在Keras中，如何拼接两个层，其中一个层的维度为(None,512)，另一个层的维度为(18577,4)？我尝试使用Concatenate
concat_layer = Concatenate()([z1,agp]但这抛出了一个错误，提示如下：
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 512), (18577, 4)]模型大致如下：
a1= (Convolution2D(32, filter_dim, activation='linear', padding='same',kernel_regularizer=regularizers.l2(reg)))(input_img) b1 = (BatchNormalization())(a1)c1 = (PReLU())(b1)d1 = (Convolution2D(32, filter_dim, activation='linear',kernel_regularizer=regularizers.l2(reg)))(c1)e1 = (BatchNormalization())(d1)f1 = (PReLU())(e1)g1 = (MaxPooling2D(pool_size=(2,2)))(f1)h1 = (Dropout(0.2))(g1)i1= (Convolution2D(64, filter_dim, activation='linear', padding='same',kernel_regularizer=regularizers.l2(reg)))(h1)j1 = (BatchNormalization())(i1)k1 = (PReLU())(j1)l1 = (Convolution2D(64, filter_dim, activation='linear',kernel_regularizer=regularizers.l2(reg)))(k1)m1 = (BatchNormalization())(k1)n1 = (PReLU())(m1)o1 = (MaxPooling2D(pool_size=(2,2)))(n1)p1 = (Dropout(0.2))(o1)q1= (Convolution2D(128, filter_dim, activation='linear', padding='same',kernel_regularizer=regularizers.l2(reg)))(p1)r1=q1s1 = (BatchNormalization())(r1)t1 = (PReLU())(s1)u1 = (Convolution2D(128, filter_dim, activation='linear',kernel_regularizer=regularizers.l2(reg)))(t1)v1 = (BatchNormalization())(u1)w1 = (PReLU())(v1)x1 = (MaxPooling2D(pool_size=(3,3)))(w1)y1 = (Dropout(0.2))(x1)z1 = (Flatten())(y1)agp=tf.convert_to_tensor(agp,np.float32)z1 = Concatenate(axis=1)([z1,agp])a2 = (Dense(128, activation='linear',kernel_regularizer=regularizers.l2(reg)))(z1)b2 = (BatchNormalization())(a2)c2 = (PReLU())(b2)d2 = (Dropout(0.2))(c2)e2 = (Dense(32, activation='linear',kernel_regularizer=regularizers.l2(reg)))(d2)f2 = (BatchNormalization())(e2)g2 = (PReLU())(f2)h2 = (Dropout(0.3))(g2)我的输入图像维度为(32,32,3)。我想将z1(None,512)与agp(18577,4)拼接起来
回答：
#!/usr/bin/env pythondef create_model(nb_classes, input_shape): """Create a NN model.""" # from keras.layers import Dropout from keras.layers import Activation, Input from keras.layers import Dense, Concatenate from keras.models import Model input_ = Input(shape=input_shape) x = input_ # Branch in two directions - this can be more # complex, of course x1 = Dense(512, activation='relu')(x) x2 = Dense(4, activation='relu')(x) # And this is how you use concatenation x = Concatenate(axis=-1)([x1, x2]) # And then finish it x = Dense(nb_classes, activation='softmax')(x) model = Model(inputs=input_, outputs=x) return modelmodel = create_model(10, (512, ))print(model.summary())输出如下
Using TensorFlow backend.____________________________________________________________________________________________________Layer (type) Output Shape Param # Connected to ====================================================================================================input_1 (InputLayer) (None, 512) 0 ____________________________________________________________________________________________________dense_1 (Dense) (None, 512) 262656 input_1[0][0] ____________________________________________________________________________________________________dense_2 (Dense) (None, 4) 2052 input_1[0][0] ____________________________________________________________________________________________________concatenate_1 (Concatenate) (None, 516) 0 dense_1[0][0] dense_2[0][0] ____________________________________________________________________________________________________dense_3 (Dense) (None, 10) 5170 concatenate_1[0][0] ====================================================================================================Total params: 269,878Trainable params: 269,878Non-trainable params: 0____________________________________________________________________________________________________None