Simple neural network example.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dpanagop/ML_and_AI_examples/blob/master/Simple%20neural%20network%20example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cowX9NMVcf0V",
        "colab_type": "text"
      },
      "source": [
        "#Simple neural network example "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oh-dLc9TdApN",
        "colab_type": "text"
      },
      "source": [
        "###First, we load the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVBEHrxTc0EQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSgP4smidKoK",
        "colab_type": "text"
      },
      "source": [
        "###Then, we create a simple neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1gTL06IegTe",
        "colab_type": "code",
        "outputId": "9b7af300-e2fa-43ca-b99f-ae6bfd932e34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "model=keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n",
        "model.add(keras.layers.Dense(1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0716 11:11:20.355340 140689434380160 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUHRqW64ewY3",
        "colab_type": "text"
      },
      "source": [
        "###Next, compile the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gltf5zKeeswG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='sgd', loss='mean_squared_error')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMW3iNWqe5dw",
        "colab_type": "text"
      },
      "source": [
        "###Create \n",
        "1. input data, variable x\n",
        "2. target data, variable y\n",
        "\n",
        "Note that: $y=2x-1$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtH9Nwj3e1_8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=np.array([-1, 0,1,2,3,4],dtype=float)\n",
        "y=np.array([-3,-1,1,3,5,7],dtype=float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lnw1qLqWfVFb",
        "colab_type": "text"
      },
      "source": [
        "###Train model"
      ]
    },
    {
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      "metadata": {
        "id": "TD1rWVBBfYY0",
        "colab_type": "code",
        "outputId": "8802dd8f-3bf0-4f9f-c787-b3206f2d66ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(x,y,epochs=500)"
      ],
      "execution_count": 0,
      "outputs": [
        {
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            "Epoch 490/500\n",
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            "Epoch 491/500\n",
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            "Epoch 492/500\n",
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            "Epoch 493/500\n",
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            "Epoch 494/500\n",
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            "Epoch 495/500\n",
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            "Epoch 496/500\n",
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            "Epoch 497/500\n",
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            "Epoch 498/500\n",
            "6/6 [==============================] - 0s 209us/sample - loss: 7.0166e-13\n",
            "Epoch 499/500\n",
            "6/6 [==============================] - 0s 227us/sample - loss: 7.0166e-13\n",
            "Epoch 500/500\n",
            "6/6 [==============================] - 0s 313us/sample - loss: 7.0166e-13\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff495cedcc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nVCCCLkjfeM7",
        "colab_type": "text"
      },
      "source": [
        "###Use model to predict responce with $x=3.5$ as input.\n",
        "###Based on formula $y=2x-1$ the predicted value should be close to 6."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cb6sV2vdfpZM",
        "colab_type": "code",
        "outputId": "7c9199ca-7ff6-4b2b-ec5d-f280ba22d97c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(model.predict([3.5]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.9999995]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXNvJTWgf8o8",
        "colab_type": "text"
      },
      "source": [
        "###We can view a summary of the model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erbBOBL8gCYa",
        "colab_type": "code",
        "outputId": "81c64390-c155-43f1-8b0c-95837aa8493c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 1)                 2         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 4\n",
            "Trainable params: 4\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}