U-Net: Error when checking target: expected activation_1 to have 3 dimensions, but got array with shape (1, 224, 224, 21)

[pgadosey]

Hi, I'm trying this out with the U-Net Architecture but i keep running into this error, I'm not sure what I might be doing wrong. This is what the definition of my model looks like:

`def get_unet(self):

	inputs = Input((self.img_rows, self.img_cols,3))
	#print(inputs.shape)
	conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
	conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
	pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
	conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
	conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
	pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
	conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
	conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
	pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
	conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
	conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
	drop4 = Dropout(0.5)(conv4)
	pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
	conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
	conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
	drop5 = Dropout(0.5)(conv5)

	up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
	merge6 = concatenate([drop4,up6], axis = 3)
	conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
	conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

	up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
	merge7 = concatenate([conv3,up7], axis = 3)
	conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
	conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

	up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
	merge8 = concatenate([conv2,up8], axis = 3)
	conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
	conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

	up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
	merge9 = concatenate([conv1,up9], axis = 3)
	conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
	conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
	conv9 = Conv2D(21, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
	conv9 = Conv2D(21, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
	print(conv9.shape)

	
	reshape = Reshape((21,self.img_rows * self.img_cols))(conv9)
	print(reshape.shape)

	permute = Permute((2,1))(reshape)
	print(permute.shape)

	activation = Activation('softmax')(permute)
	
	print(activation.shape)
	model = Model(input = inputs, output = activation)

	return model`

[Kkaboon]

have you solved the problem？