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"end_time": "2019-07-03T22:21:55.309728Z",
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"import d2l\n",
"from mxnet import gluon, np, npx\n",
"from mxnet.gluon import nn\n",
"npx.set_np()\n",
"\n",
"train_iter, test_iter = d2l.load_data_fashion_mnist(\n",
" batch_size=128, resize=224)"
]
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"execution_count": 2,
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"end_time": "2019-07-03T22:21:55.320094Z",
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"def vgg_block(num_convs, num_channels):\n",
" blk = nn.Sequential()\n",
" for _ in range(num_convs):\n",
" blk.add(nn.Conv2D(num_channels, kernel_size=3,\n",
" padding=1, activation='relu'))\n",
" blk.add(nn.MaxPool2D(pool_size=2, strides=2))\n",
" return blk"
]
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"slide_type": "slide"
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"source": [
"The model"
]
},
{
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"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:21:55.336383Z",
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"conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))\n",
"\n",
"def vgg(conv_arch):\n",
" net = nn.Sequential()\n",
" # The convolutional layer part\n",
" for (num_convs, num_channels) in conv_arch:\n",
" net.add(vgg_block(num_convs, num_channels))\n",
" # The fully connected layer part\n",
" net.add(nn.Dense(4096, activation='relu'), nn.Dropout(0.5),\n",
" nn.Dense(4096, activation='relu'), nn.Dropout(0.5),\n",
" nn.Dense(10))\n",
" return net\n",
"\n",
"net = vgg(conv_arch)"
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"source": [
"Training with narrow version"
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"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:28:14.142303Z",
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"name": "stdout",
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"text": [
"loss 0.177, train acc 0.935, test acc 0.925\n",
"9350.4 exampes/sec on gpu(0)\n"
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"ratio = 4\n",
"small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]\n",
"net = vgg(small_conv_arch)\n",
"d2l.train_ch5(net, train_iter, test_iter, num_epochs=10, lr=0.05)"
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}
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