1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning/Week 1/Programming assignment/Excercise1.ipynb

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   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Exercises/Exercise%201%20-%20House%20Prices/Exercise_1_House_Prices_Question.ipynb)"
   ]
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  {
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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": 1,
   "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, 2, 3, 4, 5, 6], dtype=float)\n",
    "    ys = np.array([1, 1.5, 2.0, 2.5, 3.0, 3.5], dtype=float)\n",
    "    model = tf.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": 9,
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     ]
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     ]
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      "Epoch 450/500\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 0.0013\n",
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      "Epoch 458/500\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 0.0013\n",
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      "Epoch 467/500\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 0.0012\n",
      "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 472/500\n",
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      "Epoch 474/500\n",
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      "Epoch 475/500\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 0.0011\n",
      "Epoch 476/500\n",
      "6/6 [==============================] - 0s 318us/sample - loss: 0.0011\n",
      "Epoch 477/500\n",
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      "Epoch 478/500\n",
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      "Epoch 479/500\n",
      "6/6 [==============================] - 0s 277us/sample - loss: 0.0011\n",
      "Epoch 480/500\n",
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      "Epoch 481/500\n",
      "6/6 [==============================] - 0s 295us/sample - loss: 0.0011\n",
      "Epoch 482/500\n",
      "6/6 [==============================] - 0s 253us/sample - loss: 0.0011\n",
      "Epoch 483/500\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 0.0011\n",
      "Epoch 484/500\n",
      "6/6 [==============================] - 0s 296us/sample - loss: 0.0010\n",
      "Epoch 485/500\n",
      "6/6 [==============================] - 0s 221us/sample - loss: 0.0010\n",
      "Epoch 486/500\n",
      "6/6 [==============================] - 0s 287us/sample - loss: 0.0010\n",
      "Epoch 487/500\n",
      "6/6 [==============================] - 0s 235us/sample - loss: 0.0010\n",
      "Epoch 488/500\n",
      "6/6 [==============================] - 0s 203us/sample - loss: 0.0010\n",
      "Epoch 489/500\n",
      "6/6 [==============================] - 0s 269us/sample - loss: 0.0010\n",
      "Epoch 490/500\n",
      "6/6 [==============================] - 0s 210us/sample - loss: 0.0010\n",
      "Epoch 491/500\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 9.9753e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 492/500\n",
      "6/6 [==============================] - 0s 232us/sample - loss: 9.9026e-04\n",
      "Epoch 493/500\n",
      "6/6 [==============================] - 0s 231us/sample - loss: 9.8305e-04\n",
      "Epoch 494/500\n",
      "6/6 [==============================] - 0s 215us/sample - loss: 9.7589e-04\n",
      "Epoch 495/500\n",
      "6/6 [==============================] - 0s 261us/sample - loss: 9.6878e-04\n",
      "Epoch 496/500\n",
      "6/6 [==============================] - 0s 248us/sample - loss: 9.6172e-04\n",
      "Epoch 497/500\n",
      "6/6 [==============================] - 0s 195us/sample - loss: 9.5471e-04\n",
      "Epoch 498/500\n",
      "6/6 [==============================] - 0s 259us/sample - loss: 9.4776e-04\n",
      "Epoch 499/500\n",
      "6/6 [==============================] - 0s 233us/sample - loss: 9.4085e-04\n",
      "Epoch 500/500\n",
      "6/6 [==============================] - 0s 14ms/sample - loss: 9.3400e-04\n",
      "[4.0440826]\n"
     ]
    }
   ],
   "source": [
    "prediction = house_model([7.0])\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [],
   "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
}