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dive-into-dl-pytorch

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提交 9f8cd357 编写于 作者: S ShusenTang
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add more info about ModuleList

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code/chapter04_DL_computation/4.1_model-construction.ipynb
浏览文件 @ 9f8cd357
......@@ -16,7 +16,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0.4.1\n"
"1.2.0\n"
]
}
],
......@@ -78,10 +78,10 @@
{
"data": {
"text/plain": [
"tensor([[ 0.1351, -0.0034, 0.0948, -0.1652, 0.1512, 0.0887, -0.0032, 0.0692,\n",
" 0.0942, 0.0956],\n",
" [ 0.1624, -0.0383, 0.1557, -0.0735, 0.1931, 0.1699, -0.0067, 0.0353,\n",
" 0.1712, 0.1568]], grad_fn=<ThAddmmBackward>)"
"tensor([[ 0.0234, -0.2646, -0.1168, -0.2127, 0.0884, -0.0456, 0.0811, 0.0297,\n",
" 0.2032, 0.1364],\n",
" [ 0.1479, -0.1545, -0.0265, -0.2119, -0.0543, -0.0086, 0.0902, -0.1017,\n",
" 0.1504, 0.1144]], grad_fn=<AddmmBackward>)"
]
},
"execution_count": 3,
......@@ -107,7 +107,9 @@
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MySequential(nn.Module):\n",
......@@ -146,10 +148,10 @@
{
"data": {
"text/plain": [
"tensor([[ 0.1883, -0.1269, -0.1886, 0.0638, -0.1004, -0.0600, 0.0760, -0.1788,\n",
" -0.1844, -0.2131],\n",
" [ 0.1319, -0.0490, -0.1365, 0.0133, -0.0483, -0.0861, 0.0369, -0.0830,\n",
" -0.0462, -0.2066]], grad_fn=<ThAddmmBackward>)"
"tensor([[ 0.1273, 0.1642, -0.1060, 0.1401, 0.0609, -0.0199, -0.0140, -0.0588,\n",
" 0.1765, -0.1296],\n",
" [ 0.0267, 0.1670, -0.0626, 0.0744, 0.0574, 0.0413, 0.1313, -0.1479,\n",
" 0.0932, -0.0615]], grad_fn=<AddmmBackward>)"
]
},
"execution_count": 5,
......@@ -199,6 +201,74 @@
"print(net)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# net(torch.zeros(1, 784)) # 会报NotImplementedError"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MyModule(nn.Module):\n",
" def __init__(self):\n",
" super(MyModule, self).__init__()\n",
" self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])\n",
"\n",
" def forward(self, x):\n",
" # ModuleList can act as an iterable, or be indexed using ints\n",
" for i, l in enumerate(self.linears):\n",
" x = self.linears[i // 2](x) + l(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"net1:\n",
"torch.Size([10, 10])\n",
"torch.Size([10])\n",
"net2:\n"
]
}
],
"source": [
"class Module_ModuleList(nn.Module):\n",
" def __init__(self):\n",
" super(Module_ModuleList, self).__init__()\n",
" self.linears = nn.ModuleList([nn.Linear(10, 10)])\n",
" \n",
"class Module_List(nn.Module):\n",
" def __init__(self):\n",
" super(Module_List, self).__init__()\n",
" self.linears = [nn.Linear(10, 10)]\n",
"\n",
"net1 = Module_ModuleList()\n",
"net2 = Module_List()\n",
"\n",
"print(\"net1:\")\n",
"for p in net1.parameters():\n",
" print(p.size())\n",
"\n",
"print(\"net2:\")\n",
"for p in net2.parameters():\n",
" print(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -208,7 +278,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 10,
"metadata": {},
"outputs": [
{
......@@ -236,6 +306,15 @@
"print(net)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# net(torch.zeros(1, 784)) # 会报NotImplementedError"
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -245,7 +324,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 12,
"metadata": {
"collapsed": true
},
......@@ -275,7 +354,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 13,
"metadata": {},
"outputs": [
{
......@@ -290,10 +369,10 @@
{
"data": {
"text/plain": [
"tensor(12.1594, grad_fn=<SumBackward0>)"
"tensor(0.8907, grad_fn=<SumBackward0>)"
]
},
"execution_count": 9,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
......@@ -307,7 +386,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 14,
"metadata": {},
"outputs": [
{
......@@ -331,10 +410,10 @@
{
"data": {
"text/plain": [
"tensor(0.1509, grad_fn=<SumBackward0>)"
"tensor(-0.4605, grad_fn=<SumBackward0>)"
]
},
"execution_count": 10,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
......@@ -367,7 +446,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
......@@ -381,7 +460,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.2"
}
},
"nbformat": 4,
......
