notebooks/MXNet_CNN_highAPI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# High-level MXNet Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import mxnet as mx\n",
    "import logging\n",
    "from common.params import *\n",
    "from common.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# logging\n",
    "logger = logging.getLogger();\n",
    "logger.setLevel(logging.DEBUG);\n",
    "formatter = logging.Formatter('%(message)s');\n",
    "h2 = logging.StreamHandler(sys.stdout)\n",
    "h2.setFormatter(formatter);\n",
    "logger.addHandler(h2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Force one-gpu\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OS:  linux\n",
      "Python:  3.5.2 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:53:06) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "Numpy:  1.14.1\n",
      "MXNet:  0.12.0\n",
      "GPU:  ['Tesla P100-PCIE-16GB', 'Tesla P100-PCIE-16GB']\n",
      "CUDA Version 8.0.61\n",
      "CuDNN Version  6.0.21\n"
     ]
    }
   ],
   "source": [
    "print(\"OS: \", sys.platform)\n",
    "print(\"Python: \", sys.version)\n",
    "print(\"Numpy: \", np.__version__)\n",
    "print(\"MXNet: \", mx.__version__)\n",
    "print(\"GPU: \", get_gpu_name())\n",
    "print(get_cuda_version())\n",
    "print(\"CuDNN Version \", get_cudnn_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_symbol(n_classes=N_CLASSES):\n",
    "    data = mx.symbol.Variable('data')\n",
    "    # size = [(old-size - kernel + 2*padding)/stride]+1\n",
    "    # if kernel = 3, pad with 1 either side\n",
    "    conv1 = mx.symbol.Convolution(data=data, num_filter=50, pad=(1,1), kernel=(3,3))\n",
    "    relu1 = mx.symbol.Activation(data=conv1, act_type=\"relu\")\n",
    "    conv2 = mx.symbol.Convolution(data=relu1, num_filter=50, pad=(1,1), kernel=(3,3))\n",
    "    pool1 = mx.symbol.Pooling(data=conv2, pool_type=\"max\", kernel=(2,2), stride=(2,2))\n",
    "    relu2 = mx.symbol.Activation(data=pool1, act_type=\"relu\")\n",
    "    drop1 = mx.symbol.Dropout(data=relu2, p=0.25)\n",
    "    \n",
    "    conv3 = mx.symbol.Convolution(data=drop1, num_filter=100, pad=(1,1), kernel=(3,3))\n",
    "    relu3 = mx.symbol.Activation(data=conv3, act_type=\"relu\")\n",
    "    conv4 = mx.symbol.Convolution(data=relu3, num_filter=100, pad=(1,1), kernel=(3,3))\n",
    "    pool2 = mx.symbol.Pooling(data=conv4, pool_type=\"max\", kernel=(2,2), stride=(2,2))\n",
    "    relu4 = mx.symbol.Activation(data=pool2, act_type=\"relu\")\n",
    "    drop2 = mx.symbol.Dropout(data=relu4, p=0.25)\n",
    "           \n",
    "    flat1 = mx.symbol.Flatten(data=drop2)\n",
    "    fc1 = mx.symbol.FullyConnected(data=flat1, num_hidden=512)\n",
    "    relu7 = mx.symbol.Activation(data=fc1, act_type=\"relu\")\n",
    "    drop4 = mx.symbol.Dropout(data=relu7, p=0.5)\n",
    "    fc2 = mx.symbol.FullyConnected(data=drop4, num_hidden=n_classes) \n",
    "    \n",
    "    input_y = mx.symbol.Variable('softmax_label')  \n",
    "    m = mx.symbol.SoftmaxOutput(data=fc2, label=input_y, name=\"softmax\")\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_model(m, batchsize=BATCHSIZE, lr=LR, momentum=MOMENTUM):\n",
    "    ctx = [mx.gpu(0)]\n",
    "    mod = mx.mod.Module(context=ctx, symbol=m)\n",
    "    mod.bind(data_shapes=[('data', (batchsize, 3, 32, 32))],\n",
    "             label_shapes=[('softmax_label', (batchsize,))])\n",
    "    return mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing train set...\n",
      "Preparing test set...\n",
      "(50000, 3, 32, 32) (10000, 3, 32, 32) (50000,) (10000,)\n",
      "float32 float32 int32 int32\n",
      "CPU times: user 1.07 s, sys: 1.09 s, total: 2.16 s\n",
      "Wall time: 2.17 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Data into format for library\n",
    "x_train, x_test, y_train, y_test = cifar_for_library(channel_first=True)\n",
    "# Load data-iterator\n",
    "train_iter = mx.io.NDArrayIter(x_train, y_train, BATCHSIZE, shuffle=True)\n",
    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
    "print(x_train.dtype, x_test.dtype, y_train.dtype, y_test.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.49 ms, sys: 0 ns, total: 4.49 ms\n",
      "Wall time: 3.58 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Load symbol\n",
    "sym = create_symbol()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1 s, sys: 714 ms, total: 1.71 s\n",
      "Wall time: 1.99 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Initialise model\n",
    "model = init_model(sym)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already bound, ignoring bind()\n",
      "Epoch[0] Train-accuracy=0.337976\n",
      "Epoch[0] Time cost=4.913\n",
      "Epoch[1] Train-accuracy=0.498601\n",
      "Epoch[1] Time cost=4.840\n",
      "Epoch[2] Train-accuracy=0.580802\n",
      "Epoch[2] Time cost=4.886\n",
      "Epoch[3] Train-accuracy=0.642144\n",
      "Epoch[3] Time cost=4.821\n",
      "Epoch[4] Train-accuracy=0.686161\n",
      "Epoch[4] Time cost=4.836\n",
      "Epoch[5] Train-accuracy=0.718570\n",
      "Epoch[5] Time cost=4.835\n",
      "Epoch[6] Train-accuracy=0.744246\n",
      "Epoch[6] Time cost=4.849\n",
      "Epoch[7] Train-accuracy=0.767823\n",
      "Epoch[7] Time cost=4.830\n",
      "Epoch[8] Train-accuracy=0.784867\n",
      "Epoch[8] Time cost=4.836\n",
      "Epoch[9] Train-accuracy=0.802130\n",
      "Epoch[9] Time cost=4.828\n",
      "CPU times: user 44.5 s, sys: 17.3 s, total: 1min 1s\n",
      "Wall time: 48.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Main training loop: 49s\n",
    "model.fit(train_data=train_iter, \n",
    "          num_epoch=EPOCHS,\n",
    "          initializer=mx.init.Xavier(rnd_type='uniform'),\n",
    "          optimizer='sgd',\n",
    "          optimizer_params=(('learning_rate', LR), ('momentum', MOMENTUM)),\n",
    "          eval_metric=mx.metric.create('acc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 370 ms, sys: 142 ms, total: 512 ms\n",
      "Wall time: 316 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Main evaluation loop: 311ms\n",
    "y_guess = model.predict(mx.io.NDArrayIter(x_test, batch_size=BATCHSIZE, shuffle=False))\n",
    "y_guess = np.argmax(y_guess.asnumpy(), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7707\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \", sum(y_guess == y_test)/len(y_guess))"
   ]
  }
 ],
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