用 theano 库实现自适应的 Dropout 方法来动态调整 VGGCNN网络的Dropout 概率值。

实现自适应的 Dropout 方法来动态调整 VGGCNN网络的Dropout 概率值，需要分为以下几个步骤：

1. 定义VGGCNN网络并加入Dropout层。

2. 构建自适应的 Dropout 方法。

3. 基于自适应的 Dropout 方法动态调整 VGGCNN 网络的 Dropout 概率值。

4. 训练并测试 VGGCNN 网络。

下面是对每个步骤的详细介绍：

1. 定义VGGCNN网络并加入Dropout层。

首先，我们需要定义一个VGGCNN网络。在定义网络时，我们需要在每个卷积层后面加入一个 Dropout 层，代码如下：

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

import lasagne
from lasagne.nonlinearities import softmax
from lasagne.layers import InputLayer, DropoutLayer, DenseLayer, NonlinearityLayer
from lasagne.layers import Conv2DLayer, MaxPool2DLayer, FlattenLayer
from lasagne.layers import batch_norm

class VGGCNN(object):
    def __init__(self, input_shape=(None, 3, 32, 32), num_classes=10):
        self.input_shape = input_shape
        self.num_classes = num_classes
        self.build_network()

    def build_network(self):
        self.net = {}
        self.net['input'] = InputLayer(self.input_shape)

        self.net['conv1_1'] = Conv2DLayer(self.net['input'], num_filters=64, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv1_2'] = Conv2DLayer(self.net['conv1_1'], num_filters=64, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['pool1'] = MaxPool2DLayer(self.net['conv1_2'], pool_size=2)

        self.net['conv2_1'] = Conv2DLayer(self.net['pool1'], num_filters=128, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv2_2'] = Conv2DLayer(self.net['conv2_1'], num_filters=128, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['pool2'] = MaxPool2DLayer(self.net['conv2_2'], pool_size=2)

        self.net['conv3_1'] = Conv2DLayer(self.net['pool2'], num_filters=256, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv3_2'] = Conv2DLayer(self.net['conv3_1'], num_filters=256, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv3_3'] = Conv2DLayer(self.net['conv3_2'], num_filters=256, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['pool3'] = MaxPool2DLayer(self.net['conv3_3'], pool_size=2)

        self.net['conv4_1'] = Conv2DLayer(self.net['pool3'], num_filters=512, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv4_2'] = Conv2DLayer(self.net['conv4_1'], num_filters=512, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv4_3'] = Conv2DLayer(self.net['conv4_2'], num_filters=512, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['pool4'] = MaxPool2DLayer(self.net['conv4_3'], pool_size=2)

        self.net['conv5_1'] = Conv2DLayer(self.net['pool4'], num_filters=512, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv5_2'] = Conv2DLayer(self.net['conv5_1'], num_filters=512, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['conv5_3'] = Conv2DLayer(self.net['conv5_2'], num_filters=512, filter_size=3, pad=1, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['pool5'] = MaxPool2DLayer(self.net['conv5_3'], pool_size=2)

        self.net['fc6'] = DenseLayer(self.net['pool5'], num_units=4096, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['drop6'] = DropoutLayer(self.net['fc6'], p=0.5)

        self.net['fc7'] = DenseLayer(self.net['drop6'], num_units=4096, nonlinearity=lasagne.nonlinearities.rectify, W=lasagne.init.GlorotUniform())
        self.net['drop7'] = DropoutLayer(self.net['fc7'], p=0.5)

        self.net['output'] = DenseLayer(self.net['drop7'], num_units=self.num_classes, nonlinearity=softmax, W=lasagne.init.GlorotUniform())

        self.input_var = T.tensor4('input_var')
        self.target_var = T.ivector('target_var')

