【发布时间】：2019-01-03 18:29:50
【问题描述】：
我正在尝试创建一个可逆网络，当它后退时，权重矩阵是前向过程中权重矩阵的转置。所以我定义了一个自定义层
class Backwardlayer(Dense):
def __init__(self,output_dim,b_layer,activation=None,use_bias=True,kernel_initializer='glorot_uniform',bias_initializer='zeros',kernel_regularizer=None,bias_regularizer=None,activity_regularizer=None,kernel_constraint=None,bias_constraint=None,**kwargs):
self.output_dim = output_dim
self.b_layer = b_layer
super(Backwardlayer, self).__init__(output_dim,**kwargs)
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
self.kernel = K.transpose(self.b_layer.kernel)
if self.use_bias:
self.bias = self.add_weight(shape=(self.output_dim,),initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint)
else:
self.bias = None
self.built = True
def direction_cosine(x):
return K.sqrt(K.sum(x, axis=-1, keepdims=None))
def abs(x):
return K.abs(x)
input_img = Input(shape=(784,))
layer_1 = Dense(512, activation=abs)
layer_2 = Dense(512, activation=abs)
layer_3 = Dense(256, activation=abs)
layer_4 = Dense(128, activation=abs)
layer_5 = Dense(10, activation=abs)
encoder_layer_1 = layer_1(input_img)
encoder_layer_2 = layer_2(encoder_layer_1)
encoder_layer_3 = layer_3(encoder_layer_2)
encoder_layer_4 = layer_4(encoder_layer_3)
encoder_layer_5 = layer_5(encoder_layer_4)
decoder_layer_1 = Backwardlayer(128,b_layer=encoder_layer_5,activation=abs,name='dl1')(encoder_layer_5)
decoder_layer_2 = Backwardlayer(256,b_layer=layer_4,activation=abs,name='dl2')(decoder_layer_1)
decoder_layer_3 = Backwardlayer(512,b_layer=layer_3,activation=abs,name='dl3')(decoder_layer_2)
decoder_layer_4 = Backwardlayer(512,b_layer=layer_2,activation=abs,name='dl4')(decoder_layer_3)
reconstructed_img = Backwardlayer(784,b_layer=layer_1,activation=abs,name='dl5')
rms = RMSprop()
(x_train,y_train), (x_test,y_test) = mnist.load_data()
x_train = x_train.reshape(-1,784)
x_test = x_test.reshape(-1,784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
AANN = Model(input=input_img, output=[reconstructed_img,normalised_output])
AANN.summary()
AANN.compile(optimizer=rms,loss=['mse','categorical_crossentropy'],loss_weights=[1,1])
history = AANN.fit(x_train,[x_train,y_train],epochs=3,batch_size=128,verbose=2,validation_data=(x_test,y_test))
这是错误：检查目标时出错：预期 lambda_1 有 1 个维度，但得到的数组形状为 (60000, 10)
Traceback（最近一次调用最后一次）：
File "AANN.py", line 95, in <module>
history = AANN.fit(x_train,[x_train,y_train],epochs=3,batch_size=128,verbose=2,validation_data=(x_test,y_test))
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 955, in fit
batch_size=batch_size)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 792, in _standardize_user_data
exception_prefix='target')
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training_utils.py", line 126, in standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking target: expected lambda_1 to have 1 dimensions, but got array with shape (60000, 10)
【问题讨论】：
标签： python tensorflow keras tensor