tensorflow回归预测.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM1z48z+AGljRHM8FmuVvvP"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "IM-fX0-h_47g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhXVg2JaBhJB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_data = np.random.rand(100)\n",
        "y_data = np.array(x_data*3+0.8)\n",
        "#print(x_data)\n",
        "#print(y_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iGwAwjxEuDA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "l_0 = tf.keras.layers.Dense(units = 1, input_shape = [1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNFy9BqyFqgX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential(l_0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFjBDW9RF9qB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss = 'mse', optimizer = tf.keras.optimizers.Adam(0.1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZ7DkQSYGNXH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d78b69a7-39c9-4f5f-da8a-816f607d1be0"
      },
      "source": [
        "history = model.fit(x_data, y_data, epochs = 500, verbose = False)\n",
        "print(\"Finished training the model\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished training the model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UekqsyTCG9sL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "7377314f-6c84-4e03-ecdc-dfbd7058e221"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.xlabel('Epoch Number')\n",
        "plt.ylabel(\"Loss Magnitude\")\n",
        "plt.plot(history.history['loss'])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2c62583550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+SYdLuknS3ZI2SFqZd5uezmlmNqbWgzaHgfdHxFpJ+wJrJK2OiLvzarDPpR4zs1FdH/FH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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYOZJDS-HnFr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "564f4375-dd75-4b3a-ee9c-75f9fcc8b767"
      },
      "source": [
        "print(model.predict([100.0]))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[300.79993]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_G0reO4HuGD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "54c44b2e-e818-4846-aad2-6a60a49c309f"
      },
      "source": [
        "print(\"These are the layer variables: {}\".format(l_0.get_weights()))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "These are the layer variables: [array([[2.9999993]], dtype=float32), array([0.80000037], dtype=float32)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lW05_Hp1QrnH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "c4421cc2-22d5-4ce8-cadf-e1ca7a49c13b"
      },
      "source": [
        "l_0 = tf.keras.layers.Dense(units=4, input_shape=[1])\n",
        "l_1 = tf.keras.layers.Dense(units=4)\n",
        "l_2 = tf.keras.layers.Dense(units=1)\n",
        "model = tf.keras.Sequential([l_0, l_1, l_2])\n",
        "model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(0.1))\n",
        "model.fit(x_data, y_data, epochs=100, verbose=False)\n",
        "print(\"Finished training the model\")\n",
        "print(model.predict([100.0]))\n",
        "print(\"Model predicts that 100 degrees Celsius is: {} degrees Fahrenheit\".format(model.predict([100.0])))\n",
        "print(\"These are the l0 variables: {}\".format(l_0.get_weights()))\n",
        "print(\"These are the l1 variables: {}\".format(l_1.get_weights()))\n",
        "print(\"These are the l2 variables: {}\".format(l_2.get_weights()))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished training the model\n",
            "[[300.79996]]\n",
            "Model predicts that 100 degrees Celsius is: [[300.79996]] degrees Fahrenheit\n",
            "These are the l0 variables: [array([[0.89781815, 0.9848509 , 0.7713721 , 0.9037795 ]], dtype=float32), array([ 0.04485591, -0.04286721, -0.39005074, -0.20019504], dtype=float32)]\n",
            "These are the l1 variables: [array([[ 0.43552428,  0.2602252 , -0.6894142 , -0.73038983],\n",
            "       [-0.46118936,  0.16520326, -1.1155523 ,  0.21018597],\n",
            "       [-0.06475709,  0.87154025, -0.30412441,  0.07943771],\n",
            "       [ 0.35211575,  0.3194765 , -0.23632023, -0.6954093 ]],\n",
            "      dtype=float32), array([-0.02960134,  0.32027358, -0.37746096, -0.35489956], dtype=float32)]\n",
            "These are the l2 variables: [array([[ 0.08018488],\n",
            "       [ 0.42091075],\n",
            "       [-0.9288875 ],\n",
            "       [-0.39415157]], dtype=float32), array([0.5416383], dtype=float32)]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}