project_cnn_full.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.utils.data\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import h5py\n",
    "from data_utils import get_data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from solver_pytorch import Solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (2308, 22, 1000) \n",
      "y_train: (2308,) \n",
      "X_val: (150, 22, 1000) \n",
      "y_val: (150,) \n",
      "X_test: (100, 22, 1000) \n",
      "y_test: (100,) \n"
     ]
    }
   ],
   "source": [
    "# Load data from all .mat files, combine them, eliminate EOG signals, shuffle and \n",
    "# seperate training data, validation data and testing data.\n",
    "# Also do mean subtraction on x.\n",
    "\n",
    "data = get_data('project_datasets',num_validation=150, num_test=100)\n",
    "for k in data.keys():\n",
    "    print('{}: {} '.format(k, data[k].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class flatten to connect to FC layer\n",
    "class Flatten(nn.Module):\n",
    "    def forward(self, x):\n",
    "        N, C, H = x.size() # read in N, C, H\n",
    "        return x.view(N, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn x and y into torch type tensor\n",
    "\n",
    "dtype = torch.FloatTensor\n",
    "\n",
    "X_train = Variable(torch.Tensor(data.get('X_train'))).type(dtype)\n",
    "y_train = Variable(torch.Tensor(data.get('y_train'))).type(torch.IntTensor)\n",
    "X_val = Variable(torch.Tensor(data.get('X_val'))).type(dtype)\n",
    "y_val = Variable(torch.Tensor(data.get('y_val'))).type(torch.IntTensor)\n",
    "X_test = Variable(torch.Tensor(data.get('X_test'))).type(dtype)\n",
    "y_test = Variable(torch.Tensor(data.get('y_test'))).type(torch.IntTensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 1150) loss: 1.466070\n",
      "(Epoch 0 / 50) train acc: 0.262998; val_acc: 0.260000\n",
      "(Epoch 1 / 50) train acc: 0.454073; val_acc: 0.286667\n",
      "(Epoch 2 / 50) train acc: 0.563692; val_acc: 0.380000\n",
      "(Iteration 51 / 1150) loss: 0.923351\n",
      "(Epoch 3 / 50) train acc: 0.696707; val_acc: 0.466667\n",
      "(Epoch 4 / 50) train acc: 0.813258; val_acc: 0.513333\n",
      "(Iteration 101 / 1150) loss: 0.477949\n",
      "(Epoch 5 / 50) train acc: 0.872617; val_acc: 0.520000\n",
      "(Epoch 6 / 50) train acc: 0.913778; val_acc: 0.553333\n",
      "(Iteration 151 / 1150) loss: 0.261262\n",
      "(Epoch 7 / 50) train acc: 0.948440; val_acc: 0.533333\n",
      "(Epoch 8 / 50) train acc: 0.970971; val_acc: 0.586667\n",
      "(Iteration 201 / 1150) loss: 0.125258\n",
      "(Epoch 9 / 50) train acc: 0.984835; val_acc: 0.526667\n",
      "(Epoch 10 / 50) train acc: 0.987868; val_acc: 0.526667\n",
      "(Iteration 251 / 1150) loss: 0.039260\n",
      "(Epoch 11 / 50) train acc: 0.992634; val_acc: 0.546667\n",
      "(Epoch 12 / 50) train acc: 0.996534; val_acc: 0.533333\n",
      "(Epoch 13 / 50) train acc: 0.997400; val_acc: 0.526667\n",
      "(Iteration 301 / 1150) loss: 0.020873\n",
      "(Epoch 14 / 50) train acc: 0.998700; val_acc: 0.546667\n",
      "(Epoch 15 / 50) train acc: 0.999567; val_acc: 0.546667\n",
      "(Iteration 351 / 1150) loss: 0.013597\n",
      "(Epoch 16 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Epoch 17 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Iteration 401 / 1150) loss: 0.007404\n",
      "(Epoch 18 / 50) train acc: 0.999567; val_acc: 0.546667\n",
      "(Epoch 19 / 50) train acc: 0.999567; val_acc: 0.560000\n",
      "(Iteration 451 / 1150) loss: 0.003863\n",
      "(Epoch 20 / 50) train acc: 0.999567; val_acc: 0.560000\n",
      "(Epoch 21 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Iteration 501 / 1150) loss: 0.001967\n",
      "(Epoch 22 / 50) train acc: 0.999567; val_acc: 0.546667\n",
      "(Epoch 23 / 50) train acc: 0.999567; val_acc: 0.566667\n",
      "(Iteration 551 / 1150) loss: 0.003691\n",
      "(Epoch 24 / 50) train acc: 0.999567; val_acc: 0.520000\n",
      "(Epoch 25 / 50) train acc: 0.999567; val_acc: 0.533333\n",
      "(Epoch 26 / 50) train acc: 0.999567; val_acc: 0.560000\n",
      "(Iteration 601 / 1150) loss: 0.001868\n",
      "(Epoch 27 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Epoch 28 / 50) train acc: 0.999567; val_acc: 0.540000\n",
      "(Iteration 651 / 1150) loss: 0.001249\n",
      "(Epoch 29 / 50) train acc: 0.999567; val_acc: 0.533333\n",
      "(Epoch 30 / 50) train acc: 0.999567; val_acc: 0.593333\n",
      "(Iteration 701 / 1150) loss: 0.002173\n",
      "(Epoch 31 / 50) train acc: 0.999567; val_acc: 0.573333\n",
      "(Epoch 32 / 50) train acc: 0.999567; val_acc: 0.566667\n",
      "(Iteration 751 / 1150) loss: 0.001677\n",
      "(Epoch 33 / 50) train acc: 0.999567; val_acc: 0.533333\n",
      "(Epoch 34 / 50) train acc: 0.999567; val_acc: 0.566667\n",
      "(Iteration 801 / 1150) loss: 0.001136\n",
      "(Epoch 35 / 50) train acc: 0.999567; val_acc: 0.540000\n",
      "(Epoch 36 / 50) train acc: 0.999567; val_acc: 0.540000\n",
      "(Iteration 851 / 1150) loss: 0.001140\n",
      "(Epoch 37 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Epoch 38 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Epoch 39 / 50) train acc: 0.999567; val_acc: 0.566667\n",
