FNO_Cifar10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc716cfb",
   "metadata": {},
   "source": [
    "# Implementing Image Classification through Fourier Neural Operator\n",
    "\n",
    "\n",
    "## Backend : Tensorflow / Keras\n",
    "## Datasets : Tensorflow.keras.datasets cifar10\n",
    "\n",
    "### GPU : Tesla V100-SXM2 x 8\n",
    "\n",
    "In this notebook we will introduce the brief theory behind the Fourier Neural Operators and use them to solve a data-driven image classification problem. The theory is adopted from the [paper](https://arxiv.org/pdf/2010.08895.pdf) by Zongyi Li et al. \n",
    "\n",
    "\n",
    "- Author of the Notebook : $Kozak Hou$\n",
    "- Email : kozak20010716@g.ncu.edu.tw\n",
    "- Tel : +886-905804898"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9178ce02",
   "metadata": {},
   "source": [
    "---\n",
    "## Import Necessary Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81d496bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "afd8be2e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:54:34.573815: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n",
      "There're 50000 32x32 pixels training images and 10000 testing images, \n"
     ]
    }
   ],
   "source": [
    "#Clear unnecessary memory\n",
    "from tensorflow.keras import backend as K\n",
    "import tensorflow as tf\n",
    "K.clear_session()\n",
    "\n",
    "\n",
    "# Preparing Datasets\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "\n",
    "# Import Necessary Libraries : TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Input, Add, Dense, Activation, \\\n",
    "ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D,\\\n",
    "MaxPooling2D, GlobalMaxPooling2D, UpSampling2D, Dropout\n",
    "\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
    "from IPython.display import SVG\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.initializers import glorot_uniform\n",
    "from tensorflow.signal import fft, fft2d, ifft, ifft2d, rfft, irfft, rfft2d, irfft2d\n",
    "import scipy.misc\n",
    "from matplotlib.pyplot import imshow\n",
    "from tensorflow import Tensor\n",
    "\n",
    "# Only used in Jupyter notebook\n",
    "%matplotlib inline \n",
    "\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "\n",
    "print(tf.__version__)\n",
    "\n",
    "\n",
    "# Loading Datasets\n",
    "(train_images, train_labels),(test_images, test_labels) = cifar10.load_data()\n",
    "\n",
    "\n",
    "print(\"There're 50000 32x32 pixels training images and 10000 testing images, \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3164ae33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8260b4dc70>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_images.shape\n",
    "plt.imshow(train_images[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72a5aab4",
   "metadata": {},
   "source": [
    "---\n",
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47b85026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalized\n",
    "train_images = train_images/255.0\n",
    "test_images =  test_images/255.0\n",
    "\n",
    "#As each images is 32 x 32, and we will later apply CNN; \n",
    "#therefore, we have to reshape the images from 2-D to 4-D\n",
    "w, h = 32, 32\n",
    "train_images = train_images.reshape(train_images.shape[0], w, h, 3)\n",
    "test_images = test_images.reshape(test_images.shape[0], w, h, 3)\n",
    "\n",
    "train_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafc6f4d",
   "metadata": {},
   "source": [
    "---\n",
    "##  Comfirming the GPU configuration\n",
    "\n",
    "\n",
    "\n",
    "The tensorflow APIs about testing whether the GPU is functioning are as follow :\n",
    "\n",
    "- **The Python Code**\n",
    "\n",
    "```python\n",
    "  import tensorflow as tf\n",
    "  if tf.test.gpu_device_name(): \n",
    "\n",
    "    print('Default GPU Device:{}'.format(tf.test.gpu_device_name()))\n",
    "\n",
    "  else:\n",
    "    print(\"Please install GPU version of TF\")\n",
    "```\n",
    "\n",
    "\n",
    "- **output** \n",
    "\n",
    " Default GPU Device:/device:GPU:0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3db646a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default GPU Device:/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "if tf.test.gpu_device_name(): \n",
    "\n",
    "    print('Default GPU Device:{}'.format(tf.test.gpu_device_name()))\n",
    "\n",
    "else:\n",
    "    print(\"Please install GPU version of TF\")"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "42e81bfe",
   "metadata": {},
   "source": [
    "## List all categories\n",
    "- In this example, we have, obviously, 10 numbers from 0 to 9 and each number represent an object.\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ab7bdf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "classess = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "classess_text = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13467dc2",
   "metadata": {},
   "source": [
    "---\n",
    "# Applying Fourier Neural Operator\n",
    "\n",
    "## FNO Theory\n",
    "\n",
