load_model 时出现 lambda 问题的 Keras 激活

Question

我正在尝试使用参数“axis”执行 softmax，我发现的唯一方法是通过函数 lambda。这是我的代码，其中包含一个带有用于 softmax 的 lambda 的激活层：

from keras.models import Model
from keras.layers import Input,Dense,Reshape,Activation
from keras.layers.merge import Multiply,Concatenate
from keras.layers.core import Lambda
from keras.activations import softmax
from keras import backend as K
import numpy as np

N = 6
M = 6
T = 1000
H = 5

# Toy input creation
input = np.concatenate([np.random.normal(np.random.rand(1)[0],1.,(1,N,M)) for t in range(T)],axis=0)
input2 = np.random.rand(T,N,M)
input3 = np.random.rand(T,N,M)
input4 = np.random.rand(T,N,M)
a = np.mean(np.reshape(input,(T,N*M)),axis=1)
a = np.maximum(0.,np.minimum(a,0.9999))
a = np.floor(a*3).astype(int)
a = np.stack([a for i in range(M)],axis=1)
a = np.stack([a for i in range(N)],axis=2)
mix1 = np.concatenate((input2[:,:2,:],input3[:,2:4,:],input4[:,4:,:]),axis=1)
mix2 = np.concatenate((input3[:,:2,:],input4[:,2:4,:],input2[:,4:,:]),axis=1)
mix3 = np.concatenate((input4[:,:2,:],input2[:,2:4,:],input3[:,4:,:]),axis=1)
output = np.choose(a,[mix1,mix2,mix3])
images = np.stack((input2,input3,input4),axis=3)

# models definition
# one general model to be trained and
# one mask model to be used later for testing
input_layer = Input(shape=(N,M))
images_input = Input(shape=(N,M,3))
x = Reshape((N*M,))(input_layer)
x = Dense(H, kernel_initializer='uniform', activation='relu')(x)
x = Dense(N*N*3, kernel_initializer='uniform')(x)
x = Reshape((N,N,3))(x)
masks = Activation(activation=lambda y:softmax(y,axis=3))(x)
output_layer = Multiply()([masks,images_input])
output_layer = Lambda(lambda x:K.sum(x,axis=3))(output_layer)
model = Model(inputs=[input_layer,images_input],outputs=output_layer)
mask_model = Model(inputs=input_layer,outputs=masks)

# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')

# Fit the model
history = model.fit([input,images], output, epochs=200, batch_size=50)

#save models
model.save('test.h5')
mask_model.save('mask_test.h5')

它在训练期间工作正常，但是当我尝试加载文件时，它失败了：

from keras.models import load_model
mask_model = load_model('mask_test.h5')

我收到错误：

from keras.models import Model
from keras.layers import Input,Dense,Reshape,Activation
from keras.layers.merge import Multiply,Concatenate
from keras.layers.core import Lambda
from keras.activations import softmax
from keras import backend as K
import numpy as np

N = 6
M = 6
T = 1000
H = 5

# Toy input creation
input = np.concatenate([np.random.normal(np.random.rand(1)[0],1.,(1,N,M)) for t in range(T)],axis=0)
input2 = np.random.rand(T,N,M)
input3 = np.random.rand(T,N,M)
input4 = np.random.rand(T,N,M)
a = np.mean(np.reshape(input,(T,N*M)),axis=1)
a = np.maximum(0.,np.minimum(a,0.9999))
a = np.floor(a*3).astype(int)
a = np.stack([a for i in range(M)],axis=1)
a = np.stack([a for i in range(N)],axis=2)
mix1 = np.concatenate((input2[:,:2,:],input3[:,2:4,:],input4[:,4:,:]),axis=1)
mix2 = np.concatenate((input3[:,:2,:],input4[:,2:4,:],input2[:,4:,:]),axis=1)
mix3 = np.concatenate((input4[:,:2,:],input2[:,2:4,:],input3[:,4:,:]),axis=1)
output = np.choose(a,[mix1,mix2,mix3])
images = np.stack((input2,input3,input4),axis=3)

# models definition
# one general model to be trained and
# one mask model to be used later for testing
input_layer = Input(shape=(N,M))
images_input = Input(shape=(N,M,3))
x = Reshape((N*M,))(input_layer)
x = Dense(H, kernel_initializer='uniform', activation='relu')(x)
x = Dense(N*N*3, kernel_initializer='uniform')(x)
x = Reshape((N,N,3))(x)
masks = Activation(activation=lambda y:softmax(y,axis=3))(x)
output_layer = Multiply()([masks,images_input])
output_layer = Lambda(lambda x:K.sum(x,axis=3))(output_layer)
model = Model(inputs=[input_layer,images_input],outputs=output_layer)
mask_model = Model(inputs=input_layer,outputs=masks)

# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')

# Fit the model
history = model.fit([input,images], output, epochs=200, batch_size=50)

#save models
model.save('test.h5')
mask_model.save('mask_test.h5')

同样的情况发生在：

model = load_model('test.h5')

我使用 lambda 函数错了吗？或者（更好）有没有办法避免使用 lambda 函数？

Answer 1

尝试自定义激活层然后加载模型。

load_model('test.h5',custom_objects=activation_layer)