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ypwhs/mxnet_mnist.ipynb

Created December 8, 2016 12:14
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mxnet_mnist
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mxnet_mnist.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import mxnet as mx\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import logging\n",
"import sys\n",
"root_logger = logging.getLogger()\n",
"stdout_handler = logging.StreamHandler(sys.stdout)\n",
"root_logger.addHandler(stdout_handler)\n",
"root_logger.setLevel(logging.DEBUG)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"data": {
"image/png": 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s47/fFRERwcaNG0lNTeXSpUu89957bNu2TbYegFKpxMXFBScnJ7Kzs9m5cyfh4eGsWrWK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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12183a990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from keras.datasets import mnist\n",
"from keras.utils import np_utils\n",
"\n",
"nb_classes = 10\n",
"(X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n",
"X_train = X_train/255.0\n",
"X_test = X_test/255.0\n",
"\n",
"X_train = X_train.reshape((60000, 1, 28, 28))\n",
"X_test = X_test.reshape((10000, 1, 28, 28))\n",
"\n",
"for i in range(10):\n",
" plt.subplot(1,10,i+1)\n",
" plt.imshow(X_train[i][0], cmap='gray')\n",
" plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"batch_size = 128\n",
"train_iter = mx.io.NDArrayIter(X_train, Y_train, batch_size=batch_size)\n",
"test_iter = mx.io.NDArrayIter(X_test, Y_test, batch_size=batch_size)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
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}
],
"source": [
"data = mx.symbol.Variable('data')\n",
"\n",
"x = mx.sym.Convolution(data=data, kernel=(3,3), num_filter=32)\n",
"x = mx.sym.Activation(data=x, act_type=\"relu\")\n",
"x = mx.sym.Pooling(data=x, pool_type=\"max\", kernel=(2,2), stride=(2,2))\n",
"\n",
"x = mx.sym.Convolution(data=x, kernel=(3,3), num_filter=32)\n",
"x = mx.sym.Activation(data=x, act_type=\"relu\")\n",
"x = mx.sym.Pooling(data=x, pool_type=\"max\", kernel=(2,2), stride=(2,2))\n",
"\n",
"x = mx.sym.Flatten(data=x)\n",
"x = mx.sym.FullyConnected(data=x, num_hidden=128)\n",
"x = mx.sym.Activation(data=x, act_type=\"relu\")\n",
"x = mx.sym.Dropout(data=x, p=0.5)\n",
"\n",
"x = mx.sym.FullyConnected(data=x, num_hidden=10)\n",
"x = mx.sym.SoftmaxOutput(data=x, name='softmax')\n",
"\n",
"mx.viz.plot_network(x, shape={\"data\" : (batch_size, 1, 28, 28)})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Start training with [gpu(0)]\n",
"Epoch[0] Batch [256]\tSpeed: 34735.23 samples/sec\tTrain-accuracy=0.113495\n",
"Epoch[0] Resetting Data Iterator\n",
"Epoch[0] Time cost=2.054\n",
"Epoch[0] Validation-accuracy=0.103244\n",
"Epoch[1] Batch [256]\tSpeed: 34652.80 samples/sec\tTrain-accuracy=0.688629\n",
"Epoch[1] Resetting Data Iterator\n",
"Epoch[1] Time cost=1.734\n",
"Epoch[1] Validation-accuracy=0.972013\n",
"Epoch[2] Batch [256]\tSpeed: 30691.36 samples/sec\tTrain-accuracy=0.962738\n",
"Epoch[2] Resetting Data Iterator\n",
"Epoch[2] Time cost=1.858\n",
"Epoch[2] Validation-accuracy=0.982298\n",
"Epoch[3] Batch [256]\tSpeed: 34644.67 samples/sec\tTrain-accuracy=0.975677\n",
"Epoch[3] Resetting Data Iterator\n",
"Epoch[3] Time cost=1.734\n",
"Epoch[3] Validation-accuracy=0.986748\n",
"Epoch[4] Batch [256]\tSpeed: 34769.88 samples/sec\tTrain-accuracy=0.980469\n",
"Epoch[4] Resetting Data Iterator\n",
"Epoch[4] Time cost=1.733\n",
"Epoch[4] Validation-accuracy=0.985562\n",
"Epoch[5] Batch [256]\tSpeed: 34872.41 samples/sec\tTrain-accuracy=0.983337\n",
"Epoch[5] Resetting Data Iterator\n",
"Epoch[5] Time cost=1.735\n",
"Epoch[5] Validation-accuracy=0.987935\n",
"Epoch[6] Batch [256]\tSpeed: 30487.65 samples/sec\tTrain-accuracy=0.984680\n",
"Epoch[6] Resetting Data Iterator\n",
"Epoch[6] Time cost=1.872\n",
"Epoch[6] Validation-accuracy=0.990309\n",
"Epoch[7] Batch [256]\tSpeed: 34931.41 samples/sec\tTrain-accuracy=0.986908\n",
"Epoch[7] Resetting Data Iterator\n",
"Epoch[7] Time cost=1.728\n",
"Epoch[7] Validation-accuracy=0.987935\n",
"Epoch[8] Batch [256]\tSpeed: 34850.90 samples/sec\tTrain-accuracy=0.987793\n",
"Epoch[8] Resetting Data Iterator\n",
"Epoch[8] Time cost=1.733\n",
"Epoch[8] Validation-accuracy=0.989814\n",
"Epoch[9] Batch [256]\tSpeed: 34906.21 samples/sec\tTrain-accuracy=0.988708\n",
"Epoch[9] Resetting Data Iterator\n",
"Epoch[9] Time cost=1.730\n",
"Epoch[9] Validation-accuracy=0.990803\n",
"Epoch[10] Batch [256]\tSpeed: 34792.47 samples/sec\tTrain-accuracy=0.989349\n",
"Epoch[10] Resetting Data Iterator\n",
"Epoch[10] Time cost=1.730\n",
"Epoch[10] Validation-accuracy=0.989517\n",
"Epoch[11] Batch [256]\tSpeed: 33466.07 samples/sec\tTrain-accuracy=0.990601\n",
"Epoch[11] Resetting Data Iterator\n",
"Epoch[11] Time cost=1.772\n",
"Epoch[11] Validation-accuracy=0.991792\n"
]
}
],
"source": [
"model = mx.model.FeedForward(symbol=x, ctx=mx.gpu(0),\n",
" num_epoch=12,\n",
" optimizer='adadelta')\n",
"model.fit(X=train_iter,\n",
" eval_data=test_iter,\n",
" batch_end_callback = mx.callback.Speedometer(batch_size, batch_size*2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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},
"outputs": [],
"source": []
}
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