本文整理汇总了Python中keras.models.Sequential.pop_layer方法的典型用法代码示例。如果您正苦于以下问题：Python Sequential.pop_layer方法的具体用法？Python Sequential.pop_layer怎么用？Python Sequential.pop_layer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keras.models.Sequential的用法示例。
在下文中一共展示了Sequential.pop_layer方法的1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: C3D_conv_features
# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import pop_layer [as 别名]
def C3D_conv_features(summary=False):
""" Return the Keras model of the network until the fc6 layer where the
convolutional features can be extracted.
"""
from keras.layers.convolutional import Convolution3D, MaxPooling3D, ZeroPadding3D
from keras.layers.core import Dense, Dropout, Flatten
from keras.models import Sequential
model = Sequential()
# 1st layer group
model.add(Convolution3D(64, 3, 3, 3, activation='relu',
border_mode='same', name='conv1',
subsample=(1, 1, 1),
input_shape=(3, 16, 112, 112),
trainable=False))
model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),
border_mode='valid', name='pool1'))
# 2nd layer group
model.add(Convolution3D(128, 3, 3, 3, activation='relu',
border_mode='same', name='conv2',
subsample=(1, 1, 1),
trainable=False))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool2'))
# 3rd layer group
model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3a',
subsample=(1, 1, 1),
trainable=False))
model.add(Convolution3D(256, 3, 3, 3, activation='relu',
border_mode='same', name='conv3b',
subsample=(1, 1, 1),
trainable=False))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool3'))
# 4th layer group
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4a',
subsample=(1, 1, 1),
trainable=False))
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv4b',
subsample=(1, 1, 1),
trainable=False))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool4'))
# 5th layer group
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5a',
subsample=(1, 1, 1),
trainable=False))
model.add(Convolution3D(512, 3, 3, 3, activation='relu',
border_mode='same', name='conv5b',
subsample=(1, 1, 1),
trainable=False))
model.add(ZeroPadding3D(padding=(0, 1, 1), name='zeropadding'))
model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
border_mode='valid', name='pool5'))
model.add(Flatten(name='flatten'))
# FC layers group
model.add(Dense(4096, activation='relu', name='fc6', trainable=False))
model.add(Dropout(.5, name='do1'))
model.add(Dense(4096, activation='relu', name='fc7'))
model.add(Dropout(.5, name='do2'))
model.add(Dense(487, activation='softmax', name='fc8'))
# Load weights
model.load_weights('data/models/c3d-sports1M_weights.h5')
for _ in range(4):
model.pop_layer()
if summary:
print(model.summary())
return model