2-1-9-FirstNeuralNetwork.ipynb

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      "source": [
        "import sys\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ]
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        "# This script requires TensorFlow 2 and Python 3.\n",
        "if tf.__version__.split('.')[0] != '2':\n",
        "    raise Exception((f\"The script is developed and tested for tensorflow 2. \"\n",
        "                     f\"Current version: {tf.__version__}\"))\n",
        "\n",
        "if sys.version_info.major < 3:\n",
        "    raise Exception((f\"The script is developed and tested for Python 3. \"\n",
        "                     f\"Current version: {sys.version_info.major}\"))"
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        "id": "CLqOZPfC8gfb"
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      "source": [
        "# define a neural network with one neuron\n",
        "# for more information on TF functions see: https://www.tensorflow.org/api_docs\n",
        "model = tf.keras.Sequential([tf.keras.layers.Dense(units=1, input_shape=[1])])\n",
        "\n",
        "# use stochastic gradient descent for optimization and\n",
        "# the mean squared error loss function\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
        "\n",
        "# define some training data (xs as inputs and ys as outputs)\n",
        "xs = np.array([-1.0,  0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
        "ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)\n",
        "\n",
        "# fit the model to the data (aka train the model)\n",
        "model.fit(xs, ys, epochs=500)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qtu5rJBA8o9M"
      },
      "outputs": [],
      "source": [
        "print(model.predict([10.0]))"
      ]
    }
  ],
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