    "Fourier neural operator (FNO) is a data-driven architecture which can be used to parameterize solutions for a distribution of PDE solutions. The key feature of FNO is the spectral convolutions: operations that place the integral kernel in Fourier space. The spectral convolution (Fourier integral oprator) is defined as follows:\n",
    "\n",
    "\\begin{equation}\n",
    "(\\mathcal{K}(\\mathbf{w})\\phi)(x) = \\mathcal{F}^{-1}(R_{\\mathbf{W}}\\cdot \\left(\\mathcal{F}\\right)\\phi)(x), \\quad \\forall x \\in D\n",
    "\\end{equation}\n",
    "\n",
    "where $\\mathcal{F}$ and $\\mathcal{F}^{-1}$ are the forward and inverse Fourier transforms, respectively.\n",
    "$R_{\\mathbf{w}}$ is the transformation which contains the learnable parameters $\\mathbf{w}$. Note this operator is calculated\n",
    "over the entire *structured Euclidean* domain $D$ discretized with $n$ points.\n",
    "\n",
    "Fast Fourier Transform (FFT) is used to perform the Fourier transforms efficiently and the resulting transformation $R_{\\mathbf{w}}$ is just finite size matrix of learnable weights. In side the spectral convolution, the Fourier coefficients are truncated to only the lower modes which intern allows explicit control over the dimensionality of the spectral space and linear operator.\n",
    "\n",
    "The FNO model is a the composition of a fully-connected \"lifting\" layer, $L$ spectral convolutions with point-wise linear skip connections and a decoding point-wise fully-connected neural network at the end.\n",
    "\n",
    "\\begin{equation}\n",
    "u_{net}(\\Phi;\\theta) = \\mathcal{Q}\\circ \\sigma(W_{L} + \\mathcal{K}_{L}) \\circ ... \\circ \\sigma(W_{1} + \\mathcal{K}_{1})\\circ \\mathcal{P}(\\Phi), \\quad \\Phi=\\left\\{\\phi(x); \\forall x \\in D\\right\\}\n",
    "\\end{equation}\n",
    "\n",
    "in which $\\sigma(W_{i} + \\mathcal{K}_{i})$ is the spectral convolution layer $i$ with the point-wise linear transform $W_{i}$ and activation function $\\sigma(\\cdot)$. $\\mathcal{P}$ is the point-wise lifting network that projects the input into a higher-dimensional latent space, $\\mathcal{P}: \\mathbb{R}^{d_in} \\rightarrow \\mathbb{R}^{k}$.\n",
    "\n",
    "Similarly $\\mathcal{Q}$ is the point-wise fully-connected decoding network, $\\mathcal{P}: \\mathbb{R}^{k} \\rightarrow \\mathbb{R}^{d_out}$. Since all fully-connected components of FNO are point-wise operations, the model is invariant to the dimensionality of the input.\n",
    "\n",
    "\n",
    "**Note:** While FNO is technically invariant to the dimensionality of the discretized domain $D$, this domain *must* be a structured grid in Euclidean space. The inputs to FNO are analgous to images, but the model is invariant to the image resolution."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "454617bb",
   "metadata": {},
   "source": [
    "---\n",
    "## Fourier Neural Operator (FNO) Control Flow\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "---\n",
    "\n",
    "***First Part***\n",
    "\n",
    "The first step we should implement is raising the dimension throuh 1 x 1 kernel's convolution, which often bein regarded as encoder layer. \n",
    "\n",
    "$$a(x) \\rightarrow P $$\n",
    "\n",
    "---------------\n",
    "\n",
    "***Middle Part***\n",
    "\n",
    "As we get the higher dimensional input space, we will start to build our Fourier Neural Operator.\n",
    "\n",
    "This method divided the input into two parts. One has to entry in the fourier space through fast fourier transfrom, and the other one is same as usual, the later one will be taken as residual to supplement the signal which been eradicate by linear transfrom within FFT. \n",
    "\n",
    "After finish the steps mentioned above. We will, as usual, add together and connect with an activation function, which used to be ReLU (Rectified Linear Units)\n",
    "\n",
    "We repeat the steps for four time and then output our result to the final parts.\n",
    "\n",
    "--------\n",
    "\n",
    "***Final Part***\n",
    "\n",
    "After we obtain the output from FNO, we have to decoded our latent output through decoder layers, which is the same as encoder, we use 1 x 1 kernel's convolution to lower our dimensional space. Then output our final results. \n",
    "\n",
    "$$Q  \\rightarrow u(x) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b81aaaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First Part : Defining a(x) --> P\n",
    "\n",
    "def P(X, F1, k_size, s, stage):\n",
    "    \n",
    "    # Name definition\n",
    "    P_Name = 'P-layer' + str(stage)\n",
    "    P_BN_Name = 'P-layer-BN' + str(stage)\n",
    "    \n",
    "    X = Conv2D(filters = F1, kernel_size = (k_size, k_size), strides = (s, s), padding = 'valid',\n",
    "              name = P_Name, kernel_initializer = glorot_uniform(seed = 0))(X)\n",
    "    \n",
    "    X = BatchNormalization(axis = 3, name = P_BN_Name)(X)\n",
    "    \n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3fc239c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Middle Part : Defining FFT and Residual block\n",
    "\n",
    "def FourierLayer(X, stage):\n",
    "    \n",
    "    # Name definition\n",
    "    FFT_name = 'fft-layer' + str(stage)\n",
    "    RFFT_name = 'ifft-layer' + str(stage)\n",
    "    \n",
    "    \n",
    "    Res_X = X\n",
    "    \n",
    "    \n",
    "    X = rfft2d(X, name = FFT_name)\n",
    "    \n",
