Python layers.DropoutLayer方法代码示例

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def create_network():
    l = 1000
    pool_size = 5
    test_size1 = 13
    test_size2 = 7
    test_size3 = 5
    kernel1 = 128
    kernel2 = 128
    kernel3 = 128
    layer1 = InputLayer(shape=(None, 1, 4, l+1024))
    layer2_1 = SliceLayer(layer1, indices=slice(0, l), axis = -1)
    layer2_2 = SliceLayer(layer1, indices=slice(l, None), axis = -1)
    layer2_3 = SliceLayer(layer2_2, indices = slice(0,4), axis = -2)
    layer2_f = FlattenLayer(layer2_3)
    layer3 = Conv2DLayer(layer2_1,num_filters = kernel1, filter_size = (4,test_size1))
    layer4 = Conv2DLayer(layer3,num_filters = kernel1, filter_size = (1,test_size1))
    layer5 = Conv2DLayer(layer4,num_filters = kernel1, filter_size = (1,test_size1))
    layer6 = MaxPool2DLayer(layer5, pool_size = (1,pool_size))
    layer7 = Conv2DLayer(layer6,num_filters = kernel2, filter_size = (1,test_size2))
    layer8 = Conv2DLayer(layer7,num_filters = kernel2, filter_size = (1,test_size2))
    layer9 = Conv2DLayer(layer8,num_filters = kernel2, filter_size = (1,test_size2))
    layer10 = MaxPool2DLayer(layer9, pool_size = (1,pool_size))
    layer11 = Conv2DLayer(layer10,num_filters = kernel3, filter_size = (1,test_size3))
    layer12 = Conv2DLayer(layer11,num_filters = kernel3, filter_size = (1,test_size3))
    layer13 = Conv2DLayer(layer12,num_filters = kernel3, filter_size = (1,test_size3))
    layer14 = MaxPool2DLayer(layer13, pool_size = (1,pool_size))
    layer14_d = DenseLayer(layer14, num_units= 256)
    layer3_2 = DenseLayer(layer2_f, num_units = 128)
    layer15 = ConcatLayer([layer14_d,layer3_2])
    layer16 = DropoutLayer(layer15,p=0.5)
    layer17 = DenseLayer(layer16, num_units=256)
    network = DenseLayer(layer17, num_units= 2, nonlinearity=softmax)
    return network


#random search to initialize the weights 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def create_network():
    l = 1000
    pool_size = 5
    test_size1 = 13
    test_size2 = 7
    test_size3 = 5
    kernel1 = 128
    kernel2 = 128
    kernel3 = 128
    layer1 = InputLayer(shape=(None, 1, 4, l+1024))
    layer2_1 = SliceLayer(layer1, indices=slice(0, l), axis = -1)
    layer2_2 = SliceLayer(layer1, indices=slice(l, None), axis = -1)
    layer2_3 = SliceLayer(layer2_2, indices = slice(0,4), axis = -2)
    layer2_f = FlattenLayer(layer2_3)
    layer3 = Conv2DLayer(layer2_1,num_filters = kernel1, filter_size = (4,test_size1))
    layer4 = Conv2DLayer(layer3,num_filters = kernel1, filter_size = (1,test_size1))
    layer5 = Conv2DLayer(layer4,num_filters = kernel1, filter_size = (1,test_size1))
    layer6 = MaxPool2DLayer(layer5, pool_size = (1,pool_size))
    layer7 = Conv2DLayer(layer6,num_filters = kernel2, filter_size = (1,test_size2))
    layer8 = Conv2DLayer(layer7,num_filters = kernel2, filter_size = (1,test_size2))
    layer9 = Conv2DLayer(layer8,num_filters = kernel2, filter_size = (1,test_size2))
    layer10 = MaxPool2DLayer(layer9, pool_size = (1,pool_size))
    layer11 = Conv2DLayer(layer10,num_filters = kernel3, filter_size = (1,test_size3))
    layer12 = Conv2DLayer(layer11,num_filters = kernel3, filter_size = (1,test_size3))
    layer13 = Conv2DLayer(layer12,num_filters = kernel3, filter_size = (1,test_size3))
    layer14 = MaxPool2DLayer(layer13, pool_size = (1,pool_size))
    layer14_d = DenseLayer(layer14, num_units= 256)
    layer3_2 = DenseLayer(layer2_f, num_units = 128)
    layer15 = ConcatLayer([layer14_d,layer3_2])
    #layer16 = DropoutLayer(layer15,p=0.5)
    layer17 = DenseLayer(layer15, num_units=256)
    network = DenseLayer(layer17, num_units= 1, nonlinearity=None)
    return network


