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未验证 提交 5952d7bc 编写于 作者: J jzhang533 提交者： GitHub
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dynamic graph example added (#876)

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 动态图\n",
"\n",
"从飞桨开源框架2.0beta版本开始，飞桨默认为用户开启了动态图模式。在这种模式下，每次执行一个运算，可以立即得到结果（而不是事先定义好网络结构，然后再执行）。\n",
"\n",
"在动态图模式下，您可以更加方便的组织代码，更容易的调试程序，本示例教程将向你介绍飞桨的动态图的使用。\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 设置环境\n",
"\n",
"我们将使用飞桨2.0beta版本，并确认已经开启了动态图模式。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0.0\n",
"edf5f3173a25ae2230e9619ab5426317b4bd7cde\n"
]
}
],
"source": [
"import paddle\n",
"import paddle.nn.functional as F\n",
"import numpy as np\n",
"\n",
"paddle.disable_static()\n",
"print(paddle.__version__)\n",
"print(paddle.__git_commit__)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 基本用法\n",
"\n",
"在动态图模式下，您可以直接运行一个飞桨提供的API，它会立刻返回结果到python。不再需要首先创建一个计算图，然后再给定数据去运行。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-1.4396907 -0.59741247]\n",
" [ 1.4717171 -0.06998838]\n",
" [-1.2790705 0.4278928 ]\n",
" [ 1.1862146 -1.895377 ]]\n",
"[1. 2.]\n",
"[[-0.4396907 1.4025875 ]\n",
" [ 2.4717171 1.9300116 ]\n",
" [-0.2790705 2.4278927 ]\n",
" [ 2.1862144 0.10462296]]\n",
"[-2.6345158 1.3317404 -0.4232849 -2.6045394]\n"
]
}
],
"source": [
"a = paddle.randn([4, 2])\n",
"b = paddle.arange(1, 3, dtype='float32')\n",
"\n",
"print(a.numpy())\n",
"print(b.numpy())\n",
"\n",
"c = a + b\n",
"print(c.numpy())\n",
"\n",
"d = paddle.matmul(a, b)\n",
"print(d.numpy())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 使用python的控制流\n",
"\n",
"动态图模式下，您可以使用python的条件判断和循环，这类控制语句来执行神经网络的计算。（不再需要`cond`, `loop`这类OP)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 +> [5 6 7]\n",
"1 -> [-3 -3 -3]\n",
"2 -> [-3 -1 3]\n",
"3 +> [ 5 13 33]\n",
"4 -> [-3 11 75]\n",
"5 +> [ 5 37 249]\n",
"6 -> [ -3 59 723]\n",
"7 -> [ -3 123 2181]\n",
"8 -> [ -3 251 6555]\n",
"9 -> [ -3 507 19677]\n"
]
}
],
"source": [
"a = paddle.to_tensor(np.array([1, 2, 3]))\n",
"b = paddle.to_tensor(np.array([4, 5, 6]))\n",
"\n",
"for i in range(10):\n",
" r = paddle.rand([1,])\n",
" if r > 0.5:\n",
" c = paddle.pow(a, i) + b\n",
" print(\"{} +> {}\".format(i, c.numpy()))\n",
" else:\n",
" c = paddle.pow(a, i) - b\n",
" print(\"{} -> {}\".format(i, c.numpy()))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 构建更加灵活的网络\n",
"\n",
"- 使用动态图可以用来创建更加灵活的网络，比如根据控制流选择不同的分支网络，和方便的构建权重共享的网络。接下来我们来看一个具体的例子，在这个例子中，第二个线性变换只有0.5的可能性会运行。\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class MyModel(paddle.nn.Layer):\n",
" def __init__(self, input_size, hidden_size):\n",
" super(MyModel, self).__init__()\n",
" self.linear1 = paddle.nn.Linear(input_size, hidden_size)\n",
" self.linear2 = paddle.nn.Linear(hidden_size, hidden_size)\n",
" self.linear3 = paddle.nn.Linear(hidden_size, 1)\n",
"\n",
" def forward(self, inputs):\n",
" x = self.linear1(inputs)\n",
" x = F.relu(x)\n",
"\n",
" if paddle.rand([1,]) > 0.5: \n",
" x = self.linear2(x)\n",
" x = F.relu(x)\n",
"\n",
" x = self.linear3(x)\n",
" \n",
" return x "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 [87.28088]\n",
"200 [57.775795]\n",
"400 [42.70884]\n",
"600 [45.509155]\n",
"800 [29.966158]\n",
"1000 [11.885025]\n",
"1200 [16.888378]\n",
"1400 [3.5780585]\n",
"1600 [5.3149533]\n",
"1800 [4.501356]\n",
"2000 [3.022315]\n",
"2200 [1.7214009]\n",
"2400 [0.3694626]\n",
"2600 [0.31249344]\n",
"2800 [0.1450614]\n"
]
}
],
"source": [
"total_data, batch_size, input_size, hidden_size = 1000, 64, 128, 256\n",
"\n",
"x_data = np.random.randn(total_data, input_size).astype(np.float32)\n",
"y_data = np.random.randn(total_data, 1).astype(np.float32)\n",
"\n",
"model = MyModel(input_size, hidden_size)\n",
"\n",
"loss_fn = paddle.nn.MSELoss(reduction='sum')\n",
"optimizer = paddle.optimizer.SGD(learning_rate=0.0001, \n",
" parameter_list=model.parameters())\n",
"\n",
"for t in range(200 * (total_data // batch_size)):\n",
" idx = np.random.choice(total_data, batch_size, replace=False)\n",
" x = paddle.to_tensor(x_data[idx,:])\n",
" y = paddle.to_tensor(y_data[idx,:])\n",
" y_pred = model(x)\n",
"\n",
" loss = loss_fn(y_pred, y)\n",
" if t % 200 == 0:\n",
" print(t, loss.numpy())\n",
"\n",
" loss.backward()\n",
" optimizer.minimize(loss)\n",
" model.clear_gradients()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The end\n",
"\n",
"可以看到使用动态图带来了更灵活易用的方式来组网和训练。"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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