    "    \n",
    "    out_ft = tf.zeros((1, X.shape[1], X.shape[2], X.shape[3]),dtype=tf.dtypes.complex64)\n",
    "\n",
    "    \n",
    "    out_ft_first = tf.Variable(np.array(out_ft.shape))\n",
    "    out_ft_first = tf.math.multiply(X[:, :, :5, :5], tf.cast(Res_X[:, :, :5, :5], tf.complex64))\n",
    "\n",
    "    out_ft_end  = tf.Variable(np.array(out_ft.shape))\n",
    "    out_ft_end = tf.math.multiply(X[:, :, -5:, :5], tf.cast(Res_X[:, :, -5:, :5], tf.complex64))\n",
    "\n",
    "\n",
    "    o1 = tf.concat([out_ft_first,out_ft[:,:,:5,5:]],axis=3)\n",
    "    o2 = tf.concat([out_ft[:,:,-5:,5:],out_ft_end],axis=3)\n",
    "    \n",
    "    out_ft = tf.concat([o1,o2],axis=2)\n",
    "\n",
    "    \n",
    "    X = irfft2d(X,name = RFFT_name)\n",
    "    \n",
    "    \n",
    "    X = Add()([X, Res_X])\n",
    "    \n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    \n",
    "    return X\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1dc236c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final Part : Defining Q --> u(x)\n",
    "\n",
    "def Q(X, F1, k_size, s, stage):\n",
    "    \n",
    "    # Name definition\n",
    "    Q_Name = 'Q-layer' + str(stage)\n",
    "    Q_BN_Name = 'Q-layer-BN' + str(stage)\n",
    "    \n",
    "    \n",
    "    X = Conv2D(filters = F1, kernel_size = (k_size, k_size), strides = (s, s), padding = 'same',\n",
    "              name = Q_Name, kernel_initializer = glorot_uniform(seed = 0))(X)\n",
    "    \n",
    "    X = BatchNormalization(axis = 3, name = Q_BN_Name)(X)\n",
    "    \n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f898e6",
   "metadata": {},
   "source": [
    "---\n",
    "## There seems to have some memory allocated problem in different Tensorflow's version, we use the following method to fix it.\n",
    "\n",
    "```python\n",
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a33a56a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "738a8058",
   "metadata": {},
   "source": [
    "---\n",
    "# Building the Model of Fourier Neural Operator (FNO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d1cdeac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def FNO(input_shape = (w, h, 3), classes = classess):\n",
    "    \n",
    "    X_input = Input(shape = input_shape)\n",
    "    \n",
    "    # Zero-Padding\n",
    "    X = ZeroPadding2D((2, 2))(X_input)\n",
    "    \n",
    "    # First Part\n",
    "    X = P(X, F1 = 64, k_size = 1, s = 1, stage = 1)\n",
    "    X = P(X, F1 = 64, k_size = 2, s = 2, stage = 2)\n",
    "    X = MaxPooling2D((2, 2), strides = (2, 2))(X)\n",
    "    X = Dropout(0.3)(X)\n",
    "    \n",
    "    \n",
    "    # Middle Part\n",
    "    X = FourierLayer(X, stage = 1)\n",
    "    X = FourierLayer(X, stage = 2)\n",
    "#     X = FourierLayer(X, stage = 3)\n",
    "#     X = FourierLayer(X, stage = 4)\n",
    "    \n",
    "    # Final Part\n",
    "    X = UpSampling2D((2, 2))(X)\n",
    "    X = Q(X, F1 = 64, k_size = 1, s = 1, stage = 1)\n",
    "    X = Q(X, F1 = 32, k_size = 1, s = 2, stage = 2)\n",
    "    X = Dropout(0.3)(X)\n",
    "    \n",
    "    # Output Softmax\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(len(classess), activation='softmax', kernel_initializer = glorot_uniform(seed=0))(X)\n",
    "\n",
    "    # Create model\n",
    "    model = Model(inputs = X_input, outputs = X, name = 'Fourier-Neural-Operator')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91577be8",
   "metadata": {},
   "source": [
    "---\n",
    "## Clearing the unnecessary memory again (Not Necessary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc639838",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Clear unnecessary memory\n",
    "from tensorflow.keras import backend as K\n",
    "import tensorflow as tf\n",
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598a6781",
   "metadata": {},
   "source": [
    "---\n",
    "## Training and Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "228b4a1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/layers/normalization/batch_normalization.py:562: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/keras/layers/normalization/batch_normalization.py:562: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "2023-01-31 15:54:39.282180: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
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      "2023-01-31 15:54:39.758072: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Fourier-Neural-Operator\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 32, 32, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " zero_padding2d (ZeroPadding2D)  (None, 36, 36, 3)   0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " P-layer1 (Conv2D)              (None, 36, 36, 64)   256         ['zero_padding2d[0][0]']         \n",
      "                                                                                                  \n",
      " P-layer-BN1 (BatchNormalizatio  (None, 36, 36, 64)  256         ['P-layer1[0][0]']               \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 36, 36, 64)   0           ['P-layer-BN1[0][0]']            \n",
      "                                                                                                  \n",
