development/classify.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining our data set\n",
    "Our data set is composed of 700 poses samples\n",
    "Each Pose has 30 features.\n",
    "Poses are split in 7 categories. The purpose of the network will be to predict the correct category for a given sample.\n",
    "We’ll use 600 samples for training and 100 samples for validation.\n",
    "We’ll use a batch size of 10 for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import OpenSSL.SSL\n",
    "import mxnet as mx\n",
    "import numpy as np\n",
    "import logging\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "sample_count = 700\n",
    "train_count = 600\n",
    "valid_count = sample_count - train_count\n",
    "feature_count = 30\n",
    "category_count = 7\n",
    "batch=10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The categories for these 700 poses samples are represented as integers in the 0–6 range for the sequentially-generated data, \n",
    "beacause these data are generated sequentially we need to shuffle them using random.permutation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(700L, 30L)\n",
      "(700L,)\n"
     ]
    }
   ],
   "source": [
    "# pose Data\n",
    "poses = np.loadtxt('poses.csv', delimiter=',')\n",
    "# shuffle data\n",
    "idx = np.random.permutation(sample_count)\n",
    "\n",
    "poses_shuffled = []\n",
    "for i in range(sample_count):\n",
    "    poses_shuffled.append(poses[idx[i]])\n",
    "\n",
    "Y = np.zeros([sample_count])\n",
    "for i in range(0,sample_count):\n",
    "    Y[i] = i/100\n",
    "    \n",
    "y_shuffled = []\n",
    "for i in range(sample_count):\n",
    "    y_shuffled.append(Y[idx[i]])\n",
    "    \n",
    "poses_shuffled = mx.nd.array(np.asarray(poses_shuffled))\n",
    "y_shuffled =  mx.nd.array(np.asarray(y_shuffled))\n",
    "print poses_shuffled.shape\n",
    "print y_shuffled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data set\n",
    "Next, we’re splitting the data set 6 to 1 for training and validation. We use the NDArray.crop function to do this. Here, the data set is completely random, so we can use the top 600 sample for training and the bottom 100 sample for validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = mx.nd.crop(poses_shuffled, begin=(0,0), end=(train_count,feature_count))\n",
    "X_valid = mx.nd.crop(poses_shuffled, begin=(train_count,0), end=(sample_count,feature_count))\n",
    "Y_train = y_shuffled[0:train_count]\n",
    "Y_valid = y_shuffled[train_count:sample_count]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = mx.sym.Variable('data')\n",
    "fc1 = mx.sym.FullyConnected(data, name='fc1', num_hidden=64)\n",
    "relu1 = mx.sym.Activation(fc1, name='relu1', act_type=\"relu\")\n",
    "fc2 = mx.sym.FullyConnected(relu1, name='fc2', num_hidden=category_count)\n",
    "out = mx.sym.SoftmaxOutput(fc2, name='softmax')\n",
    "mod = mx.mod.Module(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the data iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_iter = mx.io.NDArrayIter(data=X_train,label=Y_train,batch_size=batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Already bound, ignoring bind()\n",
      "/Users/abdu307/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/module/base_module.py:464: UserWarning: Parameters already initialized and force_init=False. init_params call ignored.\n",
      "  allow_missing=allow_missing, force_init=force_init)\n",
      "WARNING:root:optimizer already initialized, ignoring...\n",
      "INFO:root:Epoch[0] Train-accuracy=0.211667\n",
      "INFO:root:Epoch[0] Time cost=0.080\n",
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      "INFO:root:Epoch[984] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[984] Time cost=0.034\n",
      "INFO:root:Epoch[985] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[985] Time cost=0.034\n",
      "INFO:root:Epoch[986] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[986] Time cost=0.034\n",
      "INFO:root:Epoch[987] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[987] Time cost=0.041\n",
      "INFO:root:Epoch[988] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[988] Time cost=0.039\n",
      "INFO:root:Epoch[989] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[989] Time cost=0.033\n",
      "INFO:root:Epoch[990] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[990] Time cost=0.035\n",
