小编phu*_*icy的帖子

我和Recurrentshop和Keras有问题.我试图在Recurrent Model中使用Concatenate和多维张量,无论我如何安排Input,shape和batch_shape,我都会遇到维度问题.

最小代码:

from keras.layers import *
from keras.models import *
from recurrentshop import *
from keras.layers import Concatenate

input_shape=(128,128,3)

x_t = Input(shape=(128,128,3,))
h_tm1 = Input(shape=(128,128,3, ))

h_t1 = Concatenate()([x_t, h_tm1])
last = Conv2D(3, kernel_size=(3,3), strides=(1,1), padding='same',     name='conv2')(h_t1)

# Build the RNN
rnn = RecurrentModel(input=x_t, initial_states=[h_tm1], output=last,     final_states=[last], state_initializer=['zeros'])

x = Input(shape=(128,128,3, ))
y = rnn(x)

model = Model(x, y)

model.predict(np.random.random((1, 128, 128, 3)))

错误代码:

ValueError: Shape must be rank 3 but it is rank 4 for 'recurrent_model_1/concatenate_1/concat' (op:ConcatV2) with …