      " P-layer2 (Conv2D)              (None, 18, 18, 64)   16448       ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " P-layer-BN2 (BatchNormalizatio  (None, 18, 18, 64)  256         ['P-layer2[0][0]']               \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 18, 18, 64)   0           ['P-layer-BN2[0][0]']            \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 9, 9, 64)     0           ['activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 9, 9, 64)     0           ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " tf_op_layer_fft-layer1 (Tensor  [(None, 9, 9, 33)]  0           ['dropout[0][0]']                \n",
      " FlowOpLayer)                                                                                     \n",
      "                                                                                                  \n",
      " tf_op_layer_ifft-layer1 (Tenso  [(None, 9, 9, 64)]  0           ['tf_op_layer_fft-layer1[0][0]'] \n",
      " rFlowOpLayer)                                                                                    \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 9, 9, 64)     0           ['tf_op_layer_ifft-layer1[0][0]',\n",
      "                                                                  'dropout[0][0]']                \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 9, 9, 64)     0           ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " tf_op_layer_fft-layer2 (Tensor  [(None, 9, 9, 33)]  0           ['activation_2[0][0]']           \n",
      " FlowOpLayer)                                                                                     \n",
      "                                                                                                  \n",
      " tf_op_layer_ifft-layer2 (Tenso  [(None, 9, 9, 64)]  0           ['tf_op_layer_fft-layer2[0][0]'] \n",
      " rFlowOpLayer)                                                                                    \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 9, 9, 64)     0           ['tf_op_layer_ifft-layer2[0][0]',\n",
      "                                                                  'activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 9, 9, 64)     0           ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " up_sampling2d (UpSampling2D)   (None, 18, 18, 64)   0           ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " Q-layer1 (Conv2D)              (None, 18, 18, 64)   4160        ['up_sampling2d[0][0]']          \n",
      "                                                                                                  \n",
      " Q-layer-BN1 (BatchNormalizatio  (None, 18, 18, 64)  256         ['Q-layer1[0][0]']               \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 18, 18, 64)   0           ['Q-layer-BN1[0][0]']            \n",
      "                                                                                                  \n",
      " Q-layer2 (Conv2D)              (None, 9, 9, 32)     2080        ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " Q-layer-BN2 (BatchNormalizatio  (None, 9, 9, 32)    128         ['Q-layer2[0][0]']               \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 9, 9, 32)     0           ['Q-layer-BN2[0][0]']            \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 9, 9, 32)     0           ['activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 2592)         0           ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 10)           25930       ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 49,770\n",
      "Trainable params: 49,322\n",
      "Non-trainable params: 448\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = FNO(input_shape = (w, h, 3), classes = classess)\n",
    "\n",
    "# Take a look at the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14f1b692",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "91821d2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 42500 samples, validate on 7500 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:54:40.544118: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
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      "2023-01-31 15:54:40.544189: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.544194: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627768: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627803: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627808: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627814: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627829: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627840: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627844: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.627849: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:54:40.763427: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.763456: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.763461: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.763466: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.763481: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.763486: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.763490: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.763494: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791776: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791806: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791812: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791816: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791838: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791843: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791848: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:40.791852: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42496/42500 [============================>.] - ETA: 0s - loss: 1.8180 - accuracy: 0.3608"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:54:48.134553: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:48.134586: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42500/42500 [==============================] - ETA: 0s - loss: 1.8179 - accuracy: 0.3608"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:54:48.337767: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:48.337794: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
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      "2023-01-31 15:54:48.337808: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:54:48.577690: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:48.577726: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:48.577731: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
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      "2023-01-31 15:54:48.577755: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:48.577759: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:48.577763: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42500/42500 [==============================] - 9s 213us/sample - loss: 1.8179 - accuracy: 0.3608 - val_loss: 2.9677 - val_accuracy: 0.1212\n",
      "Epoch 2/20\n",
      " 1024/42500 [..............................] - ETA: 2s - loss: 1.5365 - accuracy: 0.4482"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:54:49.716167: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.716219: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
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      "2023-01-31 15:54:49.772922: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778363: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778383: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778387: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778391: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778403: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778408: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778412: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.778416: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785183: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785201: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785206: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785210: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785221: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785226: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785230: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.785234: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793117: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793136: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793141: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793145: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793156: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793161: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793165: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:54:49.793169: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42500/42500 [==============================] - 3s 75us/sample - loss: 1.4547 - accuracy: 0.4785 - val_loss: 2.2737 - val_accuracy: 0.2747\n",
      "Epoch 3/20\n",
      "42500/42500 [==============================] - 7s 171us/sample - loss: 1.3363 - accuracy: 0.5213 - val_loss: 1.7427 - val_accuracy: 0.4012\n",
      "Epoch 4/20\n",
      "42500/42500 [==============================] - 3s 73us/sample - loss: 1.2763 - accuracy: 0.5408 - val_loss: 1.3240 - val_accuracy: 0.5385\n",
      "Epoch 5/20\n",
      "42500/42500 [==============================] - 3s 72us/sample - loss: 1.2035 - accuracy: 0.5699 - val_loss: 1.2035 - val_accuracy: 0.5769\n",
      "Epoch 6/20\n",
      "42500/42500 [==============================] - 3s 73us/sample - loss: 1.1909 - accuracy: 0.5744 - val_loss: 1.2663 - val_accuracy: 0.5488\n",
      "Epoch 7/20\n",
      "42500/42500 [==============================] - 3s 73us/sample - loss: 1.1491 - accuracy: 0.5883 - val_loss: 1.2705 - val_accuracy: 0.5505\n",
      "Epoch 8/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 1.1315 - accuracy: 0.5966 - val_loss: 1.4271 - val_accuracy: 0.5149\n",
      "Epoch 9/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 1.1000 - accuracy: 0.6076 - val_loss: 1.1772 - val_accuracy: 0.5853\n",
      "Epoch 10/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 1.0876 - accuracy: 0.6131 - val_loss: 1.3745 - val_accuracy: 0.5263\n",
      "Epoch 11/20\n",
      "42500/42500 [==============================] - 3s 72us/sample - loss: 1.0811 - accuracy: 0.6159 - val_loss: 1.0869 - val_accuracy: 0.6144\n",
      "Epoch 12/20\n",
