LeNet.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mxnet import nd, autograd, init, gluon, context\n",
    "from mxnet.gluon import nn\n",
    "from mxnet.gluon.data.vision import transforms\n",
    "import d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataloader(batch_size):\n",
    "    transformer = transforms.Compose([\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    "    train = gluon.data.vision.datasets.FashionMNIST(train=True)\n",
    "    train = train.transform_first(transformer)\n",
    "    train_iter = gluon.data.DataLoader(train, batch_size, shuffle=True, num_workers=4)\n",
    "    test = gluon.data.vision.datasets.FashionMNIST(train=False)\n",
    "    test = test.transform_first(transformer)\n",
    "    test_iter = gluon.data.DataLoader(test, batch_size, shuffle=False, num_workers=4)\n",
    "    return train_iter, test_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_images(X, nrows, ncols):\n",
    "    _, axes = plt.subplots(nrows, ncols)\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip(X, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_acc(net, data_iter, ctx):\n",
    "    acc = 0\n",
    "    size = 0\n",
    "    for X, y in data_iter:\n",
    "        X, y = X.as_in_context(ctx), y.as_in_context(ctx)\n",
    "        result = net(X).softmax(axis=1)\n",
    "        acc += (result.argmax(axis=1) == y.astype('float32')).sum().asscalar()\n",
    "        size += len(y)\n",
    "        \n",
    "    return acc / size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_loss(net, data_iter, loss, ctx):\n",
    "    l = 0\n",
    "    size = 0\n",
    "    for X, y in data_iter:\n",
    "        X, y = X.as_in_context(ctx), y.as_in_context(ctx)\n",
    "        result = net(X)\n",
    "        l += loss(result, y).sum().asscalar()\n",
    "        size += len(y)\n",
    "    return l / size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet(nn.Block):\n",
    "    def __init__(self, **kwargs):\n",
    "        super(LeNet, self).__init__(**kwargs)\n",
    "        self.conv1 = nn.Conv2D(channels=6, kernel_size=5, padding=2, activation='sigmoid')\n",
    "        self.dropout1 = nn.Dropout(0.2)\n",
    "        self.pool1 = nn.AvgPool2D(pool_size=2)\n",
    "        self.conv2 = nn.Conv2D(channels=16, kernel_size=5, activation='sigmoid')\n",
    "        self.dropout2 = nn.Dropout(0.4)\n",
    "        self.pool2 = nn.AvgPool2D(pool_size=2)\n",
    "        self.dense1 = nn.Dense(120, activation='sigmoid')\n",
    "        self.dense2 = nn.Dense(84, activation='sigmoid')\n",
    "        self.dense3 = nn.Dense(10)\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.dropout1(x)\n",
    "        x = self.pool1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.dropout2(x)\n",
    "        x = self.pool2(x)\n",
    "        x = self.dense1(x)\n",
    "        x = self.dense2(x)\n",
    "        out = self.dense3(x)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "[[ 0.48523092 -0.27256382 -0.572021   -0.08011679  0.58898777  0.08744141\n",
       "  -0.5186908   0.7853493  -0.12819459  0.67557424]]\n",
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   "source": [
    "batch_size = 256\n",
    "train_iter, test_iter = get_dataloader(batch_size)\n",
    "\n",
    "ctx = context.gpu(0)\n",
    "\n",
    "net = LeNet()\n",
    "net.initialize(init=init.Xavier(), ctx=ctx)\n",
    "testX = nd.random.normal(shape=(1, 1, 28, 28), ctx=ctx)\n",
    "net(testX)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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   "source": [
    "loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate':0.03})\n",
    "epochs = 100\n",
    "animator = d2l.Animator(xlabel='epoch', xlim=[0, epochs], legend=['loss', 'acc'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
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   "source": [
    "for epoch in range(epochs):\n",
    "    for X, y in train_iter:\n",
    "        X, y = X.as_in_context(ctx), y.as_in_context(ctx)\n",
    "        with autograd.record():\n",
    "            result = net(X)\n",
    "            l = loss(result, y)\n",
    "        l.backward()\n",
    "        trainer.step(batch_size)\n",
    "    epoch_loss = eval_loss(net, test_iter, loss, ctx)\n",
    "    epoch_acc = eval_acc(net, test_iter, ctx)\n",
    "    animator.add(epoch, (epoch_loss, epoch_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for X, y in train_iter:\n",
    "    print(X.shape)\n",
    "    print(y.shape)\n",
    "    show_images(X.squeeze(axis=1).asnumpy(), 2, 5)\n",
    "    break"
   ]
  }
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