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"import d2l\n",
"from mxnet import gluon, npx, init\n",
"from mxnet.gluon import nn\n",
"npx.set_np()\n",
"\n",
"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=256)"
]
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"net = nn.Sequential()\n",
"net.add(nn.Dense(256, activation='relu'),\n",
" nn.Dense(10))\n",
"net.initialize(init.Normal(sigma=0.01))"
]
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"Training"
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"end_time": "2019-07-02T21:21:59.272107Z",
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"source": [
"loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
"trainer = gluon.Trainer(net.collect_params(), \n",
" 'sgd', {'learning_rate': 0.5})\n",
"d2l.train_ch3(net, train_iter, test_iter, loss, 10, trainer)"
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