            "Epoch 28/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0440 - mae: 0.1746 - val_loss: 0.0535 - val_mae: 0.1990\n",
            "Epoch 29/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0457 - mae: 0.1770 - val_loss: 0.0476 - val_mae: 0.1850\n",
            "Epoch 30/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0440 - mae: 0.1737 - val_loss: 0.0529 - val_mae: 0.1973\n",
            "Epoch 31/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0441 - mae: 0.1730 - val_loss: 0.0450 - val_mae: 0.1749\n",
            "Epoch 32/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0427 - mae: 0.1716 - val_loss: 0.0490 - val_mae: 0.1886\n",
            "Epoch 33/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0425 - mae: 0.1718 - val_loss: 0.0453 - val_mae: 0.1775\n",
            "Epoch 34/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0440 - mae: 0.1732 - val_loss: 0.0444 - val_mae: 0.1734\n",
            "Epoch 35/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0422 - mae: 0.1702 - val_loss: 0.0449 - val_mae: 0.1747\n",
            "Epoch 36/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0422 - mae: 0.1710 - val_loss: 0.0454 - val_mae: 0.1774\n",
            "Epoch 37/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0422 - mae: 0.1704 - val_loss: 0.0453 - val_mae: 0.1759\n",
            "Epoch 38/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0435 - mae: 0.1745 - val_loss: 0.0448 - val_mae: 0.1725\n",
            "Epoch 39/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0423 - mae: 0.1712 - val_loss: 0.0453 - val_mae: 0.1769\n",
            "Epoch 40/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0426 - mae: 0.1711 - val_loss: 0.0483 - val_mae: 0.1718\n",
            "Epoch 41/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0458 - mae: 0.1772 - val_loss: 0.0453 - val_mae: 0.1734\n",
            "Epoch 42/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0433 - mae: 0.1737 - val_loss: 0.0464 - val_mae: 0.1709\n",
            "Epoch 43/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0441 - mae: 0.1743 - val_loss: 0.0456 - val_mae: 0.1699\n",
            "Epoch 44/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0435 - mae: 0.1741 - val_loss: 0.0451 - val_mae: 0.1740\n",
            "Epoch 45/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0426 - mae: 0.1715 - val_loss: 0.0457 - val_mae: 0.1774\n",
            "Epoch 46/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0426 - mae: 0.1705 - val_loss: 0.0451 - val_mae: 0.1756\n",
            "Epoch 47/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0431 - mae: 0.1722 - val_loss: 0.0456 - val_mae: 0.1769\n",
            "Epoch 48/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0427 - mae: 0.1725 - val_loss: 0.0453 - val_mae: 0.1764\n",
            "Epoch 49/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0420 - mae: 0.1701 - val_loss: 0.0494 - val_mae: 0.1891\n",
            "Epoch 50/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0453 - mae: 0.1773 - val_loss: 0.0452 - val_mae: 0.1738\n",
            "Epoch 51/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0435 - mae: 0.1729 - val_loss: 0.0460 - val_mae: 0.1794\n",
            "Epoch 52/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0429 - mae: 0.1706 - val_loss: 0.0468 - val_mae: 0.1804\n",
            "Epoch 53/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0423 - mae: 0.1709 - val_loss: 0.0455 - val_mae: 0.1756\n",