#random search to initialize the weights 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def classificationBranch(net, kernel_size):

    # Post Convolution
    branch = l.batch_norm(l.Conv2DLayer(net,
                        num_filters=int(FILTERS[-1] * RESNET_K),
                        filter_size=kernel_size,
                        nonlinearity=nl.rectify))

    #log.p(("\t\tPOST  CONV SHAPE:", l.get_output_shape(branch), "LAYER:", len(l.get_all_layers(branch)) - 1))

    # Dropout Layer
    branch = l.DropoutLayer(branch)
    
    # Dense Convolution
    branch = l.batch_norm(l.Conv2DLayer(branch,
                        num_filters=int(FILTERS[-1] * RESNET_K * 2),
                        filter_size=1,
                        nonlinearity=nl.rectify))

    #log.p(("\t\tDENSE CONV SHAPE:", l.get_output_shape(branch), "LAYER:", len(l.get_all_layers(branch)) - 1))
    
    # Dropout Layer
    branch = l.DropoutLayer(branch)
    
    # Class Convolution
    branch = l.Conv2DLayer(branch,
                        num_filters=len(cfg.CLASSES),
                        filter_size=1,
                        nonlinearity=None)
    return branch 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model(self, input_dim):
        l_in = InputLayer(shape=(self.batch_size, input_dim))

        l_hidden1 = DenseLayer(l_in, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden1_dropout = DropoutLayer(l_hidden1, p=self.dropout)

        l_hidden2 = DenseLayer(l_hidden1_dropout, num_units=self.n_hidden / 2, nonlinearity=rectify)
        l_hidden2_dropout = DropoutLayer(l_hidden2, p=self.dropout)

        l_hidden3 = DenseLayer(l_hidden2_dropout, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden3_dropout = DropoutLayer(l_hidden3, p=self.dropout)

        l_out = DenseLayer(l_hidden3_dropout, num_units=self.n_classes_, nonlinearity=softmax)

        return l_out 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model(self, input_dim):
        l_in = InputLayer(shape=(self.batch_size, input_dim))

        l_hidden1 = DenseLayer(l_in, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden1_dropout = DropoutLayer(l_hidden1, p=self.dropout)

        l_hidden2 = DenseLayer(l_hidden1_dropout, num_units=self.n_hidden / 2, nonlinearity=rectify)
        l_hidden2_dropout = DropoutLayer(l_hidden2, p=self.dropout)

        l_hidden3 = DenseLayer(l_hidden2_dropout, num_units=self.n_hidden / 4, nonlinearity=rectify)
        l_hidden3_dropout = DropoutLayer(l_hidden3, p=self.dropout)

        l_out = DenseLayer(l_hidden3_dropout, num_units=self.n_classes_, nonlinearity=softmax)

        return l_out 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model(self, input_dim):
        l_in = InputLayer(shape=(self.batch_size, input_dim))

        l_hidden1 = DenseLayer(l_in, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden1_dropout = DropoutLayer(l_hidden1, p=self.dropout)

        l_hidden2 = DenseLayer(l_hidden1_dropout, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden2_dropout = DropoutLayer(l_hidden2, p=self.dropout)

        l_hidden3 = DenseLayer(l_hidden2_dropout, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden3_dropout = DropoutLayer(l_hidden3, p=self.dropout)

        l_out = DenseLayer(l_hidden3_dropout, num_units=self.n_classes_, nonlinearity=softmax)

        return l_out 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model(self, input_dim):
        l_in = InputLayer(shape=(self.batch_size, input_dim))

        l_hidden1 = DenseLayer(l_in, num_units=self.n_hidden / 2, nonlinearity=rectify)
        l_hidden1_dropout = DropoutLayer(l_hidden1, p=self.dropout)

        l_hidden2 = DenseLayer(l_hidden1_dropout, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden2_dropout = DropoutLayer(l_hidden2, p=self.dropout)