      "INFO:root:Epoch[991] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[991] Time cost=0.033\n",
      "INFO:root:Epoch[992] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[992] Time cost=0.034\n",
      "INFO:root:Epoch[993] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[993] Time cost=0.034\n",
      "INFO:root:Epoch[994] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[994] Time cost=0.034\n",
      "INFO:root:Epoch[995] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[995] Time cost=0.037\n",
      "INFO:root:Epoch[996] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[996] Time cost=0.042\n",
      "INFO:root:Epoch[997] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[997] Time cost=0.033\n",
      "INFO:root:Epoch[998] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[998] Time cost=0.034\n",
      "INFO:root:Epoch[999] Train-accuracy=0.995000\n",
      "INFO:root:Epoch[999] Time cost=0.033\n"
     ]
    }
   ],
   "source": [
    "# bind the input symbol to the actual data set (samples and labels). \n",
    "mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)\n",
    "# initializer\n",
    "mod.init_params(initializer=mx.init.Xavier(magnitude=2.))\n",
    "mod.init_optimizer(optimizer='sgd', optimizer_params=(('learning_rate', 0.1), ))\n",
    "mod.fit(train_iter, num_epoch=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy: 0.99\n"
     ]
    }
   ],
   "source": [
    "pred_iter = mx.io.NDArrayIter(data=X_valid,label=Y_valid, batch_size=batch)\n",
    "pred_count = valid_count\n",
    "correct_preds = total_correct_preds = 0\n",
    "for preds, i_batch, batch in mod.iter_predict(pred_iter):\n",
    "    label = batch.label[0].asnumpy().astype(int)\n",
    "    pred_label = preds[0].asnumpy().argmax(axis=1)\n",
    "    correct_preds = np.sum(pred_label==label)\n",
    "    total_correct_preds = total_correct_preds + correct_preds\n",
    "print('Validation accuracy: %2.2f' % (1.0*total_correct_preds/pred_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# save a model to pose-symbol.json and pose-1000.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Saved checkpoint to \"/Users/abdu307/deeplens-simon-says/development/pose-1000.params\"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "filename = os.getcwd()+\"/pose\"\n",
    "mod.save_checkpoint(filename,1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# leading and  Testing the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import mxnet as mx\n",
    "import numpy as np\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "sample_count = 7*20\n",
    "train_count = 4*20\n",
    "valid_count = sample_count - train_count\n",
    "feature_count = 15*2\n",
    "category_count = 7\n",
    "batch=10\n",
    "\n",
    "X_pred = mx.nd.zeros((10,feature_count))\n",
    "Y_pred = mx.nd.empty((10,))\n",
    "\n",
    "pred_iter = mx.io.NDArrayIter(data=X_pred,label=Y_pred, batch_size=batch)\n",
    "\n",
    "filename = os.getcwd()+\"/pose\"\n",
    "sym, arg_params, aux_params = mx.model.load_checkpoint(filename, 1000)\n",
    "\n",
    "new_model = mx.mod.Module(symbol=sym)\n",
    "new_model.bind(pred_iter.provide_data, pred_iter.provide_label)\n",
    "new_model.set_params(arg_params, aux_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999269\n",
      "('real pose:', 0, '   predicted pose:', 0.0)\n",
      "0.999755\n",
      "('real pose:', 1, '   predicted pose:', 1.0)\n",
      "0.997491\n",
      "('real pose:', 2, '   predicted pose:', 2.0)\n",
      "0.976469\n",
      "('real pose:', 3, '   predicted pose:', 3.0)\n",
      "0.999867\n",
      "('real pose:', 4, '   predicted pose:', 4.0)\n",
      "0.999839\n",
      "('real pose:', 5, '   predicted pose:', 5.0)\n",
      "1.0\n",
      "('real pose:', 6, '   predicted pose:', 6.0)\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,700,100):\n",
    "    pose = mx.nd.array(poses[i])\n",
    "\n",
    "    X_pred = mx.nd.empty((10,feature_count))\n",
    "    Y_pred = mx.nd.empty((10,))\n",
    "\n",
    "    X_pred[0] = pose\n",
    "\n",
    "    #print(X_pred)\n",
    "    pred_iter = mx.io.NDArrayIter(data=X_pred,label=Y_pred, batch_size=batch)\n",
    "\n",
    "    a = new_model.predict(pred_iter)[0]\n",
    "    a= list(a.asnumpy())\n",
    "    print max(a)\n",
    "    p = a.index(max(a))\n",
    "    print (\"real pose:\",p,\"   predicted pose:\",Y[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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