            "Epoch 54/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0436 - mae: 0.1727 - val_loss: 0.0475 - val_mae: 0.1845\n",
            "Epoch 55/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0435 - mae: 0.1732 - val_loss: 0.0462 - val_mae: 0.1796\n",
            "Epoch 56/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0447 - mae: 0.1764 - val_loss: 0.0472 - val_mae: 0.1828\n",
            "Epoch 57/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0432 - mae: 0.1726 - val_loss: 0.0479 - val_mae: 0.1720\n",
            "Epoch 58/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0446 - mae: 0.1744 - val_loss: 0.0461 - val_mae: 0.1795\n",
            "Epoch 59/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0416 - mae: 0.1688 - val_loss: 0.0456 - val_mae: 0.1770\n",
            "Epoch 60/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0433 - mae: 0.1728 - val_loss: 0.0454 - val_mae: 0.1767\n",
            "Epoch 61/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0427 - mae: 0.1712 - val_loss: 0.0465 - val_mae: 0.1787\n",
            "Epoch 62/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0425 - mae: 0.1703 - val_loss: 0.0481 - val_mae: 0.1851\n",
            "Epoch 63/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0424 - mae: 0.1690 - val_loss: 0.0485 - val_mae: 0.1866\n",
            "Epoch 64/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0424 - mae: 0.1708 - val_loss: 0.0454 - val_mae: 0.1747\n",
            "Epoch 65/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0428 - mae: 0.1710 - val_loss: 0.0459 - val_mae: 0.1766\n",
            "Epoch 66/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0431 - mae: 0.1719 - val_loss: 0.0457 - val_mae: 0.1727\n",
            "Epoch 67/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0421 - mae: 0.1694 - val_loss: 0.0470 - val_mae: 0.1812\n",
            "Epoch 68/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0413 - mae: 0.1680 - val_loss: 0.0458 - val_mae: 0.1771\n",
            "Epoch 69/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0415 - mae: 0.1684 - val_loss: 0.0463 - val_mae: 0.1802\n",
            "Epoch 70/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0423 - mae: 0.1697 - val_loss: 0.0459 - val_mae: 0.1761\n",
            "Epoch 71/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0416 - mae: 0.1682 - val_loss: 0.0465 - val_mae: 0.1799\n",
            "Epoch 72/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0420 - mae: 0.1703 - val_loss: 0.0474 - val_mae: 0.1827\n",
            "Epoch 73/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0428 - mae: 0.1721 - val_loss: 0.0488 - val_mae: 0.1871\n",
            "Epoch 74/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0426 - mae: 0.1695 - val_loss: 0.0459 - val_mae: 0.1759\n",
            "Epoch 75/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0431 - mae: 0.1709 - val_loss: 0.0466 - val_mae: 0.1799\n",
            "Epoch 76/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0408 - mae: 0.1667 - val_loss: 0.0459 - val_mae: 0.1752\n",
            "Epoch 77/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0410 - mae: 0.1676 - val_loss: 0.0542 - val_mae: 0.1998\n",
            "Epoch 78/100\n",
            "60/60 [==============================] - 1s 16ms/step - loss: 0.0431 - mae: 0.1711 - val_loss: 0.0487 - val_mae: 0.1864\n",
            "Epoch 79/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0417 - mae: 0.1687 - val_loss: 0.0469 - val_mae: 0.1810\n",
