mxnet_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(700, 30)\n",
      "(700,)\n"
     ]
    }
   ],
   "source": [
    "# pose Data\n",
    "poses = np.loadtxt('pose_data.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": "code",
   "execution_count": 3,
   "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": "code",
   "execution_count": 4,
   "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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<mxnet.io.io.NDArrayIter object at 0x0000025D7F1F3580>\n"
     ]
    }
   ],
   "source": [
    "train_iter = mx.io.NDArrayIter(data=X_train,label=Y_train,batch_size=batch)\n",
    "print(train_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Already bound, ignoring bind()\n",
      "C:\\Users\\seohyun\\anaconda3\\lib\\site-packages\\mxnet\\module\\base_module.py:501: UserWarning: Parameters already initialized and force_init=False. init_params call ignored.\n",
      "  self.init_params(initializer=initializer, arg_params=arg_params, aux_params=aux_params,\n",
      "WARNING:root:optimizer already initialized, ignoring...\n",
      "INFO:root:Epoch[0] Train-accuracy=0.655000\n",
      "INFO:root:Epoch[0] Time cost=0.033\n",
      "INFO:root:Epoch[1] Train-accuracy=0.905000\n",
      "INFO:root:Epoch[1] Time cost=0.030\n",
      "INFO:root:Epoch[2] Train-accuracy=0.970000\n",
      "INFO:root:Epoch[2] Time cost=0.029\n",
      "INFO:root:Epoch[3] Train-accuracy=0.978333\n",
      "INFO:root:Epoch[3] Time cost=0.030\n",
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      "INFO:root:Epoch[6] Train-accuracy=0.986667\n",
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     ]
    },
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     "output_type": "stream",
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     ]
    },
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      "INFO:root:Epoch[298] Time cost=0.026\n",
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      "INFO:root:Epoch[398] Train-accuracy=1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Epoch[398] Time cost=0.027\n",
      "INFO:root:Epoch[399] Train-accuracy=1.000000\n",
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     "output_type": "stream",
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      "INFO:root:Epoch[798] Train-accuracy=1.000000\n"
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     "output_type": "stream",
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      "INFO:root:Epoch[798] Time cost=0.027\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "INFO:root:Epoch[898] Time cost=0.026\n",
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      "INFO:root:Epoch[998] Train-accuracy=1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Epoch[998] Time cost=0.027\n",
      "INFO:root:Epoch[999] Train-accuracy=1.000000\n",
      "INFO:root:Epoch[999] Time cost=0.027\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy: 0.98\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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Saved checkpoint to \"C:\\Users\\seohyun\\Desktop\\deeplens_pose/DeepLens_pose-1000.params\"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "filename = os.getcwd()+\"/DeepLens_pose\"\n",
    "mod.save_checkpoint(filename,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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()+\"/DeepLens_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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.99999726\n",
      "real pose: 0    predicted pose: 0\n",
      "0.99999857\n",
      "real pose: 0    predicted pose: 0\n",
      "0.9999907\n",
      "real pose: 0    predicted pose: 0\n",
      "0.9999989\n",
      "real pose: 0    predicted pose: 0\n",
      "0.9999994\n",
      "real pose: 0    predicted pose: 0\n",
      "0.9999963\n",
      "real pose: 0    predicted pose: 0\n",
      "0.9999976\n",
      "real pose: 0    predicted pose: 0\n",
      "0.9999987\n",
      "real pose: 0    predicted pose: 0\n",
      "0.982346\n",
      "real pose: 0    predicted pose: 0\n",
      "0.9999993\n",
      "real pose: 0    predicted pose: 0\n",
      "1.0\n",
      "real pose: 1    predicted pose: 1\n",
      "0.99915373\n",
      "real pose: 1    predicted pose: 1\n",
      "0.99925417\n",
      "real pose: 1    predicted pose: 1\n",
      "0.9999988\n",
      "real pose: 1    predicted pose: 1\n",
      "1.0\n",
      "real pose: 1    predicted pose: 1\n",
      "0.9999993\n",
      "real pose: 1    predicted pose: 1\n",
      "0.9941011\n",
      "real pose: 1    predicted pose: 1\n",
      "0.99998856\n",
      "real pose: 1    predicted pose: 1\n",
      "0.99999833\n",
      "real pose: 1    predicted pose: 1\n",
      "0.999997\n",
      "real pose: 1    predicted pose: 1\n",
      "1.0\n",
      "real pose: 2    predicted pose: 2\n",
      "1.0\n",
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      "0.99999714\n",
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      "1.0\n",
      "real pose: 2    predicted pose: 2\n",
      "1.0\n",
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      "0.9999994\n",
      "real pose: 2    predicted pose: 2\n",
      "1.0\n",
      "real pose: 2    predicted pose: 2\n",
      "1.0\n",
      "real pose: 2    predicted pose: 2\n",
      "1.0\n",
      "real pose: 2    predicted pose: 2\n",
      "1.0\n",
      "real pose: 2    predicted pose: 2\n",
      "0.99999\n",
      "real pose: 3    predicted pose: 3\n",
      "0.99956745\n",
      "real pose: 3    predicted pose: 3\n",
      "0.9999976\n",
      "real pose: 3    predicted pose: 3\n",
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      "real pose: 3    predicted pose: 3\n",
      "0.9999418\n",
      "real pose: 3    predicted pose: 3\n",
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      "real pose: 3    predicted pose: 3\n",
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      "real pose: 3    predicted pose: 3\n",
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      "real pose: 3    predicted pose: 3\n",
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      "real pose: 4    predicted pose: 4\n",
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      "0.9999254\n",
      "real pose: 4    predicted pose: 4\n",
      "0.99999976\n",
      "real pose: 4    predicted pose: 4\n",
      "0.9995876\n",
      "real pose: 4    predicted pose: 4\n",
      "0.9999442\n",
      "real pose: 4    predicted pose: 4\n",
      "0.99997914\n",
      "real pose: 4    predicted pose: 4\n",
      "0.999997\n",
      "real pose: 4    predicted pose: 4\n",
      "0.9999932\n",
      "real pose: 4    predicted pose: 4\n",
      "0.99983025\n",
      "real pose: 4    predicted pose: 4\n",
      "0.99997795\n",
      "real pose: 5    predicted pose: 5\n",
      "0.99998665\n",
      "real pose: 5    predicted pose: 5\n",
      "0.99999475\n",
      "real pose: 5    predicted pose: 5\n",
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      "0.9999254\n",
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      "real pose: 5    predicted pose: 5\n",
      "0.99994373\n",
      "real pose: 5    predicted pose: 5\n",
      "1.0\n",
      "real pose: 5    predicted pose: 5\n",
      "0.99999475\n",
      "real pose: 5    predicted pose: 5\n",
      "0.9999963\n",
      "real pose: 6    predicted pose: 6\n",
      "0.9999894\n",
      "real pose: 6    predicted pose: 6\n",
      "0.99999845\n",
      "real pose: 6    predicted pose: 6\n",
      "0.99997973\n",
      "real pose: 6    predicted pose: 6\n",
      "0.99999714\n",
      "real pose: 6    predicted pose: 6\n",
      "0.99999785\n",
      "real pose: 6    predicted pose: 6\n",
      "0.99998724\n",
      "real pose: 6    predicted pose: 6\n",
      "0.9999894\n",
      "real pose: 6    predicted pose: 6\n",
      "0.99999905\n",
      "real pose: 6    predicted pose: 6\n",
      "0.99999356\n",
      "real pose: 6    predicted pose: 6\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,700,10):\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:\",int(Y[i]),\"   predicted pose:\",p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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