Housing_Price_tf.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Housing Price tf",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yanghuaizhi/tf_learning/blob/master/Housing_Price_tf.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "DSo3XC1t3ISH",
        "colab_type": "code",
        "outputId": "93e9f425-9261-4b5f-9a63-ba02610a215b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34034
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n",
        "model.compile(optimizer='sgd',loss='mean_squared_error')\n",
        "xs =  np.array([ 1,2,3,4,5,6],dtype=int)\n",
        "ys =  np.array([ 1.0,1.5,2.0,2.5,3.0,3.5],dtype=float)\n",
        "model.fit(xs, ys, epochs=1000)\n",
        "print(model.predict([7]))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
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            "Epoch 3/1000\n",
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            "Epoch 4/1000\n",
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            "Epoch 5/1000\n",
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            "Epoch 6/1000\n",
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            "Epoch 7/1000\n",
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            "Epoch 9/1000\n",
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            "Epoch 10/1000\n",
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            "Epoch 992/1000\n",
            "6/6 [==============================] - 0s 377us/sample - loss: 7.1314e-04\n",
            "Epoch 993/1000\n",
            "6/6 [==============================] - 0s 269us/sample - loss: 7.1262e-04\n",
            "Epoch 994/1000\n",
            "6/6 [==============================] - 0s 385us/sample - loss: 7.1210e-04\n",
            "Epoch 995/1000\n",
            "6/6 [==============================] - 0s 302us/sample - loss: 7.1158e-04\n",
            "Epoch 996/1000\n",
            "6/6 [==============================] - 0s 308us/sample - loss: 7.1106e-04\n",
            "Epoch 997/1000\n",
            "6/6 [==============================] - 0s 385us/sample - loss: 7.1054e-04\n",
            "Epoch 998/1000\n",
            "6/6 [==============================] - 0s 309us/sample - loss: 7.1002e-04\n",
            "Epoch 999/1000\n",
            "6/6 [==============================] - 0s 380us/sample - loss: 7.0950e-04\n",
            "Epoch 1000/1000\n",
            "6/6 [==============================] - 0s 374us/sample - loss: 7.0898e-04\n",
            "[[4.038535]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}