docs/chapter04_DL_computation/4.1_model-construction.md
浏览文件 @ 9f8cd357
......@@ -114,6 +114,7 @@ net = nn.ModuleList([nn.Linear(784, 256), nn.ReLU()])
net.append(nn.Linear(256, 10)) # # 类似List的append操作
print(net[-1]) # 类似List的索引访问
print(net)
# net(torch.zeros(1, 784)) # 会报NotImplementedError
```
输出：
```
......@@ -125,6 +126,55 @@ ModuleList(
)
```
既然`Sequential`和`ModuleList`都可以进行列表化构造网络，那二者区别是什么呢。`ModuleList`仅仅是一个储存各种模块的列表，这些模块之间没有联系也没有顺序（所以不用保证相邻层的输入输出维度匹配），而且没有实现`forward`功能需要自己实现，所以上面执行`net(torch.zeros(1, 784))`会报`NotImplementedError`；而`Sequential`内的模块需要按照顺序排列，要保证相邻层的输入输出大小相匹配，内部`forward`功能已经实现。
`ModuleList`的出现只是让网络定义前向传播时更加灵活，见下面官网的例子。
``` python
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])
def forward(self, x):
# ModuleList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return x
```
另外，`ModuleList`不同于一般的Python的`list`，加入到`ModuleList`里面的所有模块的参数会被自动添加到整个网络中，下面看一个例子对比一下。
``` python
class Module_ModuleList(nn.Module):
def __init__(self):
super(Module_ModuleList, self).__init__()
self.linears = nn.ModuleList([nn.Linear(10, 10)])
class Module_List(nn.Module):
def __init__(self):
super(Module_List, self).__init__()
self.linears = [nn.Linear(10, 10)]
net1 = Module_ModuleList()
net2 = Module_List()
print("net1:")
for p in net1.parameters():
print(p.size())
print("net2:")
for p in net2.parameters():
print(p)
```
输出：
```
net1:
torch.Size([10, 10])
torch.Size([10])
net2:
```
### 4.1.2.3 `ModuleDict`类
`ModuleDict`接收一个子模块的字典作为输入, 然后也可以类似字典那样进行添加访问操作:
``` python
......@@ -136,6 +186,7 @@ net['output'] = nn.Linear(256, 10) # 添加
print(net['linear']) # 访问
print(net.output)
print(net)
# net(torch.zeros(1, 784)) # 会报NotImplementedError
```
输出：
```
......@@ -148,6 +199,7 @@ ModuleDict(
)
```
和`ModuleList`一样，`ModuleDict`实例仅仅是存放了一些模块的字典，并没有定义`forward`函数需要自己定义。同样，`ModuleDict`也与Python的`Dict`有所不同，`ModuleDict`里的所有模块的参数会被自动添加到整个网络中。
## 4.1.3 构造复杂的模型
......@@ -230,6 +282,7 @@ tensor(14.4908, grad_fn=<SumBackward0>)
* 可以通过继承`Module`类来构造模型。
* `Sequential`、`ModuleList`、`ModuleDict`类都继承自`Module`类。
* 与`Sequential`不同，`ModuleList`和`ModuleDict`并没有定义一个完整的网络，它们只是将不同的模块存放在一起，需要自己定义`forward`函数。
* 虽然`Sequential`等类可以使模型构造更加简单，但直接继承`Module`类可以极大地拓展模型构造的灵活性。
......
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