      "(Iteration 901 / 1150) loss: 0.000737\n",
      "(Epoch 40 / 50) train acc: 0.999567; val_acc: 0.546667\n",
      "(Epoch 41 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Iteration 951 / 1150) loss: 0.000913\n",
      "(Epoch 42 / 50) train acc: 0.999567; val_acc: 0.560000\n",
      "(Epoch 43 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Iteration 1001 / 1150) loss: 0.000644\n",
      "(Epoch 44 / 50) train acc: 0.999567; val_acc: 0.560000\n",
      "(Epoch 45 / 50) train acc: 0.999567; val_acc: 0.566667\n",
      "(Iteration 1051 / 1150) loss: 0.000517\n",
      "(Epoch 46 / 50) train acc: 0.999567; val_acc: 0.553333\n",
      "(Epoch 47 / 50) train acc: 0.999567; val_acc: 0.560000\n",
      "(Iteration 1101 / 1150) loss: 0.000575\n",
      "(Epoch 48 / 50) train acc: 0.999567; val_acc: 0.566667\n",
      "(Epoch 49 / 50) train acc: 0.999567; val_acc: 0.573333\n",
      "(Epoch 50 / 50) train acc: 0.999567; val_acc: 0.586667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe857ea76a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the validation set:  0.593333333333\n",
      "parameters of the best model:\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "# train a 1D convolutional neural network\n",
    "# optimize hyper parameters\n",
    "best_model = None\n",
    "parameters =[] # a list of dictionaries\n",
    "parameter = {} # a dictionary\n",
    "best_params = {} # a dictionary\n",
    "best_val_acc = 0.0\n",
    "\n",
    "# hyper parameters in model\n",
    "filter_num = 40\n",
    "\n",
    "filter_sizes = [7]#, 5, 3]\n",
    "pool_sizes = [4]#, 2]\n",
    "drop_ps = [0.2]#, 0.5, 0.8]\n",
    "\n",
    "# hyper parameters in solver\n",
    "batch_sizes = [100]#, 200, 400]\n",
    "lrs = [1e-3]#, 3.3e-4, 1e-4]\n",
    "\n",
    "\n",
    "for filter_size in filter_sizes:\n",
    "    for pool_size in pool_sizes:\n",
    "        for drop_p in drop_ps:\n",
    "            linear_size = int((X_test.shape[2]-filter_size))+1\n",
    "            linear_size = int((linear_size-pool_size)/pool_size)+1\n",
    "            linear_size *= filter_num\n",
    "                \n",
    "            for batch_size in batch_sizes:\n",
    "                for lr in lrs:\n",
    "                    model = nn.Sequential(\n",
    "                        nn.Conv1d(22, filter_num, kernel_size=filter_size, stride=1),\n",
    "                        nn.ReLU(inplace=True),\n",
    "                        nn.Dropout(p=drop_p),\n",
    "                        nn.BatchNorm1d(num_features=filter_num),\n",
    "                        nn.MaxPool1d(kernel_size=pool_size, stride=pool_size),\n",
    "                        Flatten(),\n",
    "                        nn.Linear(linear_size, 20),\n",
    "                        nn.ReLU(inplace=True),\n",
    "                        nn.Linear(20, 4)\n",
    "                    )\n",
    "                    \n",
    "                    model.type(dtype)\n",
    "                    \n",
    "                    solver = Solver(model, data,\n",
    "                                    lr = lr, batch_size=batch_size,\n",
    "                                    verbose=True, print_every=50)\n",
    "\n",
    "                    solver.train()\n",
    "                    \n",
    "                    # save training results and parameters of neural networks\n",
    "                    parameter['filter_num'] = filter_num\n",
    "                    parameter['filter_size'] = filter_size\n",
    "                    parameter['pool_size'] = pool_size\n",
    "                    parameter['batch_size'] = batch_size\n",
    "                    parameter['lr'] = lr\n",
    "                    parameters.append(parameter)\n",
    "                    \n",
    "                    if solver.best_val_acc > best_val_acc:\n",
    "                        best_val_acc = solver.best_val_acc\n",
    "                        best_model = model\n",
    "                        best_solver = solver\n",
    "                        best_params = parameter\n",
    "                        \n",
    "# Plot the loss function and train / validation accuracies of the best model\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(best_solver.loss_history)\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(best_solver.train_acc_history, '-o', label='train accuracy')\n",
    "plt.plot(best_solver.val_acc_history, '-o', label='validation accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracies')\n",
    "plt.legend(loc='upper center', ncol=4)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()\n",
    "\n",
    "print('Accuracy on the validation set: ', best_val_acc)\n",
    "print('parameters of the best model:')\n",
    "print(best_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy 0.53 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# test set\n",
    "y_test_pred = model(X_test)\n",
    "  \n",
    "_, y_pred = torch.max(y_test_pred,1)\n",
    "test_accu = np.mean(y_pred.data.numpy() ==  y_test.data.numpy())\n",
    "print('Test accuracy', test_accu, '\\n')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}