使用theano实现3DCNN的非监督学习

实现3DCNN的非监督学习，可以使用自编码器（Autoencoder）来实现。自编码器是一种无监督学习算法，可以用于特征提取和数据降维。

以下是使用Theano实现3DCNN自编码器的示例代码：

import theano
import theano.tensor as T
import numpy as np

class ConvLayer(object):
    def __init__(self, rng, input_shape, filter_shape, poolsize=(2, 2)):
        fan_in = np.prod(filter_shape[1:])
        fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / np.prod(poolsize))
        w_bound = np.sqrt(6. / (fan_in + fan_out))

        self.W = theano.shared(
            np.asarray(
                rng.uniform(low=-w_bound, high=w_bound, size=filter_shape),
                dtype=theano.config.floatX
            ),
            borrow=True
        )

        b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, borrow=True)

        self.input = T.dtensor5('input')
        conv_out = T.nnet.conv3d(
            input=self.input,
            filters=self.W,
            filter_shape=filter_shape,
            input_shape=input_shape
        )

        pooled_out = T.signal.pool.pool_3d(
            input=conv_out,
            ws=poolsize,
            ignore_border=True
        )

        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x', 'x'))

        self.params = [self.W, self.b]

class DeconvLayer(object):
    def __init__(self, rng, input_shape, filter_shape, output_shape, poolsize=(2, 2)):
        fan_in = np.prod(filter_shape[1:])
        fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / np.prod(poolsize))
        w_bound = np.sqrt(6. / (fan_in + fan_out))

        self.W = theano.shared(
            np.asarray(
                rng.uniform(low=-w_bound, high=w_bound, size=filter_shape),
                dtype=theano.config.floatX
            ),
            borrow=True
        )

        b_values = np.zeros((filter_shape[1],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, borrow=True)

        self.input = T.dtensor5('input')
        conv_out = T.nnet.abstract_conv.AbstractConv3d_gradInputs(
            filters=self.W.dimshuffle(1, 0, 2, 3, 4),
            output_grad=self.input,
            input_shape=output_shape,
            filter_shape=filter_shape,
            border_mode='valid'
        )

        unpooled_out = T.signal.pool.pool_3d(
            input=conv_out,
            ws=poolsize,
            ignore_border=True
        )

        self.output = T.tanh(unpooled_out + self.b.dimshuffle('x', 0, 'x', 'x', 'x'))

        self.params = [self.W, self.b]

class AutoEncoder(object):
    def __init__(self, input_shape, filter_sizes, pool_sizes):
        rng = np.random.RandomState(1234)
        self.encoder_layers = []
        self.decoder_layers = []
        self.params = []

        layer_input_shape = input_shape
        for filter_size, pool_size in zip(filter_sizes, pool_sizes):
            encoder_layer = ConvLayer(rng=rng,
                                      input_shape=layer_input_shape,
                                      filter_shape=filter_size,
                                      poolsize=pool_size)
            self.encoder_layers.append(encoder_layer)
            self.params += encoder_layer.params
            layer_input_shape = (
                layer_input_shape[0],
                filter_size[0],
                (layer_input_shape[2] - filter_size[2] + 1) // pool_size[0],
                (layer_input_shape[3] - filter_size[3] + 1) // pool_size[1],
                (layer_input_shape[4] - filter_size[4] + 1) // pool_size[2]
            )

        for filter_size, pool_size, encoder_layer in zip(filter_sizes[::-1][1:], pool_sizes[::-1][1:], self.encoder_layers[::-1][1:]):
            decoder_layer = DeconvLayer(rng=rng,
                                        input_shape=layer_input_shape,
                                        filter_shape=filter_size,
                                        output_shape=encoder_layer.output.shape,
                                        poolsize=pool_size)
            self.decoder_layers.append(decoder_layer)
            self.params += decoder_layer.params
            layer_input_shape = encoder_layer.output.shape

        self.encode = theano.function(
            [self.encoder_layers[0].input],
            self.encoder_layers[-1].output
        )

        self.decode = theano.function(
            [self.decoder_layers[0].input],
            self.decoder_layers[-1].output
        )

        self.reconstruct = theano.function(
            [self.encoder_layers[0].input],
            self.decoder_layers[-1].output
        )

        x = self.encoder_layers[0].input
        for layer in self.encoder_layers:
            x = layer.output
        self.encoder = theano.function(
            [self.encoder_layers[0].input],
            x
        )

        x = self.decoder_layers[0].input
        for layer in self.decoder_layers:
            x = layer.output
        self.decoder = theano.function(
            [self.decoder_layers[0].input],
            x
        )

        self.params = list(set(self.params))

        cost = T.mean((self.decoder_layers[-1].output - self.encoder_layers[0].input) ** 2)
        gparams = T.grad(cost, self.params)
        updates = [(param, param - 0.01 * gparam) for param, gparam in zip(self.params, gparams)]

        self.train = theano.function(
            [self.encoder_layers[0].input],
            cost,
            updates=updates
        )

if __name__ == '__main__':
    input_shape = (10, 1, 64, 64, 64)
    filter_sizes = [(32, 1, 3, 3, 3), (64, 32, 3, 3, 3), (128, 64, 3, 3, 3)]
    pool_sizes = [(2, 2, 2), (2, 2, 2), (2, 2, 2)]
    ae = AutoEncoder(input_shape, filter_sizes, pool_sizes)

该代码实现了一个带有三个卷积层和三个反卷积层的3D自编码器，用于对输入数据进行特征提取和降维。您可以根据需要更改输入形状、卷积层和反卷积层的数量和大小，以适应您的应用场景。