`Concatenate` layer requires inputs with matching shapes except for the concat axis

Question

from keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout,  Lambda
from keras.optimizers import Adam

Shape of my image is (36,128,128,1).How to change the shape of u7 so that I can perform concatenation?How to modify this?

def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):
    #Build the model
        inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS))
        #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand
        s = inputs
        inputs = Input(shape=(36,128,128),name='input')
        #Contraction path
        c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(s)
        c1 = Dropout(0.1)(c1)
        c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c1)
        p1 = MaxPooling3D((2, 2, 2))(c1)
        
        c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p1)
        c2 = Dropout(0.1)(c2)
        c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c2)
        p2 = MaxPooling3D((2, 2, 2))(c2)
         
        c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p2)
        c3 = Dropout(0.2)(c3)
        c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c3)
        p3 = MaxPooling3D((2, 2, 2))(c3)
         
        c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p3)
        c4 = Dropout(0.2)(c4)
        c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c4)
        p4 = MaxPooling3D(pool_size=(2, 2, 2))(c4)
         
        c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p4)
        c5 = Dropout(0.3)(c5)
        c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c5)
        
        #Expansive path 
        u6 = Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(c5)
        u6 = concatenate([u6, c4])
        c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u6)
        c6 = Dropout(0.2)(c6)
        c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c6)
         
        u7 = Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(c6)
        u7 = concatenate([u7, c3])
        c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u7)
        c7 = Dropout(0.2)(c7)
        c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c7)
         
        u8 = Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(c7)
        u8 = concatenate([u8, c2])
        c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u8)
        c8 = Dropout(0.1)(c8)
        c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c8)
         
        u9 = Conv3DTranspose(16, (2, 2, 2), strides=(2, 2, 2), padding='same')(c8)
        u9 = concatenate([u9, c1])
        c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u9)
        c9 = Dropout(0.1)(c9)
        c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c9)
         
        outputs = Conv3D(num_classes, (1, 1, 1), activation='softmax')(c9)
         
        model = Model(inputs=[inputs], outputs=[outputs])
        #compile model outside of this function to make it flexible. 
        model.summary()
        
        return model

I'm getting error in line u7 = concatenate([u7, c3])

A Concatenate layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 32, 32, 8, 64), (None, 32, 32, 9, 64)] But if the Shape of my image is (64,128,128,1).It works properly.But if i increase the depth from 36 to 64;the image is changed

Building the model

epochs = 10
model.fit(
    X_train,y_train,
    validation_data=(X_test,y_test),
    epochs=epochs,
    shuffle=True,
    verbose=2,
    callbacks = callbacks_list)

I'm getting error ValueError: Input 0 is incompatible with layer model: expected shape=(None, 36, 128, 128, 1), found shape=(None, 64, 128, 128, 1)

Answer 1

You can try concatenating on axis=1 and removing one of your two Input layers. You only need one. Here is a working example (although I am not sure what your goal is):

from keras.layers import Input, Conv3D, MaxPooling3D, concatenate, Conv3DTranspose, BatchNormalization, Dropout,  Lambda
import tensorflow as tf

def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):
    #Build the model
        kernel_initializer = tf.keras.initializers.GlorotNormal()
        inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS), name='input')
        #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand
        s = inputs
        #Contraction path
        c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(s)
        c1 = Dropout(0.1)(c1)
        c1 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c1)
        p1 = MaxPooling3D((2, 2, 2))(c1)
        
        c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p1)
        c2 = Dropout(0.1)(c2)
        c2 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c2)
        p2 = MaxPooling3D((2, 2, 2))(c2)
         
        c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p2)
        c3 = Dropout(0.2)(c3)
        c3 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c3)
        p3 = MaxPooling3D((2, 2, 2))(c3)
         
        c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p3)
        c4 = Dropout(0.2)(c4)
        c4 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c4)
        p4 = MaxPooling3D(pool_size=(2, 2, 2))(c4)
         
        c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(p4)
        c5 = Dropout(0.3)(c5)
        c5 = Conv3D(256, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c5)
        
        #Expansive path 
        u6 = Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(c5)
        u6 = concatenate([u6, c4])
        c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u6)
        c6 = Dropout(0.2)(c6)
        c6 = Conv3D(128, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c6)
         
        u7 = Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(c6)
        u7 = concatenate([u7, c3], axis=1)
        c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u7)
        c7 = Dropout(0.2)(c7)
        c7 = Conv3D(64, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c7)
         
        u8 = Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(c7)
        u8 = concatenate([u8, c2], axis=1)
        c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u8)
        c8 = Dropout(0.1)(c8)
        c8 = Conv3D(32, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c8)
         
        u9 = Conv3DTranspose(16, (2, 2, 2), strides=(2, 2, 2), padding='same')(c8)
        u9 = concatenate([u9, c1], axis=1)
        c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(u9)
        c9 = Dropout(0.1)(c9)
        c9 = Conv3D(16, (3, 3, 3), activation='relu', kernel_initializer=kernel_initializer, padding='same')(c9)
         
        outputs = Conv3D(num_classes, (1, 1, 1), activation='softmax')(c9)
         
        model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
        #compile model outside of this function to make it flexible. 
        model.summary()
        
        return model


simple_unet_model(36,128,128, 1, 5)