        l_hidden3 = DenseLayer(l_hidden2_dropout, num_units=self.n_hidden / 2, nonlinearity=rectify)
        l_hidden3_dropout = DropoutLayer(l_hidden3, p=self.dropout)

        l_out = DenseLayer(l_hidden3_dropout, num_units=self.n_classes_, nonlinearity=softmax)

        return l_out 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model(self, input_dim):
        l_in = InputLayer(shape=(self.batch_size, input_dim))

        l_hidden1 = DenseLayer(l_in, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden1_dropout = DropoutLayer(l_hidden1, p=self.dropout)

        l_hidden2 = DenseLayer(l_hidden1_dropout, num_units=self.n_hidden, nonlinearity=rectify)
        l_hidden2_dropout = DropoutLayer(l_hidden2, p=self.dropout)

        l_out = DenseLayer(l_hidden2_dropout, num_units=self.n_classes_, nonlinearity=softmax)

        return l_out 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model(input_var):
    net = {}
    net['input'] = InputLayer((None, 3, 224, 224), input_var=input_var)
    net['conv1_1'] = ConvLayer(net['input'], 64, 3, pad=1, flip_filters=False)
    net['conv1_2'] = ConvLayer(net['conv1_1'], 64, 3, pad=1, flip_filters=False)
    net['pool1'] = PoolLayer(net['conv1_2'], 2)
    net['conv2_1'] = ConvLayer(net['pool1'], 128, 3, pad=1, flip_filters=False)
    net['conv2_2'] = ConvLayer(net['conv2_1'], 128, 3, pad=1, flip_filters=False)
    net['pool2'] = PoolLayer(net['conv2_2'], 2)
    net['conv3_1'] = ConvLayer(net['pool2'], 256, 3, pad=1, flip_filters=False)
    net['conv3_2'] = ConvLayer(net['conv3_1'], 256, 3, pad=1, flip_filters=False)
    net['conv3_3'] = ConvLayer(net['conv3_2'], 256, 3, pad=1, flip_filters=False)
    net['pool3'] = PoolLayer(net['conv3_3'], 2)
    net['conv4_1'] = ConvLayer(net['pool3'], 512, 3, pad=1, flip_filters=False)
    net['conv4_2'] = ConvLayer(net['conv4_1'], 512, 3, pad=1, flip_filters=False)
    net['conv4_3'] = ConvLayer(net['conv4_2'], 512, 3, pad=1, flip_filters=False)
    net['pool4'] = PoolLayer(net['conv4_3'], 2)
    net['conv5_1'] = ConvLayer(net['pool4'], 512, 3, pad=1, flip_filters=False)
    net['conv5_2'] = ConvLayer(net['conv5_1'], 512, 3, pad=1, flip_filters=False)
    net['conv5_3'] = ConvLayer(net['conv5_2'], 512, 3, pad=1, flip_filters=False)
    net['pool5'] = PoolLayer(net['conv5_3'], 2)
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
    net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
    net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096)
    net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
    net['fc8'] = DenseLayer(net['fc7_dropout'], num_units=1000, nonlinearity=None)
    net['prob'] = NonlinearityLayer(net['fc8'], softmax)

    return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def __create_toplogy__(self, input_var_first=None, input_var_second=None):
        # define network topology
        if (self.conf.rep % 2 != 0):
            raise ValueError("Representation size should be divisible by two as it's formed by combining two crossmodal translations", self.conf.rep)

        # input layers
        l_in_first  = InputLayer(shape=(self.conf.batch_size, self.conf.mod1size), input_var=input_var_first)
        l_in_second = InputLayer(shape=(self.conf.batch_size, self.conf.mod2size), input_var=input_var_second)

