Course 1/Week2_Assignment.ipynb

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    "# W2 Assignment: Creating a Custom Loss Function"
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   "source": [
    "This short exercise will require you to write a simple linear regression neural network that is trained on two arrays: $xs$ (inputs) and $ys$ (labels), where the relationship between each corresponding element is $y=2x-1$.\n",
    "\n",
    "\n",
    "$xs = [-1.0,  0.0, 1.0, 2.0, 3.0, 4.0]$\n",
    "\n",
    "$ys = [-3.0, -1.0, 1.0, 3.0, 5.0, 7.0]$\n",
    "\n",
    "\n",
    "You will need to implement a custom loss function that returns the root mean square error (RMSE) of $y_{true} - y_{pred}$. Let's begin!"
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   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "import utils"
   ]
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    "# inputs\n",
    "xs = np.array([-1.0,  0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
    "\n",
    "# labels. relationship with the inputs above is y=2x-1.\n",
    "ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)"
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    "### Define the custom loss function (TODO)\n",
    "Define the custom loss function below called `my_rmse()` that returns the RMSE between the target (`y_true`) and prediction (`y_pred`). \n",
    "\n",
    "You will return $\\sqrt{error}$, where $error$ = $mean((y_{true} - y_{pred})^2)$\n",
    "- error: the difference between the true label and predicted label.\n",
    "- sqr_error: the square of the error.\n",
    "- mean_sqr_error: the mean of the square of the error\n",
    "- sqrt_mean_sqr_error: the square root of hte mean of the square of the error (the root mean squared error).\n",
    "- Please use `K.mean`, `K.square`, and `K.sqrt`\n",
    "- The steps are broken down into separate lines of code for clarity.  Feel free to combine them, and just remember to return the root mean squared error."
   ]
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    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "def my_rmse(y_true, y_pred):\n",
    "    error = y_true - y_pred\n",
    "    sqr_error = K.square(error)\n",
    "    mean_sqr_error = K.mean(sqr_error)\n",
    "    sqrt_mean_sqr_error = K.sqrt(mean_sqr_error)\n",
    "    return sqrt_mean_sqr_error"
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     "name": "stdout",
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     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
   ],
   "source": [
    "utils.test_my_rmse(my_rmse)\n"
   ]
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   "source": [
    "### Define a model using the custom loss function (TODO)\n",
    "Similar to the ungraded labs, you will define a simple model and pass the function you just coded as the loss.\n",
    "- When compiling the model, you'll choose the `sgd` optimizer and set the `loss` parameter to the custom loss function that you just defined.\n",
    "- For grading purposes, please leave the other parameter values as is."
   ]
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      "[[19.094328]]\n"
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   "source": [
    "# Please uncomment all lines in this cell and replace those marked with `# YOUR CODE HERE`.\n",
    "# You can select all lines in this code cell with Ctrl+A (Windows/Linux) or Cmd+A (Mac), then press Ctrl+/ (Windows/Linux) or Cmd+/ (Mac) to uncomment.\n",
    "\n",
    "\n",
    "\n",
    "# define the model architecture\n",
    "model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n",
    "\n",
    "# use the function you just coded as the loss\n",
    "model.compile(optimizer='sgd', loss=my_rmse)\n",
    "              \n",
    "# train the model \n",
    "model.fit(xs, ys, epochs=500,verbose=0)\n",
    "              \n",
    "# test with a sample input\n",
    "print(model.predict([10.0]))"
   ]
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     "text": [
      "\u001b[92m All public tests passed\n"
     ]
    }
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   "source": [
    "utils.test_model_loss(model.loss)\n"
   ]
  },
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