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Python normalization.local_response_normalization方法代码示例

本文整理汇总了Python中tflearn.layers.normalization.local_response_normalization方法的典型用法代码示例。如果您正苦于以下问题：Python normalization.local_response_normalization方法的具体用法？Python normalization.local_response_normalization怎么用？Python normalization.local_response_normalization使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tflearn.layers.normalization的用法示例。


在下文中一共展示了normalization.local_response_normalization方法的11个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: alexnet

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
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
开发者ID:Sentdex，项目名称:pygta5，代码行数:28，代码来源:alexnet.py

示例2: alexnet2

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
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
开发者ID:Sentdex，项目名称:pygta5，代码行数:39，代码来源:alexnet.py

示例3: sentnet_color_2d

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
def sentnet_color_2d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
    network = input_data(shape=[None, width, height, 3], 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,
                        max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:39，代码来源:models.py

示例4: sentnet_color

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
def sentnet_color(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
    network = input_data(shape=[None, width, height,3, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(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_name,
                        max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:39，代码来源:models.py

示例5: sentnet_frames

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
def sentnet_frames(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height,frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(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=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:39，代码来源:models.py

示例6: sentnet

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
def sentnet(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    network = conv_3d(network, 256, 5, activation='relu')
    network = avg_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = avg_pool_3d(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=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:39，代码来源:models.py

示例7: alexnet2

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
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=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:39，代码来源:models.py

示例8: sentnet_v0

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
def sentnet_v0(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    
    #network = local_response_normalization(network)
    
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)

    #network = local_response_normalization(network)
    
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 384, 3, 3, activation='relu')
    network = conv_3d(network, 256, 3, 3, activation='relu')

    network = max_pool_3d(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=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:35，代码来源:models.py

示例9: alexnet

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
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=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:28，代码来源:models.py

示例10: sentnet2

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
def sentnet2(width, height, frame_count, lr, output=9):
    network = input_data(shape=[None, width, height, frame_count, 1], name='input')
    network = conv_3d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 256, 5, activation='relu')
    network = max_pool_3d(network, 3, strides=2)
    #network = local_response_normalization(network)
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 384, 3, activation='relu')
    network = conv_3d(network, 256, 3, activation='relu')
    network = max_pool_3d(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, 3, 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=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:28，代码来源:models.py

示例11: alexnet

# 需要导入模块: from tflearn.layers import normalization [as 别名]
# 或者: from tflearn.layers.normalization import local_response_normalization [as 别名]
def alexnet(width, height, lr):
    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, 3, 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=0, tensorboard_dir='log')

    return model
开发者ID:Sentdex，项目名称:pygta5，代码行数:28，代码来源:alexnet.py


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