        # first -> second
        l_hidden1_first   = DenseLayer(l_in_first, num_units=self.conf.hdn, nonlinearity=self.conf.act, W=GlorotUniform())         # enc1
        l_hidden2_first   = DenseLayer(l_hidden1_first, num_units=self.conf.rep//2, nonlinearity=self.conf.act, W=GlorotUniform()) # enc2
        l_hidden2_first_d = DropoutLayer(l_hidden2_first, p=self.conf.dropout)
        l_hidden3_first   = DenseLayer(l_hidden2_first_d, num_units=self.conf.hdn, nonlinearity=self.conf.act, W=GlorotUniform())    # dec1
        l_out_first       = DenseLayer(l_hidden3_first, num_units=self.conf.mod2size, nonlinearity=self.conf.act, W=GlorotUniform()) # dec2

        if self.conf.untied:
            # FREE
            l_hidden1_second   = DenseLayer(l_in_second, num_units=self.conf.hdn, nonlinearity=self.conf.act, W=GlorotUniform())         # enc1
            l_hidden2_second   = DenseLayer(l_hidden1_second, num_units=self.conf.rep//2, nonlinearity=self.conf.act, W=GlorotUniform()) # enc2
            l_hidden2_second_d = DropoutLayer(l_hidden2_second, p=self.conf.dropout)
            l_hidden3_second   = DenseLayer(l_hidden2_second_d, num_units=self.conf.hdn, nonlinearity=self.conf.act, W=GlorotUniform())    # dec1
            l_out_second       = DenseLayer(l_hidden3_second, num_units=self.conf.mod1size, nonlinearity=self.conf.act, W=GlorotUniform()) # dec2
        else:
            # TIED middle
            l_hidden1_second   = DenseLayer(l_in_second, num_units=self.conf.hdn, nonlinearity=self.conf.act, W=GlorotUniform())             # enc1
            l_hidden2_second   = DenseLayer(l_hidden1_second, num_units=self.conf.rep//2, nonlinearity=self.conf.act, W=l_hidden3_first.W.T) # enc2
            l_hidden2_second_d = DropoutLayer(l_hidden2_second, p=self.conf.dropout)
            l_hidden3_second   = DenseLayer(l_hidden2_second_d, num_units=self.conf.hdn, nonlinearity=self.conf.act, W=l_hidden2_first.W.T) # dec1
            l_out_second       = DenseLayer(l_hidden3_second, num_units=self.conf.mod1size, nonlinearity=self.conf.act, W=GlorotUniform())  # dec2

        l_out = concat([l_out_first, l_out_second])

        return l_out, l_hidden2_first, l_hidden2_second 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model():
    net = {}
    net['input'] = InputLayer((None, 3, 32, 32))
    net['conv1'] = ConvLayer(net['input'],
                             num_filters=192,
                             filter_size=5,
                             pad=2,
                             flip_filters=False)
    net['cccp1'] = ConvLayer(
        net['conv1'], num_filters=160, filter_size=1, flip_filters=False)
    net['cccp2'] = ConvLayer(
        net['cccp1'], num_filters=96, filter_size=1, flip_filters=False)
    net['pool1'] = PoolLayer(net['cccp2'],
                             pool_size=3,
                             stride=2,
                             mode='max',
                             ignore_border=False)
    net['drop3'] = DropoutLayer(net['pool1'], p=0.5)
    net['conv2'] = ConvLayer(net['drop3'],
                             num_filters=192,
                             filter_size=5,
                             pad=2,
                             flip_filters=False)
    net['cccp3'] = ConvLayer(
        net['conv2'], num_filters=192, filter_size=1, flip_filters=False)
    net['cccp4'] = ConvLayer(
        net['cccp3'], num_filters=192, filter_size=1, flip_filters=False)
    net['pool2'] = PoolLayer(net['cccp4'],
                             pool_size=3,
                             stride=2,
                             mode='average_exc_pad',
                             ignore_border=False)
    net['drop6'] = DropoutLayer(net['pool2'], p=0.5)
    net['conv3'] = ConvLayer(net['drop6'],
                             num_filters=192,
                             filter_size=3,
                             pad=1,
                             flip_filters=False)
    net['cccp5'] = ConvLayer(
        net['conv3'], num_filters=192, filter_size=1, flip_filters=False)
    net['cccp6'] = ConvLayer(
        net['cccp5'], num_filters=10, filter_size=1, flip_filters=False)
    net['pool3'] = PoolLayer(net['cccp6'],
                             pool_size=8,
                             mode='average_exc_pad',
                             ignore_border=False)
    net['output'] = FlattenLayer(net['pool3'])

