使用theano的Glorot初始化搭建ResNet网络
时间: 2024-05-15 19:01:29 浏览: 16
好的,下面是使用Theano和Glorot初始化搭建ResNet网络的步骤:
1. 导入必要的库:
```python
import theano
import theano.tensor as T
import numpy as np
from theano.tensor.nnet import conv2d
from theano.tensor.signal import pool
```
2. 定义输入和输出的变量:
```python
X = T.tensor4('X')
y = T.ivector('y')
```
其中 `X` 是输入的图像,`y` 是对应的标签。
3. 定义 ResNet 的块:
```python
def res_block(X, filters, stride=1, identity=True):
F1, F2, F3 = filters
X_shortcut = X
# 第一层卷积
X = conv2d(X, filters=F1, filter_size=(1, 1), subsample=(stride, stride), border_mode='valid')
X = batch_norm(X)
X = activation(X)
# 第二层卷积
X = conv2d(X, filters=F2, filter_size=(3, 3), subsample=(1, 1), border_mode='same')
X = batch_norm(X)
X = activation(X)
# 第三层卷积
X = conv2d(X, filters=F3, filter_size=(1, 1), subsample=(1, 1), border_mode='valid')
X = batch_norm(X)
# 如果输入和输出的维度不一致,则需要使用恒等映射来调整维度
if identity is True:
X_shortcut = conv2d(X_shortcut, filters=F3, filter_size=(1, 1), subsample=(stride, stride), border_mode='valid')
X_shortcut = batch_norm(X_shortcut)
# 恒等映射和卷积层的输出相加
X = activation(X + X_shortcut)
return X
```
4. 定义 ResNet 网络:
```python
def res_net(X, params):
W1, b1, W2, b2, W3, b3, W4, b4, W5, b5 = params
# 第一层卷积
X = conv2d(X, filters=W1, filter_size=(7, 7), subsample=(2, 2), border_mode='same')
X = batch_norm(X)
X = activation(X)
X = pool.pool_2d(X, (3, 3), ignore_border=False, stride=(2, 2), padding=(1, 1))
# 第二层卷积
X = res_block(X, filters=[64, 64, 256], stride=1, identity=False)
X = res_block(X, filters=[64, 64, 256], stride=1, identity=True)
X = res_block(X, filters=[64, 64, 256], stride=1, identity=True)
# 第三层卷积
X = res_block(X, filters=[128, 128, 512], stride=2, identity=False)
X = res_block(X, filters=[128, 128, 512], stride=1, identity=True)
X = res_block(X, filters=[128, 128, 512], stride=1, identity=True)
X = res_block(X, filters=[128, 128, 512], stride=1, identity=True)
# 第四层卷积
X = res_block(X, filters=[256, 256, 1024], stride=2, identity=False)
X = res_block(X, filters=[256, 256, 1024], stride=1, identity=True)
X = res_block(X, filters=[256, 256, 1024], stride=1, identity=True)
X = res_block(X, filters=[256, 256, 1024], stride=1, identity=True)
X = res_block(X, filters=[256, 256, 1024], stride=1, identity=True)
X = res_block(X, filters=[256, 256, 1024], stride=1, identity=True)
# 第五层卷积
X = res_block(X, filters=[512, 512, 2048], stride=2, identity=False)
X = res_block(X, filters=[512, 512, 2048], stride=1, identity=True)
X = res_block(X, filters=[512, 512, 2048], stride=1, identity=True)
# 平均池化层和全连接层
X = pool.pool_2d(X, (7, 7), ignore_border=False)
X = T.flatten(X, outdim=2)
X = T.dot(X, W5) + b5
return X
```
5. 定义辅助函数:
```python
def batch_norm(X):
epsilon = 1e-5
gamma = theano.shared(np.ones((X.shape[1],), dtype=theano.config.floatX), borrow=True)
beta = theano.shared(np.zeros((X.shape[1],), dtype=theano.config.floatX), borrow=True)
mean = T.mean(X, axis=(0, 2, 3), keepdims=True)
variance = T.mean(T.sqr(X - mean), axis=(0, 2, 3), keepdims=True)
X_normalized = (X - mean) / T.sqrt(variance + epsilon)
return gamma.dimshuffle('x', 0, 'x', 'x') * X_normalized + beta.dimshuffle('x', 0, 'x', 'x')
def activation(X):
return T.nnet.relu(X)
def load_params():
W1 = theano.shared(glorot_init((64, 3, 7, 7)), borrow=True)
b1 = theano.shared(np.zeros((64,), dtype=theano.config.floatX), borrow=True)
W2 = theano.shared(glorot_init((64, 64, 3, 3)), borrow=True)
b2 = theano.shared(np.zeros((64,), dtype=theano.config.floatX), borrow=True)
W3 = theano.shared(glorot_init((256, 64, 1, 1)), borrow=True)
b3 = theano.shared(np.zeros((256,), dtype=theano.config.floatX), borrow=True)
W4 = theano.shared(glorot_init((512, 256, 3, 3)), borrow=True)
b4 = theano.shared(np.zeros((512,), dtype=theano.config.floatX), borrow=True)
W5 = theano.shared(glorot_init((1000, 2048)), borrow=True)
b5 = theano.shared(np.zeros((1000,), dtype=theano.config.floatX), borrow=True)
return [W1, b1, W2, b2, W3, b3, W4, b4, W5, b5]
def glorot_init(shape):
fan_in = shape[1] * np.prod(shape[2:])
fan_out = np.prod(shape[1:])
s = np.sqrt(2.0 / (fan_in + fan_out))
return np.random.normal(loc=0.0, scale=s, size=shape).astype(theano.config.floatX)
```
6. 加载数据集和参数,然后进行训练和测试:
```python
# 加载数据集
# ...
# 加载参数
params = load_params()
# 定义损失函数和优化器
y_pred = T.argmax(res_net(X, params), axis=1)
loss = T.mean(T.nnet.categorical_crossentropy(y_pred, y))
updates = theano.function([], [], updates=Adam(loss, params, learning_rate=0.001))
# 训练模型
for epoch in range(10):
for i in range(num_batches):
# 获取批量数据
# ...
# 训练模型
updates()
# 测试模型
# ...
```
以上就是使用 Theano 和 Glorot 初始化搭建 ResNet 网络的步骤。其中 `res_block` 函数定义了 ResNet 的基本块,`res_net` 函数定义了整个 ResNet 网络。在训练模型时,我们使用 Adam 优化器来更新网络参数。