- 阅读权限
- 255
- 威望
- 1 级
- 论坛币
- 49635 个
- 通用积分
- 55.7537
- 学术水平
- 370 点
- 热心指数
- 273 点
- 信用等级
- 335 点
- 经验
- 57805 点
- 帖子
- 4005
- 精华
- 21
- 在线时间
- 582 小时
- 注册时间
- 2005-5-8
- 最后登录
- 2023-11-26
|
Recurrent neural networks using Python Blocks
- >>> import numpy
- >>> import theano
- >>> from theano import tensor
- >>> from blocks import initialization
- >>> from blocks.bricks import Identity
- >>> from blocks.bricks.recurrent import SimpleRecurrent
- >>> x = tensor.tensor3('x')
- >>> rnn = SimpleRecurrent(
- ... dim=3, activation=Identity(), weights_init=initialization.Identity())
- >>> rnn.initialize()
- >>> h = rnn.apply(x)
- >>> f = theano.function([x], h)
- >>> print(f(numpy.ones((3, 1, 3), dtype=theano.config.floatX)))
- [[[ 1. 1. 1.]]
- [[ 2. 2. 2.]]
- [[ 3. 3. 3.]]]...
复制代码- >>> from blocks.bricks import Linear
- >>> doubler = Linear(
- ... input_dim=3, output_dim=3, weights_init=initialization.Identity(2),
- ... biases_init=initialization.Constant(0))
- >>> doubler.initialize()
- >>> h_doubler = rnn.apply(doubler.apply(x))
- >>> f = theano.function([x], h_doubler)
- >>> print(f(numpy.ones((3, 1, 3), dtype=theano.config.floatX)))
- [[[ 2. 2. 2.]]
- [[ 4. 4. 4.]]
- [[ 6. 6. 6.]]]...
复制代码
|
|