wehub-resource-sync
279 lines
7.4 KiB
Plaintext
279 lines
7.4 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1.2.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib inline\n",
|
|
"import torch\n",
|
|
"import torch.nn as nn\n",
|
|
"import numpy as np\n",
|
|
"import sys\n",
|
|
"sys.path.append(\"..\") \n",
|
|
"import d2lzh_pytorch as d2l\n",
|
|
"\n",
|
|
"print(torch.__version__)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def dropout(X, drop_prob):\n",
|
|
" X = X.float()\n",
|
|
" assert 0 <= drop_prob <= 1\n",
|
|
" keep_prob = 1 - drop_prob\n",
|
|
" # 这种情况下把全部元素都丢弃\n",
|
|
" if keep_prob == 0:\n",
|
|
" return torch.zeros_like(X)\n",
|
|
" mask = (torch.rand(X.shape) < keep_prob).float()\n",
|
|
" \n",
|
|
" return mask * X / keep_prob"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"tensor([[ 0., 1., 2., 3., 4., 5., 6., 7.],\n",
|
|
" [ 8., 9., 10., 11., 12., 13., 14., 15.]])"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"X = torch.arange(16).view(2, 8)\n",
|
|
"dropout(X, 0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"tensor([[ 0., 0., 4., 6., 0., 0., 12., 14.],\n",
|
|
" [ 0., 18., 20., 22., 0., 0., 28., 0.]])"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"dropout(X, 0.5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"tensor([[0., 0., 0., 0., 0., 0., 0., 0.],\n",
|
|
" [0., 0., 0., 0., 0., 0., 0., 0.]])"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"dropout(X, 1.0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 256, 256\n",
|
|
"\n",
|
|
"W1 = torch.tensor(np.random.normal(0, 0.01, size=(num_inputs, num_hiddens1)), dtype=torch.float, requires_grad=True)\n",
|
|
"b1 = torch.zeros(num_hiddens1, requires_grad=True)\n",
|
|
"W2 = torch.tensor(np.random.normal(0, 0.01, size=(num_hiddens1, num_hiddens2)), dtype=torch.float, requires_grad=True)\n",
|
|
"b2 = torch.zeros(num_hiddens2, requires_grad=True)\n",
|
|
"W3 = torch.tensor(np.random.normal(0, 0.01, size=(num_hiddens2, num_outputs)), dtype=torch.float, requires_grad=True)\n",
|
|
"b3 = torch.zeros(num_outputs, requires_grad=True)\n",
|
|
"\n",
|
|
"params = [W1, b1, W2, b2, W3, b3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"drop_prob1, drop_prob2 = 0.2, 0.5\n",
|
|
"\n",
|
|
"def net(X, is_training=True):\n",
|
|
" X = X.view(-1, num_inputs)\n",
|
|
" H1 = (torch.matmul(X, W1) + b1).relu()\n",
|
|
" if is_training: # 只在训练模型时使用丢弃法\n",
|
|
" H1 = dropout(H1, drop_prob1) # 在第一层全连接后添加丢弃层\n",
|
|
" H2 = (torch.matmul(H1, W2) + b2).relu()\n",
|
|
" if is_training:\n",
|
|
" H2 = dropout(H2, drop_prob2) # 在第二层全连接后添加丢弃层\n",
|
|
" return torch.matmul(H2, W3) + b3"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# def evaluate_accuracy(data_iter, net):\n",
|
|
"# acc_sum, n = 0.0, 0\n",
|
|
"# for X, y in data_iter:\n",
|
|
"# if isinstance(net, torch.nn.Module):\n",
|
|
"# net.eval() # 评估模式, 这会关闭dropout\n",
|
|
"# acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()\n",
|
|
"# net.train() # 改回训练模式\n",
|
|
"# else: # 自定义的模型\n",
|
|
"# if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数\n",
|
|
"# # 将is_training设置成False\n",
|
|
"# acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() \n",
|
|
"# else:\n",
|
|
"# acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() \n",
|
|
"# n += y.shape[0]\n",
|
|
"# return acc_sum / n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch 1, loss 0.0045, train acc 0.561, test acc 0.662\n",
|
|
"epoch 2, loss 0.0023, train acc 0.783, test acc 0.786\n",
|
|
"epoch 3, loss 0.0019, train acc 0.823, test acc 0.773\n",
|
|
"epoch 4, loss 0.0017, train acc 0.838, test acc 0.847\n",
|
|
"epoch 5, loss 0.0016, train acc 0.848, test acc 0.809\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"num_epochs, lr, batch_size = 5, 100.0, 256 # 这里的学习率设置的很大，原因同3.9.6节。\n",
|
|
"loss = torch.nn.CrossEntropyLoss()\n",
|
|
"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n",
|
|
"d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"net = nn.Sequential(\n",
|
|
" d2l.FlattenLayer(),\n",
|
|
" nn.Linear(num_inputs, num_hiddens1),\n",
|
|
" nn.ReLU(),\n",
|
|
" nn.Dropout(drop_prob1),\n",
|
|
" nn.Linear(num_hiddens1, num_hiddens2), \n",
|
|
" nn.ReLU(),\n",
|
|
" nn.Dropout(drop_prob2),\n",
|
|
" nn.Linear(num_hiddens2, 10)\n",
|
|
" )\n",
|
|
"\n",
|
|
"for param in net.parameters():\n",
|
|
" nn.init.normal_(param, mean=0, std=0.01)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch 1, loss 0.0048, train acc 0.526, test acc 0.743\n",
|
|
"epoch 2, loss 0.0023, train acc 0.779, test acc 0.764\n",
|
|
"epoch 3, loss 0.0020, train acc 0.815, test acc 0.819\n",
|
|
"epoch 4, loss 0.0018, train acc 0.836, test acc 0.814\n",
|
|
"epoch 5, loss 0.0016, train acc 0.848, test acc 0.842\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"optimizer = torch.optim.SGD(net.parameters(), lr=0.5)\n",
|
|
"d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.6.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|