Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 1 - A New Programming Paradigm/Exercise_1_House_Prices_Question.ipynb

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   "cell_type": "markdown",
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    "id": "mw2VBrBcgvGa"
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   "source": [
    "In this exercise you'll try to build a neural network that predicts the price of a house according to a simple formula.\n",
    "\n",
    "So, imagine if house pricing was as easy as a house costs 50k + 50k per bedroom, so that a 1 bedroom house costs 100k, a 2 bedroom house costs 150k etc.\n",
    "\n",
    "How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc.\n",
    "\n",
    "Hint: Your network might work better if you scale the house price down. You don't have to give the answer 400...it might be better to create something that predicts the number 4, and then your answer is in the 'hundreds of thousands' etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PUNO2E6SeURH"
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: house_model\n",
    "def house_model(y_new):\n",
    "    xs = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 10.0], dtype=float)\n",
    "    ys = np.array([1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 5.5], dtype=float)\n",
    "    model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n",
    "    model.compile(optimizer='sgd', loss='mean_squared_error')\n",
    "    model.fit(xs, ys, epochs=500)\n",
    "    return model.predict(y_new)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     ]
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      "Epoch 401/500\n",
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      "Epoch 406/500\n",
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      "Epoch 407/500\n",
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      "Epoch 408/500\n",
      "7/7 [==============================] - 0s 182us/sample - loss: 8.9310e-04\n",
      "Epoch 409/500\n",
      "7/7 [==============================] - 0s 201us/sample - loss: 8.8334e-04\n",
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      "Epoch 411/500\n",
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      "Epoch 412/500\n",
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      "Epoch 413/500\n",
      "7/7 [==============================] - 0s 12ms/sample - loss: 8.4536e-04\n",
      "Epoch 414/500\n",
      "7/7 [==============================] - 0s 212us/sample - loss: 8.3612e-04\n",
      "Epoch 415/500\n",
      "7/7 [==============================] - 0s 248us/sample - loss: 8.2698e-04\n",
      "Epoch 416/500\n",
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      "Epoch 417/500\n",
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      "Epoch 418/500\n",
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      "Epoch 419/500\n",
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      "Epoch 420/500\n",
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      "7/7 [==============================] - 0s 211us/sample - loss: 7.4912e-04\n",
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      "7/7 [==============================] - 0s 154us/sample - loss: 7.1691e-04\n",
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      "7/7 [==============================] - 0s 173us/sample - loss: 7.0133e-04\n",
      "Epoch 431/500\n",
      "7/7 [==============================] - 0s 187us/sample - loss: 6.9366e-04\n",
      "Epoch 432/500\n",
      "7/7 [==============================] - 0s 168us/sample - loss: 6.8608e-04\n",
      "Epoch 433/500\n",
      "7/7 [==============================] - 0s 182us/sample - loss: 6.7858e-04\n",
      "Epoch 434/500\n",
      "7/7 [==============================] - 0s 221us/sample - loss: 6.7117e-04\n",
      "Epoch 435/500\n",
      "7/7 [==============================] - 0s 173us/sample - loss: 6.6383e-04\n",
      "Epoch 436/500\n",
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      "Epoch 437/500\n",
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      "Epoch 438/500\n",
      "7/7 [==============================] - 0s 169us/sample - loss: 6.4231e-04\n",
      "Epoch 439/500\n",
      "7/7 [==============================] - 0s 187us/sample - loss: 6.3529e-04\n",
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      "Epoch 441/500\n",
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      "Epoch 442/500\n",
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      "Epoch 443/500\n",
      "7/7 [==============================] - 0s 201us/sample - loss: 6.0797e-04\n",
      "Epoch 444/500\n",
      "7/7 [==============================] - 0s 12ms/sample - loss: 6.0133e-04\n",
      "Epoch 445/500\n",
      "7/7 [==============================] - 0s 198us/sample - loss: 5.9476e-04\n",
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      "Epoch 453/500\n",
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      "Epoch 454/500\n",
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      "Epoch 455/500\n",
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      "Epoch 456/500\n",
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      "Epoch 457/500\n",
