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‎samples/core/tutorials/keras/basic_regression.ipynb

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"colab": {
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"name": "basic-regression.ipynb",
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"version": "0.3.2",
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"provenance": [],
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"private_outputs": true,
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"metadata": {
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"id": "AwOEIRJC6Une",
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"metadata": {
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"colab": {}
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"metadata": {
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"metadata": {
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"colab_type": "code",
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"metadata": {
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"colab_type": "code",
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"metadata": {
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"id": "jl_yNr5n1kms",
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"colab_type": "code",
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"metadata": {
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"id": "Xe7RXH3N3CWU",
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"colab_type": "code",
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"colab": {}
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"cell_type": "code",
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"source": [
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"test_predictions = model.predict(test_data).flatten()\n",
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"\n",
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"print(test_predictions)"
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"plt.scatter(test_labels, test_predictions)\n",
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"plt.xlabel('True Values [1000$]')\n",
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"plt.ylabel('Predictions [1000$]')\n",
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"plt.axis('equal')\n",
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"plt.xlim(plt.xlim())\n",
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"plt.ylim(plt.ylim())\n",
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"_ = plt.plot([-100, 100],[-100,100])\n"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"metadata": {
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"id": "f-OHX4DiXd8x",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"error = test_predictions - test_labels\n",
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"plt.hist(error, bins = 50)\n",
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"plt.xlabel(\"Prediction Error [1000$]\")\n",
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"_ = plt.ylabel(\"Count\")"
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],
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"execution_count": 0,
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"outputs": []

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