googlenet.py 文件源码

def build_model(self, input_var, forward, dropout):
        net = dict()
        net['input'] = InputLayer((None, 3, None, None), input_var=input_var)
        net['conv1/7x7_s2'] = ConvLayer(
            net['input'], 64, 7, stride=2, pad=3, flip_filters=False)
        net['pool1/3x3_s2'] = PoolLayer(
            net['conv1/7x7_s2'], pool_size=3, stride=2, ignore_border=False)
        net['pool1/norm1'] = LRNLayer(net['pool1/3x3_s2'], alpha=0.00002, k=1)
        net['conv2/3x3_reduce'] = ConvLayer(
            net['pool1/norm1'], 64, 1, flip_filters=False)
        net['conv2/3x3'] = ConvLayer(
            net['conv2/3x3_reduce'], 192, 3, pad=1, flip_filters=False)
        net['conv2/norm2'] = LRNLayer(net['conv2/3x3'], alpha=0.00002, k=1)
        net['pool2/3x3_s2'] = PoolLayerDNN(net['conv2/norm2'], pool_size=3, stride=2)

        net.update(self.build_inception_module('inception_3a',
                                               net['pool2/3x3_s2'],
                                               [32, 64, 96, 128, 16, 32]))
        net.update(self.build_inception_module('inception_3b',
                                               net['inception_3a/output'],
                                               [64, 128, 128, 192, 32, 96]))
        net['pool3/3x3_s2'] = PoolLayerDNN(net['inception_3b/output'],
                                           pool_size=3, stride=2)

        net.update(self.build_inception_module('inception_4a',
                                               net['pool3/3x3_s2'],
                                               [64, 192, 96, 208, 16, 48]))
        net.update(self.build_inception_module('inception_4b',
                                               net['inception_4a/output'],
                                               [64, 160, 112, 224, 24, 64]))
        net.update(self.build_inception_module('inception_4c',
                                               net['inception_4b/output'],
                                               [64, 128, 128, 256, 24, 64]))
        net.update(self.build_inception_module('inception_4d',
                                               net['inception_4c/output'],
                                               [64, 112, 144, 288, 32, 64]))
        net.update(self.build_inception_module('inception_4e',
                                               net['inception_4d/output'],
                                               [128, 256, 160, 320, 32, 128]))
        net['pool4/3x3_s2'] = PoolLayerDNN(net['inception_4e/output'],
                                           pool_size=3, stride=2)

        net.update(self.build_inception_module('inception_5a',
                                               net['pool4/3x3_s2'],
                                               [128, 256, 160, 320, 32, 128]))
        net.update(self.build_inception_module('inception_5b',
                                               net['inception_5a/output'],
                                               [128, 384, 192, 384, 48, 128]))

        net['pool5/7x7_s1'] = GlobalPoolLayer(net['inception_5b/output'])

        if forward:
            #net['fc6'] = DenseLayer(net['pool5/7x7_s1'], num_units=1000)
            net['prob'] = DenseLayer(net['pool5/7x7_s1'], num_units=4, nonlinearity=softmax)
        else:
            net['dropout1'] = DropoutLayer(net['pool5/7x7_s1'], p=dropout)
            #net['fc6'] = DenseLayer(net['dropout1'], num_units=1000)
            #net['dropout2'] = DropoutLayer(net['fc6'], p=dropout)
            net['prob'] = DenseLayer(net['dropout1'], num_units=4, nonlinearity=softmax)
        return net