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      "Epoch 458/500\n",
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      "Epoch 459/500\n",
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      "Epoch 460/500\n",
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      "Epoch 461/500\n",
      "7/7 [==============================] - 0s 166us/sample - loss: 4.9888e-04\n",
      "Epoch 462/500\n",
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      "Epoch 465/500\n",
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      "Epoch 466/500\n",
      "7/7 [==============================] - 0s 189us/sample - loss: 4.7221e-04\n",
      "Epoch 467/500\n",
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      "Epoch 468/500\n",
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      "Epoch 469/500\n",
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      "Epoch 470/500\n",
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      "Epoch 471/500\n",
      "7/7 [==============================] - 0s 181us/sample - loss: 4.4697e-04\n",
      "Epoch 472/500\n",
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      "Epoch 473/500\n",
      "7/7 [==============================] - 0s 186us/sample - loss: 4.3725e-04\n",
      "Epoch 474/500\n",
      "7/7 [==============================] - 0s 12ms/sample - loss: 4.3247e-04\n",
      "Epoch 475/500\n",
      "7/7 [==============================] - 0s 200us/sample - loss: 4.2775e-04\n",
      "Epoch 476/500\n",
      "7/7 [==============================] - 0s 174us/sample - loss: 4.2307e-04\n",
      "Epoch 477/500\n",
      "7/7 [==============================] - 0s 201us/sample - loss: 4.1845e-04\n",
      "Epoch 478/500\n",
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      "Epoch 479/500\n",
      "7/7 [==============================] - 0s 161us/sample - loss: 4.0935e-04\n",
      "Epoch 480/500\n",
      "7/7 [==============================] - 0s 184us/sample - loss: 4.0488e-04\n",
      "Epoch 481/500\n",
      "7/7 [==============================] - 0s 167us/sample - loss: 4.0046e-04\n",
      "Epoch 482/500\n",
      "7/7 [==============================] - 0s 188us/sample - loss: 3.9608e-04\n",
      "Epoch 483/500\n",
      "7/7 [==============================] - 0s 195us/sample - loss: 3.9175e-04\n",
      "Epoch 484/500\n",
      "7/7 [==============================] - 0s 154us/sample - loss: 3.8747e-04\n",
      "Epoch 485/500\n",
      "7/7 [==============================] - 0s 199us/sample - loss: 3.8324e-04\n",
      "Epoch 486/500\n",
      "7/7 [==============================] - 0s 11ms/sample - loss: 3.7905e-04\n",
      "Epoch 487/500\n",
      "7/7 [==============================] - 0s 224us/sample - loss: 3.7491e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 488/500\n",
      "7/7 [==============================] - 0s 207us/sample - loss: 3.7081e-04\n",
      "Epoch 489/500\n",
      "7/7 [==============================] - 0s 205us/sample - loss: 3.6676e-04\n",
      "Epoch 490/500\n",
      "7/7 [==============================] - 0s 165us/sample - loss: 3.6275e-04\n",
      "Epoch 491/500\n",
      "7/7 [==============================] - 0s 181us/sample - loss: 3.5879e-04\n",
      "Epoch 492/500\n",
      "7/7 [==============================] - 0s 204us/sample - loss: 3.5487e-04\n",
      "Epoch 493/500\n",
      "7/7 [==============================] - 0s 175us/sample - loss: 3.5099e-04\n",
      "Epoch 494/500\n",
      "7/7 [==============================] - 0s 201us/sample - loss: 3.4715e-04\n",
      "Epoch 495/500\n",
      "7/7 [==============================] - 0s 201us/sample - loss: 3.4336e-04\n",
      "Epoch 496/500\n",
      "7/7 [==============================] - 0s 221us/sample - loss: 3.3961e-04\n",
      "Epoch 497/500\n",
      "7/7 [==============================] - 0s 217us/sample - loss: 3.3590e-04\n",
      "Epoch 498/500\n",
      "7/7 [==============================] - 0s 186us/sample - loss: 3.3223e-04\n",
      "Epoch 499/500\n",
      "7/7 [==============================] - 0s 175us/sample - loss: 3.2860e-04\n",
      "Epoch 500/500\n",
      "7/7 [==============================] - 0s 149us/sample - loss: 3.2500e-04\n",
      "4.004991\n"
     ]
    }
   ],
   "source": [
    "prediction = house_model([7.0])\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now click the 'Submit Assignment' button above.\n",
    "# Once that is complete, please run the following two cells to save your work and close the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "<!-- Save the notebook -->\n",
    "IPython.notebook.save_checkpoint();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.session.delete();\n",
       "window.onbeforeunload = null\n",
       "setTimeout(function() { window.close(); }, 1000);\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.notebook.session.delete();\n",
    "window.onbeforeunload = null\n",
    "setTimeout(function() { window.close(); }, 1000);"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "introduction-tensorflow",
   "graded_item_id": "jRjw8",
   "launcher_item_id": "YoQKl"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}