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Files/AI/Models/Prediction_with_keras.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zTKyKPBvAGXt"
+      },
+      "outputs": [],
+      "source": [
+        "from keras.models import Sequential\n",
+        "from keras.layers import Dense\n",
+        "import keras\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from keras.models import load_model\n",
+        "from keras.layers import Dropout\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import matplotlib.pyplot as plt\n",
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "path = \"/content/wd.csv\"\n",
+        "df = pd.read_csv(path,sep=\",\")"
+      ],
+      "metadata": {
+        "id": "GgwnEH8cAZ-X"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop(labels=[\"Unnamed: 0\"],axis=1,inplace=True)"
+      ],
+      "metadata": {
+        "id": "7o5tKojYq-8i"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = df.iloc[:-1,1:].values\n",
+        "data = np.transpose(data)\n",
+        "total = df.iloc[-1][1:].values\n",
+        "X = np.asarray(data).astype(np.float32)\n",
+        "Y = np.asarray(total).astype(np.float32)"
+      ],
+      "metadata": {
+        "id": "B2V80bNlAhCO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(data, total, test_size = 1/5, random_state = 123, shuffle=1)\n",
+        "X = np.asarray(X_train).astype(np.float32)\n",
+        "Y = np.asarray(y_train).astype(np.float32)\n",
+        "X_test = np.asarray(X_test).astype(np.float32)\n",
+        "Y_test = np.asarray(y_test).astype(np.float32)"
+      ],
+      "metadata": {
+        "id": "lplZPLbdAsKP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Dense(512, activation='relu'))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(256, activation='relu'))\n",
+        "model.add(Dropout(0.25))\n",
+        "model.add(Dense(1))\n",
+        "model.compile(optimizer='Adam',\n",
+        "              loss='mean_absolute_error',\n",
+        "              metrics=['RootMeanSquaredError'])\n",
+        "history = model.fit(X,Y,\n",
+        "          batch_size=16,\n",
+        "          epochs=5000,\n",
+        "          verbose=1)\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "id": "SVa_xfZeEJk-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predict = model.predict(X)\n",
+        "for i in range(len(X)):\n",
+        "  print(int(abs(predict[i]-Y[i])))"
+      ],
+      "metadata": {
+        "id": "cjiiE0kS6aob"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'])\n",
+        "#plt.plot(history.history['val_loss'])\n",
+        "plt.title('Mean Absolute Error')\n",
+        "plt.ylabel('loss')\n",
+        "plt.xlabel('epoch')\n",
+        "plt.legend(['train', 'test'], loc='upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "WisGZSpFJyHr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#model = keras.models.load_model('path/to/location.keras')"
+      ],
+      "metadata": {
+        "id": "WLCiSMyIH5xG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save('model.keras')\n",
+        "def convert(model):\n",
+        "    converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+        "    tflite_model = converter.convert()\n",
+        "\n",
+        "    with open('app_model.tflite', 'wb') as f:\n",
+        "        f.write(tflite_model)\n",
+        "convert(model)"
+      ],
+      "metadata": {
+        "id": "Yp-jEIQ-PeeG"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}