    return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model():
    net = {}

    net['input'] = InputLayer((None, 3, 224, 224))
    net['conv1'] = ConvLayer(net['input'],
                             num_filters=96,
                             filter_size=7,
                             stride=2,
                             flip_filters=False)
    # caffe has alpha = alpha * pool_size
    net['norm1'] = NormLayer(net['conv1'], alpha=0.0001)
    net['pool1'] = PoolLayer(net['norm1'],
                             pool_size=3,
                             stride=3,
                             ignore_border=False)
    net['conv2'] = ConvLayer(net['pool1'],
                             num_filters=256,
                             filter_size=5,
                             flip_filters=False)
    net['pool2'] = PoolLayer(net['conv2'],
                             pool_size=2,
                             stride=2,
                             ignore_border=False)
    net['conv3'] = ConvLayer(net['pool2'],
                             num_filters=512,
                             filter_size=3,
                             pad=1,
                             flip_filters=False)
    net['conv4'] = ConvLayer(net['conv3'],
                             num_filters=512,
                             filter_size=3,
                             pad=1,
                             flip_filters=False)
    net['conv5'] = ConvLayer(net['conv4'],
                             num_filters=512,
                             filter_size=3,
                             pad=1,
                             flip_filters=False)
    net['pool5'] = PoolLayer(net['conv5'],
                             pool_size=3,
                             stride=3,
                             ignore_border=False)
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
    net['drop6'] = DropoutLayer(net['fc6'], p=0.5)
    net['fc7'] = DenseLayer(net['drop6'], num_units=4096)
    net['drop7'] = DropoutLayer(net['fc7'], p=0.5)
    net['fc8'] = DenseLayer(net['drop7'], num_units=1000, nonlinearity=None)
    net['prob'] = NonlinearityLayer(net['fc8'], softmax)

    return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model():
    net = {}
    net['input'] = InputLayer((None, 3, 224, 224))
    net['conv1_1'] = ConvLayer(
        net['input'], 64, 3, pad=1, flip_filters=False)
    net['conv1_2'] = ConvLayer(
        net['conv1_1'], 64, 3, pad=1, flip_filters=False)
    net['pool1'] = PoolLayer(net['conv1_2'], 2)
    net['conv2_1'] = ConvLayer(
        net['pool1'], 128, 3, pad=1, flip_filters=False)
    net['conv2_2'] = ConvLayer(
        net['conv2_1'], 128, 3, pad=1, flip_filters=False)
    net['pool2'] = PoolLayer(net['conv2_2'], 2)
    net['conv3_1'] = ConvLayer(
        net['pool2'], 256, 3, pad=1, flip_filters=False)
    net['conv3_2'] = ConvLayer(
        net['conv3_1'], 256, 3, pad=1, flip_filters=False)
    net['conv3_3'] = ConvLayer(
        net['conv3_2'], 256, 3, pad=1, flip_filters=False)
    net['pool3'] = PoolLayer(net['conv3_3'], 2)
    net['conv4_1'] = ConvLayer(
        net['pool3'], 512, 3, pad=1, flip_filters=False)
    net['conv4_2'] = ConvLayer(
        net['conv4_1'], 512, 3, pad=1, flip_filters=False)
    net['conv4_3'] = ConvLayer(
        net['conv4_2'], 512, 3, pad=1, flip_filters=False)
    net['pool4'] = PoolLayer(net['conv4_3'], 2)
    net['conv5_1'] = ConvLayer(
        net['pool4'], 512, 3, pad=1, flip_filters=False)
    net['conv5_2'] = ConvLayer(
        net['conv5_1'], 512, 3, pad=1, flip_filters=False)
    net['conv5_3'] = ConvLayer(
        net['conv5_2'], 512, 3, pad=1, flip_filters=False)
    net['pool5'] = PoolLayer(net['conv5_3'], 2)
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
    net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
    net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096)
    net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
    net['fc8'] = DenseLayer(
        net['fc7_dropout'], num_units=1000, nonlinearity=None)
    net['prob'] = NonlinearityLayer(net['fc8'], softmax)

