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    "# Assignment 4\n",
    "\n",
    "\n",
    "- **Zhilong Li**\n",
    "- **USC ID:** 3884-8441-02\n",
    "- **USC email:** zhilongl@usc.edu"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Plugin\n",
      "Init Graph Optimizer\n",
      "Init Kernel\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-02T03:23:58.323626Z",
     "start_time": "2021-10-02T03:23:58.316529Z"
    }
   },
   "outputs": [],
   "source": [
    "global_random_seed = 3884 # np.random.randint(0, 99999)\n",
    "np.random.seed(global_random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "end_time": "2021-10-02T03:24:01.030972Z",
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   "source": [
    "def get_model(input_dim, output_dim, width, depth, regularization_param,\n",
    "                act_func='tanh', output_act_func=False, random_seed=None):\n",
    "    \"\"\"Construct a sequential model based on the input configuration\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    input_dim : int\n",
    "    output_dim : int\n",
    "    width : int\n",
    "    depth : int\n",
    "    regularization_param : float\n",
    "        Number of hidden layers + output layer\n",
    "    act_func : str, optional\n",
    "        Which activation function this model will be using, by default 'tanh'\n",
    "    output_act_func : bool, optional\n",
    "        Should the output layer use an activation function, by default True\n",
    "    random_seed : int\n",
    "        Seed to initialize bias and weights of the network\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    keras.Sequential\n",
    "        The constructed model based on the input arguments\n",
    "    \"\"\"\n",
    "    initializer = keras.initializers.RandomNormal(seed=random_seed)\n",
    "    regularizer = keras.regularizers.l2(regularization_param)\n",
    "\n",
    "    if act_func == 'sin':\n",
    "        act_func = tf.math.sin\n",
    "\n",
    "    all_layers = [keras.layers.InputLayer(input_shape=[input_dim])]\n",
    "    all_layers += [keras.layers.Dense(width, activation=act_func,\n",
    "                                         kernel_initializer=initializer,\n",
    "                                         bias_initializer=initializer,\n",
    "                                         kernel_regularizer=regularizer,\n",
    "                                         bias_regularizer=regularizer) for _ in range(depth-1)]\n",
    "    all_layers.append(keras.layers.Dense(\n",
    "        output_dim, kernel_initializer=initializer,\n",
    "        activation=act_func if output_act_func else None,\n",
    "        bias_initializer=initializer,\n",
    "        kernel_regularizer=regularizer,\n",
    "        bias_regularizer=regularizer))\n",
    "    m = keras.Sequential(all_layers)\n",
    "    return m"
   ]
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     "start_time": "2021-10-02T03:24:06.306974Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-10-01 20:24:06.336225: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-10-01 20:24:06.336513: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "model = get_model(1,1,20,5,1.0e-7,act_func='sin',output_act_func=False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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