{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 5.2 - Solution\n",
    "## Interpolation\n",
    "In this task, we implement a simple NN to learn a complicated function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "layers = keras.layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generation of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def some_complicated_function(x):\n",
    "    return (\n",
    "        (np.abs(x)) ** 0.5\n",
    "        + 0.1 * x\n",
    "        + 0.01 * x ** 2\n",
    "        + 1\n",
    "        - np.sin(x)\n",
    "        + 0.5 * np.exp(x / 10.0)\n",
    "        ) / (0.5 + np.abs(np.cos(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's simulate the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_train = 10 ** 4  # number of training samples\n",
    "# Note: \"[:, np.newaxis]\" reshapes array to (N,1) as required by our DNN (we input one feature per sample)\n",
    "xtrain = np.random.uniform(-10, 10, N_train)[:, np.newaxis]\n",
    "ytrain = some_complicated_function(xtrain) + np.random.randn(xtrain.shape[0])  # train data includes some noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain.shape (10000, 1)\n",
      "ytrain.shape (10000, 10000)\n"
     ]
    }
   ],
   "source": [
    "print(\"xtrain.shape\", xtrain.shape)\n",
    "print(\"ytrain.shape\", ytrain.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulate test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_test = 10000  # number of testing samples\n",
    "xtest = np.linspace(-10, 10, N_test)\n",
    "ytest = some_complicated_function(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtest.shape (10000,)\n",
      "ytest.shape (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"xtest.shape\", xtest.shape)\n",
    "print(\"ytest.shape\", ytest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Model\n",
    "\n",
    "In this case, we make use of a simple network with 5 layers. As activation function the ReLU is used. We further add parameter norm penalties (L1 and L2) as regularization strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"1Dfit\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32)                64        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 3,265\n",
      "Trainable params: 3,265\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "nb_nodes = 32\n",
    "nb_layers = 4\n",
    "activation = \"relu\"\n",
    "reg_strategy = keras.regularizers.l1_l2(l1=0.01, l2=0.01)  # use L1 and L2 regularization\n",
    "\n",
    "model = keras.models.Sequential(name=\"1Dfit\")\n",
    "model.add(layers.Dense(nb_nodes, activation=activation, kernel_regularizer=reg_strategy,\n",
    "                       input_dim=xtrain.shape[1]))\n",
    "\n",
    "for i in range(nb_layers - 1):\n",
    "    model.add(layers.Dense(nb_nodes, activation=activation, kernel_regularizer=reg_strategy))\n",
    "\n",
    "model.add(layers.Dense(1))\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the model (set an objective and choose an optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "adam = keras.optimizers.Adam(lr=0.001)  # learning rate = 0.001\n",
    "model.compile(loss=\"MeanSquaredError\", optimizer=adam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 15.2431\n",
      "Epoch 2/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 6.8571\n",
      "Epoch 3/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 5.8455\n",
      "Epoch 4/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 5.3997\n",
      "Epoch 5/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 5.0038\n",
      "Epoch 6/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 4.6660\n",
      "Epoch 7/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 4.5469\n",
      "Epoch 8/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 4.3841\n",
      "Epoch 9/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 4.2089\n",
      "Epoch 10/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 4.0169\n",
      "Epoch 11/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.9143\n",
      "Epoch 12/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.7840\n",
      "Epoch 13/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.8204\n",
      "Epoch 14/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.6407\n",
      "Epoch 15/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.5697\n",
      "Epoch 16/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.5290\n",
      "Epoch 17/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.5095\n",
      "Epoch 18/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.4352\n",
      "Epoch 19/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.3327\n",
      "Epoch 20/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.3706\n",
      "Epoch 21/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.3128\n",
      "Epoch 22/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.3145\n",
      "Epoch 23/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.2287\n",
      "Epoch 24/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.1591\n",
      "Epoch 25/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.2557\n",
      "Epoch 26/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.2274\n",
      "Epoch 27/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.1732\n",
      "Epoch 28/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.1343\n",
      "Epoch 29/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.1498\n",
      "Epoch 30/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.1352\n",
      "Epoch 31/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.0498\n",
      "Epoch 32/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.0276\n",
      "Epoch 33/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.0969\n",
      "Epoch 34/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.1399\n",
      "Epoch 35/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.0425\n",
      "Epoch 36/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 3.1180\n",
      "Epoch 37/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 3.1092\n",
      "Epoch 38/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.0495\n",
      "Epoch 39/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9993\n",
      "Epoch 40/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9800\n",
      "Epoch 41/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.0281\n",
      "Epoch 42/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9979\n",
      "Epoch 43/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9808\n",
