Python core.dropout方法代碼示例

本文整理匯總了Python中tflearn.layers.core.dropout方法的典型用法代碼示例。如果您正苦於以下問題：Python core.dropout方法的具體用法？Python core.dropout怎麽用？Python core.dropout使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tflearn.layers.core的用法示例。

在下文中一共展示了core.dropout方法的11個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: build_network

# 需要導入模塊: from tflearn.layers import core [as 別名]
# 或者: from tflearn.layers.core import dropout [as 別名]
def build_network(self):
      print("---> Starting Neural Network") 
      self.network = input_data(shape = [None, 48, 48, 1])
      self.network = conv_2d(self.network, 64, 5, activation = 'relu')
      self.network = max_pool_2d(self.network, 3, strides = 2)
      self.network = conv_2d(self.network, 64, 5, activation = 'relu')
      self.network = max_pool_2d(self.network, 3, strides = 2)
      self.network = conv_2d(self.network, 128, 4, activation = 'relu')
      self.network = dropout(self.network, 0.3)
      self.network = fully_connected(self.network, 3072, activation = 'relu')
      self.network = fully_connected(self.network, len(self.target_classes), activation = 'softmax')
      self.network = regression(self.network,
        optimizer = 'momentum',
        loss = 'categorical_crossentropy')
      self.model = tflearn.DNN(
        self.network,
        checkpoint_path = 'model_1_nimish',
        max_checkpoints = 1,
        tensorboard_verbose = 2
      )
      self.load_model()

開發者ID:nimish1512，項目名稱:Emotion-recognition-and-prediction，代碼行數:23，代碼來源:em_model.py

示例2: alexnet

# 需要導入模塊: from tflearn.layers import core [as 別名]
# 或者: from tflearn.layers.core import dropout [as 別名]
def alexnet(width, height, lr, output=3):
    network = input_data(shape=[None, width, height, 1], name='input')
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, output, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')

    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log')

    return model

開發者ID:Sentdex，項目名稱:pygta5，代碼行數:28，代碼來源:alexnet.py

示例3: alexnet2

# 需要導入模塊: from tflearn.layers import core [as 別名]
# 或者: from tflearn.layers.core import dropout [as 別名]
def alexnet2(width, height, lr, output=3):
    network = input_data(shape=[None, width, height, 1], name='input')
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, output, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')

    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log')

    return model

開發者ID:Sentdex，項目名稱:pygta5，代碼行數:39，代碼來源:alexnet.py