      "42500/42500 [==============================] - 3s 73us/sample - loss: 1.0597 - accuracy: 0.6232 - val_loss: 1.1244 - val_accuracy: 0.5989\n",
      "Epoch 13/20\n",
      "42500/42500 [==============================] - 3s 73us/sample - loss: 1.0509 - accuracy: 0.6257 - val_loss: 1.0818 - val_accuracy: 0.6160\n",
      "Epoch 14/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 1.0453 - accuracy: 0.6285 - val_loss: 1.1701 - val_accuracy: 0.5851\n",
      "Epoch 15/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 1.0209 - accuracy: 0.6361 - val_loss: 1.0875 - val_accuracy: 0.6103\n",
      "Epoch 16/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 1.0219 - accuracy: 0.6376 - val_loss: 1.0762 - val_accuracy: 0.6176\n",
      "Epoch 17/20\n",
      "42500/42500 [==============================] - 3s 73us/sample - loss: 1.0149 - accuracy: 0.6391 - val_loss: 1.0456 - val_accuracy: 0.6300\n",
      "Epoch 18/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 0.9954 - accuracy: 0.6472 - val_loss: 1.1506 - val_accuracy: 0.5996\n",
      "Epoch 19/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 1.0075 - accuracy: 0.6434 - val_loss: 1.0193 - val_accuracy: 0.6371\n",
      "Epoch 20/20\n",
      "42500/42500 [==============================] - 3s 74us/sample - loss: 0.9837 - accuracy: 0.6498 - val_loss: 1.0887 - val_accuracy: 0.6115\n",
      "\n",
      "\n",
      "\n",
      "Calculating time :  73.34427452087402\n",
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "original_time = time()\n",
    "history = model.fit(train_images, \n",
    "                    train_labels,\n",
    "                    batch_size = 256,\n",
    "                    validation_split=0.15,\n",
    "                    epochs = 20,\n",
    "                    shuffle = True)\n",
    "\n",
    "print(\"\\n\\n\\nCalculating time : \", time()-original_time)\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9228a07",
   "metadata": {},
   "source": [
    "---\n",
    "# Evaluate the Training Process (Loss and Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b144cace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('FNO model loss', fontsize = 15)\n",
    "plt.ylabel('loss', fontsize = 15)\n",
    "plt.xlabel('epoch', fontsize = 15)\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5c20b51b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('FNO model train accuracy', fontsize = 15)\n",
    "plt.xlabel('epoch', fontsize = 15)\n",
    "plt.ylabel('accuracy', fontsize = 15)\n",
    "plt.legend(['train accuracy', 'test accuracy'], loc = 'upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32bb8d4f",
   "metadata": {},
   "source": [
    "---\n",
    "# Evaluate the Testing Process (Loss and Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e855bf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test accuracy: 0.6132\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model using the test set\n",
    "loss, accuracy = model.evaluate(test_images, test_labels, verbose = 1)\n",
    "print('\\nTest accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23efff56",
   "metadata": {},
   "source": [
    "---\n",
    "# Making Prediction from the test_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e18681e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-31 15:55:54.867979: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:54.868015: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:54.868020: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:54.868026: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:54.868042: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:54.868047: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:54.868051: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:54.868055: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869011: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869048: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869055: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869060: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869080: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869087: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869092: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.869098: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880679: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880712: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880723: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880732: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880756: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880767: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880777: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.880786: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888540: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888558: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888563: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888567: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888578: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888585: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888589: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.888594: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896579: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896597: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896601: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896606: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896617: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896622: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896626: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.896630: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902485: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902503: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902507: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902512: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902523: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902527: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902532: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.902537: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918215: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918232: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918237: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918241: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918252: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918257: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918261: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.918268: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923680: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923697: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923702: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923706: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923717: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923723: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923728: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.923734: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934586: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934629: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934647: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934661: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934696: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934713: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934728: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.934743: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940557: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940574: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940579: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940583: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940594: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940601: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940605: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n",
      "2023-01-31 15:55:55.940610: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:161] TF_ENABLE_AUTO_MIXED_PRECISION has no effect.\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2122df0",
   "metadata": {},
   "source": [
    "---\n",
    "# Defining the Visualizing Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa61b996",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape(train_images.shape[0], w, h, 3)\n",
    "test_images = test_images.reshape(test_images.shape[0], w, h, 3)\n",
    "\n",
    "\n",
    "# Cifar's label need to reshape to scalar array\n",
    "test_labels = test_labels.reshape(10000, )\n",
    "\n",
    "\n",
    "\n",
    "# Helper functions to plot images \n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(classess_text[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                classess_text[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46290641",
   "metadata": {},
   "source": [
    "---\n",
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57f1ce24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f793930f",
   "metadata": {},
   "source": [
    "---\n",
    "# References : \n",
    "\n",
    "\n",
    "**Citations**\n",
    "\n",
    "```\n",
    "@misc{li2020fourier,\n",
    "      title={Fourier Neural Operator for Parametric Partial Differential Equations}, \n",
    "      author={Zongyi Li and Nikola Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},\n",
    "      year={2020},\n",
    "      eprint={2010.08895},\n",
    "      archivePrefix={arXiv},\n",
    "      primaryClass={cs.LG}\n",
    "}\n",
    "\n",
    "@misc{li2020neural,\n",
    "      title={Neural Operator: Graph Kernel Network for Partial Differential Equations}, \n",
    "      author={Zongyi Li and Nikola Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar},\n",
    "      year={2020},\n",
    "      eprint={2003.03485},\n",
    "      archivePrefix={arXiv},\n",
    "      primaryClass={cs.LG}\n",
    "}\n",
    "\n",
    "```\n",
    "\n",
    "[Author's github site](https://github.com/KozakHou)\n",
    "\n",
    "[Zhongyi Li 's github site](https://github.com/zongyi-li/fourier_neural_operator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b0878741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "truck\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_images[15][:, :, 1])\n",
    "print('truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3d34005",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}