RBG_parameter_test.ipynb

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  "metadata": {
    "colab": {
      "name": "RBG function approximation RBG to Y",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/PrashantDandriyal/Neural-Networks-In-Cpp/blob/master/RBG_parameter_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVPxSuY1TTCl",
        "colab_type": "code",
        "outputId": "0a8b73af-75a7-4e28-9d99-d4da83358db7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np\n"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-258-a53980e597a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplt2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'plt2'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "um5aZrPjTVeg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pR0fk57lTYAv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv(\"pallet_train.csv\")\n",
        "x = train.drop('summ',axis=1)\n",
        "y = train.summ \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiEnOdAVRibG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Normalizing\n",
        "MAXX = ((x.max()).max())\n",
        "MAXX = max(MAXX,y.max())\n",
        "\n",
        "x = x/MAXX\n",
        "y = y/MAXX"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqubr8m7lx4y",
        "colab_type": "code",
        "outputId": "b9960bb1-0c87-4ed2-82a3-b84e692dc6d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>0</th>\n",
              "      <td>0.115436</td>\n",
              "      <td>0.133852</td>\n",
              "      <td>0.167258</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.125120</td>\n",
              "      <td>0.126643</td>\n",
              "      <td>0.098904</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.121684</td>\n",
              "      <td>0.140799</td>\n",
              "      <td>0.171286</td>\n",
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              "         rr        bb        gg\n",
              "0  0.115436  0.133852  0.167258\n",
              "1  0.125120  0.126643  0.098904\n",
              "2  0.121684  0.140799  0.171286\n",
              "3  0.084877  0.106004  0.162194\n",
              "4 -0.245338 -0.187749  0.144503"
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          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUOw1tIY1os9",
        "colab_type": "code",
        "outputId": "f0cd375f-320e-4bc6-dda2-3daa974f5f89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "y.head()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.416547\n",
              "1    0.350668\n",
              "2    0.433769\n",
              "3    0.353075\n",
              "4   -0.288584\n",
              "Name: summ, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgrk6lya0Aql",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIZohjGn0EZR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fb38ada2-63d4-41c3-d6cf-4818cb4cad29"
      },
      "source": [
        "#make the x for train and test (also called validation data) \n",
        "xtrain,xtest,ytrain,ytest = train_test_split(x,y,\n",
        "                                             train_size=.35\n",
        "                                             )\n",
        "print(\"Size of train data: \"+ str(xtrain.shape))"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size of train data: (8, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EE_djgHTbeT",
        "colab_type": "code",
        "outputId": "8e6d9394-aada-436a-94cf-ac7c759ac5b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "hidden = tf.keras.layers.Dense(units=10,\n",
        "                               input_shape=[3],\n",
        "                               activation = 'elu'\n",
        "                               )\n",
        "\n",
        "output = tf.keras.layers.Dense(units=1,\n",
        "                               activation = 'linear'\n",
        "                               )\n",
        "\n",
        "model = tf.keras.Sequential([hidden, output])\n",
        "model.compile(loss='mean_squared_error'\n",
        "              )\n",
        "history = model.fit(xtrain,ytrain,\n",
        "                    epochs=1000,\n",
        "                    shuffle= True,\n",
        "                    verbose=False)\n",
        "print(\"Finished training the model\")\n"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished training the model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfRoXxemuPYn",
        "colab_type": "code",
        "outputId": "c3a4043e-523b-4b4a-ffe6-47ecb273603f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "import matplotlib.pyplot as plt1\n",
        "plt1.grid()\n",
        "\n",
        "plt1.xlabel('Epoch Number')\n",
        "plt1.ylabel(\"Loss Magnitude\")\n",
        "plt1.plot(history.history['loss'])"
      ],
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0784ee8a20>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 286
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oO4lf/PEZHn7y+UKHY2YlJG+JQ1I5cB3wLmA6cKmk6a0OuxzYFRFTgWuBa9Ly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\ndYkTh5mZdYkTh5mZdcn/Bzara1CzBzExAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_Kc68gJirlW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make predictions using the test set\n",
        "pred = model.predict(xtest)\n",
        "pred = pred*MAXX"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cu6-wdNh7JzR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_axes = xtest.shape[0]\n",
        "x_axes = range(x_axes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfkX3BUT7oUC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ytestt = ytest*MAXX\n",
        "#ytest"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGVRtWtR7qdH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "28c8665d-dd4e-49bf-c5da-2505eaca63fa"
      },
      "source": [
        "pred = list(pred)\n",
        "table = pd.DataFrame(\n",
        "                      {'Expected': ytestt,\n",
        "                       'Predicted' : pred\n",
        "                      })\n",
        "print(table)\n",
        "table.to_csv('elu_lin.csv')"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Expected     Predicted\n",
            "17  170.50320   [174.40681]\n",
            "20 -277.92718  [-266.20633]\n",
            "0   414.07920   [419.44797]\n",
            "15  350.10360   [354.02982]\n",
            "6   458.89242    [464.4342]\n",
            "2   431.19920    [436.5054]\n",
            "19  274.42896   [279.18784]\n",
            "4  -286.87500   [-269.1825]\n",
            "3   350.98320   [356.54742]\n",
            "22  -94.20716   [-92.96275]\n",
            "18  181.87008   [185.86726]\n",
            "13  -24.13704   [-22.47338]\n",
            "1   348.59062   [353.23596]\n",
            "8   994.07670    [981.7294]\n",
            "11  848.09020   [841.68994]\n",
            "5   383.50054    [389.6225]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuYB9UVAGRB3",
        "colab_type": "code",
        "outputId": "32ea2dc5-d270-4118-e5ea-7e8c719b0f5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        }
      },
      "source": [
        "# Plot outputs\n",
        "import matplotlib.pyplot as plt\n",
        "#Enlarges the plot\n",
        "plt.figure(figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k')\n",
        "\n",
        "plt.grid()\n",
        "plt.title('Result Comparison')\n",
        "\n",
        "#Its important to label the plot (& scatter) right where you draw them\n",
        "plt.scatter(x_axes, ytestt,  color='black', label='Expected')\n",
        "plt.plot(x_axes, pred, color='blue', linewidth=3, label='Predicted Regressor')\n",
        "\n",
        "plt.legend(loc='upper left')   #The order is important\n",
        "plt.show()"
      ],
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0tDS4urqiRo0aGDduHL744gsMHjwYr7zyCgICAnDu3Dl89913WLFiBVxdXTFq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\niEgz/gG2ojjFWrrnEwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60IbTqqRW_45",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}