    return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def build_model():
    net = {}
    net['input'] = InputLayer((None, 3, 224, 224))
    net['conv1_1'] = ConvLayer(
        net['input'], 64, 3, pad=1, flip_filters=False)
    net['conv1_2'] = ConvLayer(
        net['conv1_1'], 64, 3, pad=1, flip_filters=False)
    net['pool1'] = PoolLayer(net['conv1_2'], 2)
    net['conv2_1'] = ConvLayer(
        net['pool1'], 128, 3, pad=1, flip_filters=False)
    net['conv2_2'] = ConvLayer(
        net['conv2_1'], 128, 3, pad=1, flip_filters=False)
    net['pool2'] = PoolLayer(net['conv2_2'], 2)
    net['conv3_1'] = ConvLayer(
        net['pool2'], 256, 3, pad=1, flip_filters=False)
    net['conv3_2'] = ConvLayer(
        net['conv3_1'], 256, 3, pad=1, flip_filters=False)
    net['conv3_3'] = ConvLayer(
        net['conv3_2'], 256, 3, pad=1, flip_filters=False)
    net['conv3_4'] = ConvLayer(
        net['conv3_3'], 256, 3, pad=1, flip_filters=False)
    net['pool3'] = PoolLayer(net['conv3_4'], 2)
    net['conv4_1'] = ConvLayer(
        net['pool3'], 512, 3, pad=1, flip_filters=False)
    net['conv4_2'] = ConvLayer(
        net['conv4_1'], 512, 3, pad=1, flip_filters=False)
    net['conv4_3'] = ConvLayer(
        net['conv4_2'], 512, 3, pad=1, flip_filters=False)
    net['conv4_4'] = ConvLayer(
        net['conv4_3'], 512, 3, pad=1, flip_filters=False)
    net['pool4'] = PoolLayer(net['conv4_4'], 2)
    net['conv5_1'] = ConvLayer(
        net['pool4'], 512, 3, pad=1, flip_filters=False)
    net['conv5_2'] = ConvLayer(
        net['conv5_1'], 512, 3, pad=1, flip_filters=False)
    net['conv5_3'] = ConvLayer(
        net['conv5_2'], 512, 3, pad=1, flip_filters=False)
    net['conv5_4'] = ConvLayer(
        net['conv5_3'], 512, 3, pad=1, flip_filters=False)
    net['pool5'] = PoolLayer(net['conv5_4'], 2)
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
    net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
    net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096)
    net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
    net['fc8'] = DenseLayer(
        net['fc7_dropout'], num_units=1000, nonlinearity=None)
    net['prob'] = NonlinearityLayer(net['fc8'], softmax)

    return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import DropoutLayer [as 别名]
def get_net():
    return NeuralNet(
            layers=[
                ('input', layers.InputLayer),
                ('conv1', Conv2DLayer),
                ('pool1', MaxPool2DLayer),
                ('dropout1', layers.DropoutLayer),
                ('conv2', Conv2DLayer),
                ('pool2', MaxPool2DLayer),
                ('dropout2', layers.DropoutLayer),
                ('conv3', Conv2DLayer),
                ('pool3', MaxPool2DLayer),
                ('dropout3', layers.DropoutLayer),
                ('hidden4', layers.DenseLayer),
                ('dropout4', layers.DropoutLayer),
                ('hidden5', layers.DenseLayer),
                ('output', layers.DenseLayer),
            ],
            input_shape=(None, 1, 96, 96),
            conv1_num_filters=32, conv1_filter_size=(3, 3), pool1_pool_size=(2, 2),
            dropout1_p=0.1,
            conv2_num_filters=64, conv2_filter_size=(2, 2), pool2_pool_size=(2, 2),
            dropout2_p=0.2,
            conv3_num_filters=128, conv3_filter_size=(2, 2), pool3_pool_size=(2, 2),
            dropout3_p=0.3,
            hidden4_num_units=1000,
            dropout4_p=0.5,
            hidden5_num_units=1000,
            output_num_units=30, output_nonlinearity=None,

            update_learning_rate=theano.shared(float32(0.03)),
            update_momentum=theano.shared(float32(0.9)),

            regression=True,
            batch_iterator_train=FlipBatchIterator(batch_size=128),
            on_epoch_finished=[
                AdjustVariable('update_learning_rate', start=0.03, stop=0.0001),
                AdjustVariable('update_momentum', start=0.9, stop=0.999),
                EarlyStopping(patience=200),
            ],
            max_epochs=3000,
            verbose=1,
    )