我正在努力做转学习; 为此我想删除神经网络的最后两层并添加另外两层.这是一个示例代码,它也输出相同的错误.
from keras.models import Sequential
from keras.layers import Input,Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dropout, Activation
from keras.layers.pooling import GlobalAveragePooling2D
from keras.models import Model
in_img = Input(shape=(3, 32, 32))
x = Convolution2D(12, 3, 3, subsample=(2, 2), border_mode='valid', name='conv1')(in_img)
x = Activation('relu', name='relu_conv1')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
x = Convolution2D(3, 1, 1, border_mode='valid', name='conv2')(x)
x = Activation('relu', name='relu_conv2')(x)
x = GlobalAveragePooling2D()(x)
o = Activation('softmax', name='loss')(x)
model = Model(input=in_img, output=[o])
model.compile(loss="categorical_crossentropy", optimizer="adam")
#model.load_weights('model_weights.h5', by_name=True)
model.summary() …我有一个训练有素的Keras模型，我想要：
1）用相同但无偏差的Con2D层替换。
2）在首次激活之前添加BatchNormalization层
我怎样才能做到这一点？
def keras_simple_model():
from keras.models import Model
from keras.layers import Input, Dense, GlobalAveragePooling2D
from keras.layers import Conv2D, MaxPooling2D, Activation
inputs1 = Input((28, 28, 1))
x = Conv2D(4, (3, 3), activation=None, padding='same', name='conv1')(inputs1)
x = Activation('relu')(x)
x = Conv2D(4, (3, 3), activation=None, padding='same', name='conv2')(x)
x = Activation('relu')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)
x = Conv2D(8, (3, 3), activation=None, padding='same', name='conv3')(x)
x = Activation('relu')(x)
x = Conv2D(8, (3, 3), activation=None, padding='same', name='conv4')(x)
x = Activation('relu')(x)
x = …