            "Epoch 80/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0412 - mae: 0.1669 - val_loss: 0.0520 - val_mae: 0.1947\n",
            "Epoch 81/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0413 - mae: 0.1690 - val_loss: 0.0466 - val_mae: 0.1719\n",
            "Epoch 82/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0418 - mae: 0.1675 - val_loss: 0.0486 - val_mae: 0.1855\n",
            "Epoch 83/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0425 - mae: 0.1695 - val_loss: 0.0460 - val_mae: 0.1757\n",
            "Epoch 84/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0412 - mae: 0.1679 - val_loss: 0.0468 - val_mae: 0.1726\n",
            "Epoch 85/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0409 - mae: 0.1659 - val_loss: 0.0460 - val_mae: 0.1736\n",
            "Epoch 86/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0432 - mae: 0.1703 - val_loss: 0.0459 - val_mae: 0.1750\n",
            "Epoch 87/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0417 - mae: 0.1697 - val_loss: 0.0457 - val_mae: 0.1748\n",
            "Epoch 88/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0402 - mae: 0.1654 - val_loss: 0.0480 - val_mae: 0.1831\n",
            "Epoch 89/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0411 - mae: 0.1669 - val_loss: 0.0468 - val_mae: 0.1798\n",
            "Epoch 90/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0401 - mae: 0.1647 - val_loss: 0.0463 - val_mae: 0.1761\n",
            "Epoch 91/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0402 - mae: 0.1655 - val_loss: 0.0466 - val_mae: 0.1786\n",
            "Epoch 92/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0398 - mae: 0.1638 - val_loss: 0.0469 - val_mae: 0.1806\n",
            "Epoch 93/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0412 - mae: 0.1684 - val_loss: 0.0469 - val_mae: 0.1779\n",
            "Epoch 94/100\n",
            "60/60 [==============================] - 1s 13ms/step - loss: 0.0420 - mae: 0.1698 - val_loss: 0.0467 - val_mae: 0.1763\n",
            "Epoch 95/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0406 - mae: 0.1665 - val_loss: 0.0472 - val_mae: 0.1800\n",
            "Epoch 96/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0408 - mae: 0.1665 - val_loss: 0.0464 - val_mae: 0.1756\n",
            "Epoch 97/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0407 - mae: 0.1663 - val_loss: 0.0465 - val_mae: 0.1764\n",
            "Epoch 98/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0419 - mae: 0.1684 - val_loss: 0.0466 - val_mae: 0.1782\n",
            "Epoch 99/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0401 - mae: 0.1657 - val_loss: 0.0471 - val_mae: 0.1788\n",
            "Epoch 100/100\n",
            "60/60 [==============================] - 1s 14ms/step - loss: 0.0397 - mae: 0.1639 - val_loss: 0.0489 - val_mae: 0.1862\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "QCD5i_1wf-Q-",
        "outputId": "66d89e06-4133-472c-a04b-95e4aaddd21f"
      },
      "source": [
        "plt.semilogy(history.history['loss'])\n",
        "plt.semilogy(history.history['val_loss'])\n",
        "plt.title('Loss vs Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.yscale('log')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 291,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq94hhs0f-Q_"
      },
      "source": [
        "predictions_all = model.predict(X)\n",
        "predictions_train = model.predict(X_train)\n",