2. 构建自适应的 Dropout 方法。

在构建自适应的 Dropout 方法时，我们需要定义一个 Dropout 层的更新函数。在更新函数中，我们可以根据网络的输出和目标值来动态调整 Dropout 概率值。具体实现如下：

def adaptive_dropout(layer, p, x, target, update_fn=lasagne.updates.adam, update_params={'learning_rate': 0.001}):
    # 计算当前层的输出
    output = lasagne.layers.get_output(layer, x, deterministic=False)

    # 计算当前层的目标值
    target_output = T.zeros_like(output)
    target_output = T.set_subtensor(target_output[T.arange(target.shape[0]), target], 1)

    # 计算交叉熵误差
    loss = lasagne.objectives.categorical_crossentropy(output, target_output).mean()

    # 计算梯度
    grads = T.grad(loss, wrt=layer.get_params(trainable=True))

    # 计算当前层的 Dropout 概率值
    p_shared = shared(p, name='p_shared')
    new_p = p_shared - update_fn(grads, [p_shared], **update_params)[0]

    # 更新 Dropout 概率值
    layer.p = T.clip(new_p, 0.0, 0.5)

3. 基于自适应的 Dropout 方法动态调整 VGGCNN 网络的 Dropout 概率值。

在训练过程中，我们需要在每个 Dropout 层之后调用 adaptive_dropout() 函数来动态调整 Dropout 概率值。具体实现如下：

def train(network, X_train, y_train, X_val, y_val, num_epochs=100, batch_size=128):
    # 定义训练函数
    prediction = lasagne.layers.get_output(network.net['output'], network.input_var, deterministic=False)
    loss = lasagne.objectives.categorical_crossentropy(prediction, network.target_var)
    loss = loss.mean() + 1e-4 * lasagne.regularization.regularize_network_params(network.net['output'], lasagne.regularization.l2)
    params = lasagne.layers.get_all_params(network.net['output'], trainable=True)
    updates = lasagne.updates.adam(loss, params)
    train_fn = theano.function([network.input_var, network.target_var], loss, updates=updates)

    # 定义验证函数
    val_prediction = lasagne.layers.get_output(network.net['output'], network.input_var, deterministic=True)
    val_loss = lasagne.objectives.categorical_crossentropy(val_prediction, network.target_var)
    val_loss = val_loss.mean()
    val_fn = theano.function([network.input_var, network.target_var], val_loss)

    # 训练模型
    for epoch in range(num_epochs):
        train_loss = 0
        train_batches = 0

        # 动态调整 Dropout 概率值
        adaptive_dropout(network.net['drop6'], 0.5, network.input_var, network.target_var)
        adaptive_dropout(network.net['drop7'], 0.5, network.input_var, network.target_var)

        # 训练模型
        for batch in iterate_minibatches(X_train, y_train, batch_size):
            inputs, targets = batch
            train_loss += train_fn(inputs, targets)
            train_batches += 1

        # 计算验证误差
        val_loss = 0
        val_batches = 0
        for batch in iterate_minibatches(X_val, y_val, batch_size):
            inputs, targets = batch
            val_loss += val_fn(inputs, targets)
            val_batches += 1

        # 输出训练进度
        print("Epoch {} of {}:".format(epoch + 1, num_epochs))
        print("  training loss:\t\t{:.6f}".format(train_loss / train_batches))
        print("  validation loss:\t\t{:.6f}".format(val_loss / val_batches))

4. 训练并测试 VGGCNN 网络。

最后，我们可以使用 train() 函数来训练并测试 VGGCNN 网络。具体实现如下：

# 加载数据集
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset()

# 构建 VGGCNN 网络
network = VGGCNN(input_shape=(None, 3, 32, 32), num_classes=10)

# 训练并测试 VGGCNN 网络
train(network, X_train, y_train, X_val, y_val, num_epochs=100, batch_size=128)
test(network, X_test, y_test)

这样，我们就成功地实现了自适应的 Dropout 方法来动态调整 VGGCNN 网络的 Dropout 概率值。