      "Epoch 44/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 3.0271\n",
      "Epoch 45/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9723\n",
      "Epoch 46/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 2.9811\n",
      "Epoch 47/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9228\n",
      "Epoch 48/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9430\n",
      "Epoch 49/100\n",
      "157/157 [==============================] - 1s 5ms/step - loss: 2.9944\n",
      "Epoch 50/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8671\n",
      "Epoch 51/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8856\n",
      "Epoch 52/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9121\n",
      "Epoch 53/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9119\n",
      "Epoch 54/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8628\n",
      "Epoch 55/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8097\n",
      "Epoch 56/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.9499\n",
      "Epoch 57/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8291\n",
      "Epoch 58/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8037\n",
      "Epoch 59/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8379\n",
      "Epoch 60/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8073\n",
      "Epoch 61/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.8789\n",
      "Epoch 62/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.7798\n",
      "Epoch 63/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.7955\n",
      "Epoch 64/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.6759\n",
      "Epoch 65/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.7420\n",
      "Epoch 66/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.6766\n",
      "Epoch 67/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.6872\n",
      "Epoch 68/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.6192\n",
      "Epoch 69/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.4959\n",
      "Epoch 70/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.4935\n",
      "Epoch 71/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.4498\n",
      "Epoch 72/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.3763\n",
      "Epoch 73/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.3874\n",
      "Epoch 74/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.3980\n",
      "Epoch 75/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.3195\n",
      "Epoch 76/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.2619\n",
      "Epoch 77/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.2884\n",
      "Epoch 78/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.2914\n",
      "Epoch 79/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.2354\n",
      "Epoch 80/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.2463\n",
      "Epoch 81/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1911\n",
      "Epoch 82/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1427\n",
      "Epoch 83/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1706\n",
      "Epoch 84/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1064\n",
      "Epoch 85/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.0968\n",
      "Epoch 86/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1388\n",
      "Epoch 87/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1235\n",
      "Epoch 88/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1044\n",
      "Epoch 89/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1308\n",
      "Epoch 90/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.0616\n",
      "Epoch 91/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.0945\n",
      "Epoch 92/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.0894\n",
      "Epoch 93/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.1128\n",
      "Epoch 94/100\n",
      "157/157 [==============================] - 1s 3ms/step - loss: 2.0994\n",
      "Epoch 95/100\n",
      "157/157 [==============================] - 1s 3ms/step - loss: 2.0892\n",
      "Epoch 96/100\n",
      "157/157 [==============================] - 1s 3ms/step - loss: 2.0684\n",
      "Epoch 97/100\n",
      "157/157 [==============================] - 1s 3ms/step - loss: 2.0471\n",
      "Epoch 98/100\n",
      "157/157 [==============================] - 1s 3ms/step - loss: 2.0347\n",
      "Epoch 99/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.0641\n",
      "Epoch 100/100\n",
      "157/157 [==============================] - 1s 4ms/step - loss: 2.0764\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "save_period = 20  # after how many epochs the model should be saved?\n",
    "\n",
    "chkpnt_saver = keras.callbacks.ModelCheckpoint(\"weights-{epoch:02d}.hdf5\", save_weights_only=True, save_freq=save_period)\n",
    "\n",
    "results = model.fit(\n",
    "    xtrain,\n",
    "    ytrain,\n",
    "    batch_size=64,\n",
    "    epochs=epochs,\n",
    "    verbose=1,\n",
    "    callbacks=[chkpnt_saver]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the performance of the model during the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(12, 8))\n",
    "\n",
    "ax1.plot(xtest, ytest, color=\"black\", label=\"data\")\n",
    "saved_epochs = range(save_period, epochs + 1, save_period)\n",
    "\n",
    "colors = [plt.cm.jet((i + 1) / float(len(saved_epochs) + 1)) for i in range(len(saved_epochs))]\n",
    "\n",
    "for i, epoch in enumerate(saved_epochs):\n",
    "    model.load_weights(\"weights-{epoch:02d}.hdf5\".format(epoch=epoch))\n",
    "    ypredict = model.predict(xtest).squeeze()\n",
    "    ax1.plot(xtest.squeeze(), ypredict, color=colors[i], label=epoch)\n",
    "    ax2.plot(epoch, results.history[\"loss\"][epoch - 1], color=colors[i], marker=\"o\")\n",
    "\n",
    "ax1.set(xlabel=\"x\", ylabel=\"some_complicated_function(x)\", xlim=(-10, 13), title=\"\")\n",
    "ax1.grid(True)\n",
    "ax1.legend(loc=\"upper right\", title=\"Epochs\")\n",
    "\n",
    "ax2.plot(results.history[\"loss\"], color=\"black\")\n",
    "ax2.set(xlabel=\"epoch\", ylabel=\"loss\")\n",
    "ax2.grid(True)\n",
    "ax2.semilogy()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen, with an increasing number of iterations, the performance of the DNN improves.\n",
    "Additionally, it can be seen nicely that the ReLU activation function was used (sharp linear intervals)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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