        "predictions_test = model.predict(X_test)"
      ],
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "acjwVhl7f-Q_",
        "outputId": "f198990a-3512-4688-b64c-6125424335ba"
      },
      "source": [
        "xlim = 1\n",
        "x = np.linspace(0,xlim,11)\n",
        "plt.plot(x, x)\n",
        "\n",
        "# x = np.linspace(1,1,10)\n",
        "plt.scatter(predictions_all,y)\n",
        "# plt.plot(x,x)"
      ],
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f29946b4cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 299
        },
        {
          "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": "FOALfuzcf-RA"
      },
      "source": [
        "import seaborn as snsn"
      ],
      "execution_count": 294,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "JV2Z23cMf-RA",
        "outputId": "82643a8a-0acf-4295-bd6c-38da09cbd81c"
      },
      "source": [
        "import seaborn as sns\n",
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(predictions_test.ravel(),label=\"\",ax=ax,c='b')\n",
        "sns.kdeplot(y_test.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')\n",
        "\n",
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(predictions_train.ravel(),label=\"\",ax=ax,c='b')\n",
        "sns.kdeplot(y_train.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')\n"
      ],
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yi4CCW0TixOGCGzQJiwgouEUkTkQS3DrOLaLgFpE4oeAWiYyCW0TigoJbJDIKbhGJCwpukcgouEUkLii4RSKj4BaRuKDgFomMgltEAtfc7N0OFtxZWd5rCm4RBbeIxIFDXdIzrGdPncctAgpuEYkD4RnRDnRJz7CCAtixw596ROKZgltEAtfU5C27dz/4e3SFMBGPgltEAqfgFomcgltEAhcO7kN1lffsqa5yEVBwi0gciPQYt1rcIgpuEYkDkba4dU1ukRgGt5n9xsw2m9n7ezz3X2b2kZktN7OnzKxnrLYvIokj0uDWNblFYtvifhA4a5/nXgJGOOdGAZ8AP43h9kUkQUQ6OA10nFskZsHtnHsD2LrPcy8651pCDxcC/WO1fRFJHJG0uAsKvKWOc0uqC/IY99eBvx/sRTObZWaLzWxxdXW1j2WJiN8i7SoHBbdIIMFtZjcBLcAfDvYe59wDzrlxzrlxJSUl/hUnIr6LZFS5glvEk+H3Bs1sJnAOMM05jQ8VkY61uHWMW1Kdr8FtZmcBPwZOc841+LltEYlfOsYtErlYng72KPAWcLSZbTCzq4FfAXnAS2a2zMzuj9X2RSRxNDWBmXf5zoNRV7mIJ2YtbufcpQd4em6sticiiaupyWttmx38Pd26ecGu4JZUp5nTRCRw4eA+FDPNVy4CCm4RiQORBDdovnIRUHCLSBxobIwsuHVpTxEFt4jEgaamQ093GqbgFlFwi0gciLSrXMe4RRTcIhIHdIxbJHIKbhEJXEda3ApuSXUKbhEJXEeCu7ERdu+OfU0i8UrBLSKB68ioctBxbkltCm4RCVyko8o1X7mIgltE4kBHuspBwS2pTcEtIoFTcItETsEtIoHraHDrGLekMgW3iASuI+dxg1rcktoU3CISqOZmaG2NfMpTUHBLalNwi0igmpq8ZSQt7txcSEtTcEtqU3CLSKA6Ety6JreIgltEAtaR4AbNVy6i4BaRQHU0uDVfuaQ6BbeIBErBLdIxCm4RCVRjo7eMZFQ56Bi3iIJbRAKlY9wiHaPgFpFAqatcpGMU3CISqM4Ed12dN2mLSCpScItIoDoT3AC1tbGpRyTeKbhFJFDh4I50cJrmK5dUp+AWkUCFR5V3tMWt4JZUpeAWkUB1tqtcwS2pSsEtIoHqbHDrXG5JVQpuEQlUOLizsyN7v45xS6pTcItIoJqaICvLu1xnJNRVLqlOwS0igWpqinxEOUB+vrdUcEuqillwm9lvzGyzmb2/x3OFZvaSma0KLXvFavsikhiamiI/vg2Qng55eTrGLakrli3uB4Gz9nnuJ8ArzrmjgFdCj0UkhTU2diy4QdOeSmqLWXA7594Atu7z9HnAQ6H7DwHnx2r7IpIYOtriBgW3pDa/j3GXOuc2hu5vAkp93r6IxBkFt0jHBDY4zTnnAHew181slpktNrPF1dXVPlYmIn7q6OA08E4J0zFuSVV+B3eVmfUFCC03H+yNzrkHnHPjnHPjSkpKfCtQRPylFrdIx/gd3H8BrgzdvxJ4xufti0icUXCLdEwsTwd7FHgLONrMNpjZ1cBtwBfMbBVwRuixiKSwzo4q37ED3EEPtokkr4xYrdg5d+lBXpoWq22KSOLpTIu7oADa2mDnTu+cbpFUopnTRCRQnRmcpmlPJZUpuEUkUJ3tKgcFt6QmBbeIBKqzg9NAp4RJalJwi0igGhs7dx43qMUtqUnBLSKBaWnxbuoqF4mcgltEAtPU5C01OE0kcgpuEQlMOLg7czoY6Bi3pCYFt4gEprHRW3a0xZ2V5X1GLW5JRQpuEQlMZ1vcoGlPJXUpuEUkMApukY5TcItIYDrbVQ7Qqxds2xbdekQSgYJbRALTlRZ3YSFs3RrdekQSgYJbRALTlRZ3URHU1ES3HpFEoOAWkcCoxS3ScQpuEQlMuMXd2eDeuRN2745uTSLxTsEtIoHp7Mxp4AU3aICapB4Ft4gEpitd5UVF3lLHuSXVKLhFJDBdGZwWbnHrOLekGgW3iASmq4PTQMEtqUfBLSKBaWwEM2/u8Y5ScEuqiii4zexJM/uSmSnoRSRqmpq81rZZxz+rY9ySqiIN4vuAy4BVZnabmR0dw5pEJEU0NnaumxwgLw/S09XiltQTUXA75152zl0OjAXKgZfNbIGZXWVmmbEsUESSV1NT5wamgddK1yQskooi7vo2syJgJvAN4F3gbrwgfykmlYlI0gt3lXeWgltSUUYkbzKzp4CjgYeBLzvnNoZe+qOZLY5VcSKS3BobO9/iBi+4dYxbUk1EwQ382jn3tz2fMLNs59wu59y4GNQlIimgqy3uoiKoqIhePSKJINKu8p8f4Lm3olmIiKSergxOA3WVS2o6ZIvbzPoAZUB3MzseCJ+0kQ/kxLg2EUlyTU2Q04W/JApuSUWH6yo/E29AWn/gzj2erwNujFFNIpIiGhs/n0ilMwoLoa7Ou0JYZyZxEUlEhwxu59xDwENmdqFz7s8+1SQiKaIrp4PB55OwbNsGpaXRqUkk3h2uq/yrzrnfA4PN7Lp9X3fO3XmAj4mIRCQap4OB112u4JZUcbiu8h6hZW6sCxGR1BONwWmg49ySWg7XVf5/oeXPorlRM/sh3kQuDlgBXOWca4rmNkQk/nW1qzwc3DqXW1JJpBcZud3M8s0s08xeMbNqM/tqZzZoZmXAtcA459wIIB24pDPrEpHE1tUWd/gYt1rckkoiPY97unOuFjgHb67yocCPurDdDLxTzDLwTiur7MK6RCQBOQe7dqnFLdJRkQZ3uEv9S8Djzrkdnd2gc64CuANYB2wEdjjnXuzs+kQkMTWFDo51pcWdnw8ZGQpuSS2RBvezZvYRcALwipmVAJ06Jm1mvYDzgCFAP6DHgbrdzWyWmS02s8XV1dWd2ZSIxLFwcHelxR2+QpiCW1JJpJf1/AkwGe+4dDNQjxe+nXEG8Jlzrjq0ridD6953mw8458Y558aVlJR0clMiEq8aG71lV1rc4B3nVnBLKon0IiMAw/HO597zM7/rxDbXARPNLAdoBKYBusKYSIqJRlc5QHExbNnS9XpEEkWkl/V8GDgSWAa0hp52dCK4nXOLzOwJYCnQgndt7wc6uh4RSWzR6CoHr8X96addr0ckUUTa4h4HHOucc9HYqHPuZuDmaKxLRBJTNLvKFy3qej0iiSLSwWnvA31iWYiIpJZotbiLi71j3NFpVojEv0hb3MXAh2b2NrAr/KRz7tyYVCUiSS+aLe7du2HnTsjL63pdIvEu0uC+JZZFiEjqidbgtPDsaTU1Cm5JDZGeDjYPb8a0zND9d/AGl4mIdEq4xR2NrnLQKWGSOiKdq/ybwBPA/4WeKgOejlVRIpL8ot3i1ilhkioiHZx2DXASUAvgnFsF9I5VUSKS/KJ5OhioxS2pI9Lg3uWc2x1+EJqERWM4RaTTojk4DRTckjoiDe55ZnYj3hW9vgA8Dvw1dmWJSLKLVld5r17enOXqKpdUEWlw/wSoBlYA3wL+BvxrrIoSkeQXrRZ3Rgb07KkWt6SOiE4Hc861mdnTwNPOOV2qS0S6rKkJMjMhPb3r69KFRiSVHLLFbZ5bzGwL8DHwsZlVm9m/+VOeiCSrxsauD0wLC8+eJpIKDtdV/kO80eQnOucKnXOFwATgJDP7YcyrE5Gk1dTU9W7ysKIiHeOW1HG44L4CuNQ591n4CefcGuCrwNdiWZiIJLeGBsjJic661FUuqeRwwZ3pnNvve2zoOHdmbEoSkVQQzeBWV7mkksMF9+5OviYickjRbnHX139+iplIMjvcqPLRZlZ7gOcNiNLRKRFJRdEObvBa3WVl0VmnSLw6ZHA756JwooaIyP4aG6GwMDrrUnBLKol0AhYRkaiK9jFu0MhySQ0KbhEJREND9M7j1nzlkkoU3CISiFgd4xZJdgpuEQmEglukcxTcIhKIaAZ3djbk5uoYt6QGBbeI+K65GVpaohfcoNnTJHUouEXEd+FLekYzuDV7mqQKBbeI+K6hwVtGa1Q56EIjkjoU3CLiu3Bwq6tcpOMU3CLiOwW3SOcpuEXEd7EI7uJi2L7dG/QmkswU3CLiu1i1uAG2bo3eOkXikYJbRHwXi1HlmoRFUoWCW0R8F6uuclBwS/JTcIuI72J1OhjolDBJfoEEt5n1NLMnzOwjM1tpZpOCqENEghHLY9xqcUuyywhou3cDzzvnZphZFhDF/74iEu/UVS7Seb4Ht5kVAKcCMwGcc7uB3X7XISLBiUVw5+R4FxtRV7kkuyC6yocA1cBvzexdM5tjZj0CqENEAtLQAOnpkJkZvXWaaRIWSQ1BBHcGMBb4X+fc8UA98JN932Rms8xssZktrq6u9rtGEYmhxkavhWwW3fUquCUVBBHcG4ANzrlFocdP4AX5XpxzDzjnxjnnxpWUlPhaoIjEVkNDdEeUhxUXq6tckp/vwe2c2wSsN7OjQ09NAz70uw4RCU5DQ3SPb4epxS2pIKhR5d8D/hAaUb4GuCqgOkQkAApukc4LJLidc8uAcUFsW0SCF6vgLi725ipva4M0TS8lSUq/2iLiu1gd4y4qgtZW2LEj+usWiRcKbhHxXX095OZGf72aPU1SgYJbRHy3c2dsgluzp0kqUHCLiO9iFdy60IikAgW3iPiuvh56xGC+RHWVSypQcIuI72Ld4lZwSzJTcIuIr3bvhubm2AR3QYE3B7q6yiWZKbhFxFf19d4yFsGdlgaFhWpxS3JTcIuIr3bu9JaxOMYNmj1Nkp+CW0R8FQ7uWLS4wTslTMEtyUzBLSK+inVwFxXpGLckNwW3iPgqlse4QV3lkvwU3CLiq1gf4w53lTsXm/WLBE3BLSK+8qOrfNeuz1v2IslGwS0ivvKjqxzUXS7JS8EtIr7yo8UNCm5JXgpuEfGVH+dxg4JbkpeCW0R8VV8PmZmQlRWb9YeDe+vW2KxfJGgKbhHxVawuMBJWWOgt1eKWZKXgFhFf7dwZu25yUHBL8lNwi4ivYt3izsqCvDwFtyQvBbeI+Kq+PrbBDZo9TZKbgltEfBXrFjcouCW5KbhFxFexPsYN3nFujSqXZKXgFhFfqatcpGsU3CLiK3WVi3SNgltEfFVX509wb98Ora2x3Y5IEBTcIuKbtjYvuAsKYrudoiLvsp7btsV2OyJBUHCLiG927vQCNT8/ttvRfOWSzBTcIuKb2lpvGevgDs+eppHlkowU3CLim3Bw+9FVDmpxS3JScIuIb3bs8JbqKhfpvMCC28zSzexdM3s2qBpExF9qcYt0XZAt7u8DKwPcvoj4zK8Wd0EBpKcruCU5BRLcZtYf+BIwJ4jti0gw/Gpxm3kD1BTckoyCanHfBfwYaAto+yISAL9a3KD5yiV5+R7cZnYOsNk5t+Qw75tlZovNbHF1dbVP1YlILNXWeq3hWM+cBpr2VJJXEC3uk4BzzawceAyYama/3/dNzrkHnHPjnHPjSkpK/K5RRGKgthby8iDNh788Cm5JVr4Ht3Pup865/s65wcAlwKvOua/6XYeI+G/HDn+6yUHBLclL53GLiG9qa2M/MC1MwS3JKtDgds697pw7J8gaRMQ/fra4CwuhsdG7iSQTtbhFxDe1tf52lYNGlkvyUXCLiG/87ioHdZdL8lFwi4hv/B6cBgpuST4KbhHxjVrcIl2n4BYRX7S0QH29WtwiXaXgFhFf1NV5S7+De8sWf7Yn4hcFt4j4Yts2b9mrlz/b69bN+5JQVeXP9kT8ouAWEV+ET8sqLPRvm6WlCm5JPgpuEfFF+FhzuAvbDwpuSUYKbhHxRVAt7s2b/dueiB8U3CLii3CLW13lIl2j4BYRXwTV4t66FZqb/dumSKwpuEXEFzU13ijvjAz/tlla6i3VXS7JRMEtIr7YutXfgWkAvXt7S3WXSzJRcIuIL2pq/O0mh89b3ApuSSYKbhHxRRAtbgW3JCMFt4j4Qi1ukehQcIuIL7Zu9T+4c3MhJ0eD0yS5KLhFJOba2ry5yv3uKgfo0wcqK/3frkisKLhFJOa2bwfn/G9xAwwcCOvX+79dkVhRcItIzIUnXwmixa3glmSj4BaRmAtiutOwAQOgogJaWvzftkgsKLhFJOaqq71lcbH/2x44EFpbYeNG/7ctEgsKbhGJuU2bvGsHHLMAABpBSURBVGXfvv5ve+BAb7lunf/bFokFBbeIxFw4uMPnVfspHNw6zi3JQsEtIjG3aRP06gXZ2f5ve8AAb6kWtyQLBbeIxNzGjcF0kwPk5UHPngpuSR4KbhGJuU2bvIlQgjJwoIJbkoeCW0RibtOm4FrcAIMHw5o1wW1fJJoU3CISU855XeVBtriPOQY++UTncktyUHCLSEzV1UFjY7DBfeyx0NwMn34aXA0i0aLgFpGYCvIc7rBjj/WWH34YXA0i0eJ7cJvZADN7zcw+NLMPzOz7ftcgIv4Jz1gWdFc5KLglOWQEsM0W4Hrn3FIzywOWmNlLzjn9lxJJQuEWd5DB3aOHN0Dtgw+Cq0EkWnwPbufcRmBj6H6dma0EygAFt0gSCl8LO8iucvC6y/1ocTe3NlNVX8XGuo1s2rmJnbt30tjSSGNzI00tTWSlZ5GXnUdeVh65Wbn0z+/PkYVH0i2jW+yLk6QQRIu7nZkNBo4HFgVZh4jETnm5NwlKr17B1jFiBLz8MjQ1QbcuZmRzazMf13zMB5s/4JOaT/h026d8uvVTVm9dzeb6zThch9aXZmkMKhjE0cVHM6Z0DCcPPJmTB55MQbeCrhUqScmc69gvWNQ2bJYLzAP+wzn35AFenwXMAhg4cOAJa9eu9blCEYmGc8+FtWvhvfeCreOZZ+D882H+fDj55Mg+45yjsq6SFZtXsLxqeftyZfVKmtua299XllfG0MKhDC0cSv/8/vTN7UvfvL70ye1DfnY+3TO60z2zO90yurG7dTd1u+qo211H3a461u1Yx8c1H3u3LR+zYvMKWtpayEjL4JSBp3D+8PP5ynFfoTQ3gIneJTBmtsQ5N+6ArwUR3GaWCTwLvOCcu/Nw7x83bpxbvHhx7AsTkagbORKOOMILziBt2QIlJfCLX8BPf7r/68451u5Yy+LKxSypXMLijYtZunEpWxu3tr+nLK+MUaWjGNl7JCNLRzKy90iOKjqKnMycqNXZ0NzA2xVv88KnL/DXT/7KB9UfkGZpnD30bH448YdMHTIVM4va9iQ+xVVwm/cb9xCw1Tn3g0g+o+AWSUzOQX4+XH013HVX0NV4x7kHD4bnnnOsr13vBXTlYhZv9MK6prEGgMy0TEaWjuSEvicwqnQUo0pHMaL3CAq7F/pe84fVH/L75b9n7rtz2Vy/mdGlo7lu0nVcMuISstKzfK9H/BFvwX0yMB9YAbSFnr7ROfe3g31GwS2SmMKt3Lvugu8HeOJnQ3MD71S8w4/uWcC7WxbQa8QiqhuqAUi39PaQHtdvHOP6jWNk75FkZwRwKbNDaGpp4g/L/8CdC+/kw+oPGZA/gP+Y+h9cPupy0kxTciSbuAruzlBwiySmd96B8eO9bvJzz/Vnm8451u1Yx1sb3mLB+gUsWL+AZZuW0epavTdsOZpzxkzkrJEnMq7fOEaVjqJ7Znd/iosC5xwvrH6Bf331X1mycQlj+47lv6f/N6cPPj3o0iSKDhXcgY4qF5HkVl7uLQcPju121m5fy2vlr3m3z15jfe16AHIyc5hQNoF/OelfmDxgMkOyJnLckCJOmg3XjI9tTbFiZpw19CymHzmdR1Y8wo2v3MiUh6Zw/vDzuefse+if3z/oEiXG1OIWkZi5/Xb4l3+B2lrvlLBoqayr5LXPXmsP6zXbvEt/FXUvYsqQKZw68FROGngSo0pHkZG2d/tkzBgoLIRXX41ePUFqbG7kroV3cesbt5KZnskdX7iDb4z9hgawJTi1uEUkEGvWQFFR10N7c/1mXi9/vT2sP675GICC7AJOH3w6146/lilDpjCi94jDHu+dPt075l5f782olui6Z3bnp6f8lIuPu5hv/vWbzHp2Fo998Bi//vKvOaLXEUGXJzGgFreIxMzJJ0NaGrzxRsc+t61xG/PWzuPVz17ltfLXeH/z+wDkZuVyysBTmDpkKlMGT2FMnzGkp6V3aN0vvwxf+AI89xx88Ysdqyvetbk25iydww0v3kCra+VXZ/+KmWNmqvWdgNTiFhHfOQcrVsAVVxz+vdubtjN/7XyvVV3+Gss2LcPh6J7RnZMGnsRlIy5jypApnND3BDLTM7tU18knQ/fu8OKLyRfcaZbGrBNmcfbQs/na01/j63/5Oi+ueZH7v3S/ZmFLIgpuEYmJdeu8Y9sjR+7/2p5B/fra13l347s4HNnp2UzsP5GbT7uZqUOmMr5sfNRPy+rWDU47DV54IaqrjSsDCgbw8hUvc9ubt3Hz6zezcMNCHr3wUSb2nxh0aRIFCm4RiYkVK7zlyJGwpWELC9YvYF75vP2CetKASdx82s2cPvh0JvSf4MvFNqZPh+uu875cDBwY880FIj0tnZtOvYmpQ6Zy6Z8v5eTfnMwd0+/g+xO+r67zBKfgFpGoanNtfLTlI36zbAGct4CZixew6iVvMFlQQb2v6dO95UsvebO6JbNJAyax7NvLmPn0TH74wg9ZuGEhc86dQ25WbtClSSdpcJqIdMnO3Tt5u+Lt9slO3trwFtubtgOQ1lTEl0ZNZvIA7za+bHxcXL7SOejf3zve/cc/Bl2NP9pcG7f/43ZuevUmhhcP588X/5nhxcODLksOQjOniUhUOOdYs20Nb214i7fWv8WCDQtYXrWcNufNXnxcyXHtIX3z1yczZuBRPPN0fHbLXnWVN6NbdTWkd2xgekJ7Zc0rXPrnS2lsaeTB8x7kwmMvDLokOQCNKheRTmlsbmRx5eL26UPf2vAWm+s3A96pWRPKJnDjyTdy0sCTmFA2gV7dvYtub9oEV70L37ssyOoP7QtfgAcf9C43OnZs0NX4Z9oR01gyawkXPX4RMx6fwQ2TbmD2GbP3m6hG4pf+pUQE8FrT62vXey3pUEi/u+ldWtpaABhaOJSzhp7FpP6TmDxgMseVHHfQc6jnz/eWkV73OghTpnjLV19NreAGb9T5vJnzuO6F67jjrTtYvHExj134mK75nSDUVS6Sona17OLdTe+2h/SC9QuorKsEoHtGd8aXjW8P6Yn9J1LSoyTidYe7oTdvhow4bh4MHw5HHulNxpKqHn7vYb717Lfo1b0Xj1/0OJMHTA66JEFd5SICbKzbuFeX95LKJexq3QXA4J6DOW3Qae1BPap0VKcnOmlu9oLwzDPjO7TBa3X/4Q/Q0hL/tcbKFaOvYFTpKC7804Wc9uBp3Dn9Tr47/rs6ZSyOpeivqkhya25tZnnV8r1a02t3rAW8U7JO6HcC3xv/PSYNmMSk/pPom9c3att+/nlvwNell0ZtlTEzdSrcfz8sWQITJgRdTXBG9xnN4lmL+dpTX+Pa569lYcVCHjjnAXpkJcFk7klIwS2S4JxzrN2xlkUbFvF2xdssqljE0o1LaWxpBKAsr4zJAybz/QnfZ9KASRzf5/ioz0b2eS1wxx3Qrx+cfXZMNhFVp5/uLV99NbWDG6Bnt548fcnTzJ4/m//32v9jedVynrz4SY4qOiro0mQfOsYtkkCcc2zauYnlVct5p/IdFlV4YR0e6d0toxtj+45lfL/xTOw/kckDJjOgYIBv9T3yCFx+Ofzyl/C97/m22S4ZORL69vXmLhfPi6tf5LI/X0ZzWzMPnf8Q5w8/P+iSUo7O4xZJMM45tjZuZdXWVby/+X2WVy1nxeYVrKhaQU1jTfv7jik+hgn9JzC+33gm9J/AyN4ju3wRjs5av94LwWOP9a4GlijHjK+9FubMge3bISsr6Grix9rta5nx+AwWVy7muonXMfuM2WSl6wfkFw1OE4kzzjlqGmuorKtkY91GNtRuYPW21azetppPt37K6q2r2bFrR/v7e2T2YETvEVww/AJGlo5kZO+RjO07Nm6u+OQcfP3r0NoKDz+cOKEN3nHue+6BRYvglFOCriZ+DOo5iPlXzef6F67nzoV3Mm/tPB698FF1nceBBPrvJRL/nHNsa9pGRW0FG3dupLKusj2cK3dWtj/etHMTu1t37/XZjLQMBvcczNDCoUzqP4kjex3J0MKhHNf7OAb3HEyapQW0V4f36KPeda7vu887vSqRnH6690XjuecU3PvqltGNe790L1848gt8/ZmvM/aBsdz3xfu4YnQE12qVmFFXuUgH1O6qZVXNKtbuWMuG2g1U1FZQUVfh3Q8tm1qa9vtcz2496ZfXj355/eib27f9fvhxWX4Z/fP7J+TsVU1NMGSId5WtBQsSc/rQM8+E1ath1SrQWVAHtn7Hei5/8nLmr5vPV477Cr/64q8ozikOuqykpa5ykQ5oc22s3rqa5VXL+WjLR6zauopVW1fx6dZP2weBhWWlZ1GWV0ZZfhkn9D2B844+r/3xnsHcPbN7QHsTe48/7k1x+oc/JGZoA8yYAbNmwfLlMHp00NXEpwEFA3jtyte47c3b+Nm8n/Fa+Wvc/6X7ueCYC4IuLeWoxS0praG5gaUbl7Js0zKWVy1vHwTW0NzQ/p5+ef04qvAojio8iqGFQzmq6CiG9BxC//z+FOcUp/xEFZMnQ00NfPRR4rZWq6uhTx/40Y/gttuCrib+La9azpVPX8myTcu4dMSl/PLsX6r1HWUaVS6C15JeWb2y/RSqRRWLWFG1glbXCkBh90JGl45mVOkoRpWOYnTpaIYXD9ckFIfw3nswZgzceSf88IdBV9M1F1zgjYZfvx5ycoKuJv41tzYz+83Z3PrGreRn5zN72my+MfYbcT0WI5EouCUlVe2sYlHFIhZuWMiiikW8U/EOdbvrACjILmB82XjGl41nQtkExvYdS7+8finfeu6o73wHfvtbqKiAwsKgq+ma+fPh1FO9mdS+9a2gq0kc729+n2v+dg1vrH2DE/udyL1fvJcTy04MuqyEp+CWpLerZRfLNi1rD+mFGxby2fbPAG+09ujS0e0hPaH/BIYVDVPLoIvq6rwZ0i680Ls8ZqJzzps9raoKVq5Uq7sjnHM8+v6jXP/i9VTtrGLmmJncfNrNDOo5KOjSEpaCW5JKa1srq7etZunGpSzasIiFFQtZunFp++lVA/IHMKH/BCaWTWRi/4mM7Ts2qQeHBeX//g++/W146y2YODHoaqJj3jzv9LB/+zf42c+Cribx1O6q5dZ5t3LP2/fgcHz7hG9z4yk36nKhnaDgloS1rXEbH235iPeq3mPZpmW8V/Uey6uWtw8e657RnXH9xjGxvxfSE8omUJZfFnDVyc85OP54bzDa0qWJOyjtQC69FP78Z1i4MPWu0x0t63es59Y3buU37/6G7IxsrjnxGq6dcC398/sHXVrCUHBL3Gpta6WqvooNtRtYu30tn9R8wqqtq9qXWxq2tL+3Z7eejC4dzZg+Y9qXI3qPCGyKz1S2cCFMmpScx4O3bPFOCcvN9WZT69kz6IoS16qaVdwy7xYee/8x0iyNrxz3Fa6bdB1j++ob0eEouMU3za3NbG3ceshbOKgr6irYWLexfVR3WL+8fgwrGsZRhUcxrGgYw4qGMbp0NAMLBmrwWJy49FJvprGKCsjLC7qa6HvjDZg2zes2f+45zWHeVeXby7ln0T38eumvqdtdx+QBk5k5eiYXHXcRPbvpm9GBKLilw5xz1DfXs6VhywFvNQ011DTW7BfK4VHbB5JmaRR2L6Qkp4Sy/DLK8rzZwsITlgwsGMjQwqHkZuX6uKfSUatXw7BhcP31cPvtQVcTOw8+CFdd5QX4n/6U+KPm48GOph3MfXcuc5bOYeWWlWSnZ3P+8PO5bORlnHHEGeRkakRgmIJb2jnn2NKwhYq6CipqK6isq2y/X1HnPa5uqKamoYZdrbsOuI5wABfnFFPYvfDzW7dCinKK9n5uj1t+dr5GcieBq6+G3/8ePvvMG1WezH73O29/8/Phyith8GBvspaPP/YmnPnsMxgxAq65xrucqTqEIuOcY3HlYn733u945P1H2Nq4lW4Z3Zg2ZBpfHvZlzj7qbAYWDAy6zEDFXXCb2VnA3UA6MMc5d8i5ihTckanfXd9+EYt9AzkcypV1lftd3MIwSnNL26foLO1RSlH3Iopzig94K+hWoABOUa+/DlOmeK3tO+4Iuhp/rFgBN97oXUSlqQnS0rwLqQwf7s3P/sYb3nvOPNML+t69g644sexu3c288nk8+8mz/PWTv7afxjmwYCAnDTiJkwacxOQBkzm25FiyM7IDrtY/cRXcZpYOfAJ8AdgAvANc6pz78GCfSeXgbmppau+WrmmoYXP95r2uOtV+9amdG6ndVbvf53tk9mjvli7LL6Nfbr+9HpflldEnt48GeCWAhgYvPJ59FjZvhrIy75KUZ57pDaQ6nKoqeOIJb2DZjh1e6/GLX/TWEckx3HXr4OSTvfcuX5565znv2gU7d3rH9Pf8ebW1eYP0rr/eG8j2yCPelxvpOOccH235iJfWvMQ/1v+DN9e9SWVdJQDpls6womGMLB3JcSXHcUSvIxjScwhDeg2hT26fpGtMxFtwTwJucc6dGXr8UwDn3OyDfcbv4HbO0ebacLiD3m9zbTjn2u83tzazu3X3YW8NzQ3s3L1z/1uzt9zWuK09pGsaa/aaM3tP2enZ+11haq/HeX0pyysjPztfA7oS0O7dXriuWQOLF8Pzz3/e4isogEGDvG7aujrIzobp0+Gf/gnGjfMCPSPDC5nycvjHP7ywnz/fC5n+/b3jtatXQ329FzYXXABnneWNpu7Tx/si4Jy3/ooKb4DWf/0XNDfDa6/pNKkDWb4cLr7Y60b/p3+Cr33N+3mWlUGmvhd3inOO8u3lvF3xNis2r/BuVSvaW+VhWelZlOSUUNKjhJKcEnr36E1JTgnFOcXkZ+fTI6sHuVm55Gbl0iPTu5+VnkVmeiYZaRlkpoWW6Zl73c9IyyDd0gP5GxpvwT0DOMs5943Q4yuACc657x7sM9EM7lvn3crsN2cfNJgd/v48MtMyycvOa/+lKsguoCiniKLuoVvO3suSHiWU5ZXRs1tPBXISamnxgrlhn+9rgwfDeefBued614zOzPTeu2ABPPUUPPmk1yI+mJEj4fzz4StfgeOO855raoKXXvKu7vXMM1C7f4fNXqZPh7vv9rqI5cB27vS+4Nx9t/fFK2zqVHjlleDqSjaNzY2s3bGWz7Z9xmfbP2Pt9rVUN1RT3VDN5vrNVNd793fu3hnV7RqGme21TLM0zIwxfcbw1tVvRW9biRjcZjYLmBV6eDTw8R4vFwNbSD3a79Si/U49qbrv2u/9DXLOlRzohSCux10BDNjjcf/Qc3txzj0APHCgFZjZ4oN9E0lm2u/Uov1OPam679rvjgniaP47wFFmNsTMsoBLgL8EUIeIiEjC8b3F7ZxrMbPvAi/gnQ72G+fcB37XISIikoiC6CrHOfc34G9dWMUBu9BTgPY7tWi/U0+q7rv2uwMSYuY0ERER8STXGesiIiJJLiGC28wKzewlM1sVWvY6wHvGmNlbZvaBmS03s68EUWs0mNlZZvaxmX1qZj85wOvZZvbH0OuLzGyw/1VGXwT7fZ2ZfRj6933FzAYFUWe0HW6/93jfhWbmzCwpRt9Gst9mdnHo3/wDM3vE7xpjIYLf84Fm9pqZvRv6Xf9iEHVGm5n9xsw2m9n7B3ndzOyXoZ/LcjNLiml+Itjvy0P7u8LMFpjZ6MOu1DkX9zfgduAnofs/Af7zAO8ZBhwVut8P2Aj0DLr2TuxrOrAaOALIAt4Djt3nPd8B7g/dvwT4Y9B1+7TfU4Cc0P1/TpX9Dr0vD3gDWAiMC7pun/69jwLeBXqFHvcOum6f9vsB4J9D948FyoOuO0r7fiowFnj/IK9/Efg7YMBEYFHQNfu035P3+B0/O5L9TogWN3Ae8FDo/kPA+fu+wTn3iXNuVeh+JbAZOODJ63FuPPCpc26Nc2438Bje/u9pz5/HE8A0S/xp1A67386515xz4TnFFuLNAZDoIvn3BrgV+E+gyc/iYiiS/f4mcK9zbhuAc26zzzXGQiT77YD80P0CoNLH+mLGOfcGsPUQbzkP+J3zLAR6mllff6qLncPtt3NuQfh3nAj/riVKcJc65zaG7m8CSg/1ZjMbj/dtdnWsC4uBMmD9Ho83hJ474Huccy3ADqDIl+piJ5L93tPVeN/OE91h9zvUZTjAOfecn4XFWCT/3sOAYWb2DzNbGLqqYKKLZL9vAb5qZhvwzr75nj+lBa6jfwOSUUR/1wI5HexAzOxloM8BXrppzwfOOWdmBx0KH/qG9jBwpXOuLbpVSjwws68C44DTgq4l1swsDbgTmBlwKUHIwOsuPx2vFfKGmY10zm0PtKrYuxR40Dn336GLMj1sZiP09yy5mdkUvOA++XDvjZvgds6dcbDXzKzKzPo65zaGgvmAXWZmlg88B9wU6mpJRJFMCRt+zwYzy8DrTqvxp7yYiWgqXDM7A+/L3GnOuV0+1RZLh9vvPGAE8HroaEgf4C9mdq5zLpGvdRvJv/cGvON9zcBnZvYJXpC/40+JMRHJfl8NnAXgnHvLzLrhzWmdDIcKDiWivwHJyMxGAXOAs51zh/1bnihd5X8BrgzdvxJ4Zt83hKZPfQrvGMkTPtYWbZFMCbvnz2MG8KoLjWxIYIfdbzM7Hvg/4NwkOd4Jh9lv59wO51yxc26wc24w3jGwRA9tiOz3/Gm81jZmVozXdb7GzyJjIJL9XgdMAzCzY4BuQLWvVQbjL8DXQqPLJwI79jhEmrTMbCDwJHCFc+6TiD4U9Ii7CEflFQGvAKuAl4HC0PPjgDmh+18FmoFle9zGBF17J/f3i8AneMfobwo99+94f7DB+4/8OPAp8DZwRNA1+7TfLwNVe/z7/iXomv3Y733e+zpJMKo8wn9vwztM8CGwArgk6Jp92u9jgX/gjThfBkwPuuYo7fejeGf7NOP1plwNfBv49h7/3veGfi4rkuj3/HD7PQfYtsfftcWHW6dmThMREUkgidJVLiIiIii4RUREEoqCW0REJIEouEVERBKIgltERCSBKLhFREQSiIJbREQkgSi4RUREEsj/B7VFhv9O9UUVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQnuPsSLf-RB",
        "outputId": "be84621b-c8f5-4de8-c356-418736529892"
      },
      "source": [
        "sns.kdeplot(y,label=\"\",ax=ax,c='b')"
      ],
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f299466a0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "ZxIDnQTZf-RB",
        "outputId": "7216dce2-d830-47d8-a8cb-7924d32fcdc4"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(8,6))\n",
        "sns.kdeplot(y.ravel(),label=\"\",ax=ax,c='b')\n",
        "# sns.kdeplot(y_test.ravel(),label=\"\",ax=ax,c='g')\n",
        "ax.legend(['Actual','Pred'],fontsize=14)\n",
        "ax.title.set_text('Test_set')"
      ],
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "SZ5OTTOzf-RB",
        "outputId": "14f2b784-f34b-4cc2-ff18-14888f00350c"
      },
      "source": [
        "y.hist()"
      ],
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-298-c33d1d5b2630>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'hist'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0k0Ggvpf-RC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}