[Python] Keras(FIT_GENERATOR)- Error, when checking target: expected activation_1 to have 3...

I've been working on a segmentation problem for many days and after finally finding out how to properly read the dataset, I ran into this problem:

ValueError: Error when checking target: expected activation_1(Softmax) to have 3 dimensions, but got array with shape (32, 416, 608, 3)​

I used the functional API, since I took the FCNN architecture from [here](https://github.com/divamgupta/image-segmentation-keras/blob/master/Models/FCN32.py).


It is slightly modified and adapted in accordance with my task(IMAGE_ORDERING = "channels_last"(TensorFlow backend)). Can anyone please help me? Massive thanks in advance. The architecture below is for FCNN, which I try to implement for the purpose of the segmentation. Here is the architecture(after calling model.summary()):

1.

2.

1. 
   The specific error is:
   
   
2. 
   "Importing the dataset" function:
   
   
3. 
   "Fit_Generator method calling":
   
   img_input = Input(shape=(input_height,input_width,3))
   
   #Block 1
   x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', data_format=IMAGE_ORDERING)(img_input)
   x = BatchNormalization()(x)
   x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool', data_format=IMAGE_ORDERING)(x)
   f1 = x
   # Block 2
   x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format=IMAGE_ORDERING )(x)
   f2 = x
   
   # Block 3
   x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', data_format=IMAGE_ORDERING )(x)
   f3 = x
   
   # Block 4
   x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2',data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3',data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool', data_format=IMAGE_ORDERING)(x)
   f4 = x
   
   # Block 5
   x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2',data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', data_format=IMAGE_ORDERING)(x)
   x = BatchNormalization()(x)
   x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING)(x)
   f5 = x
   
   x = (Convolution2D(4096,(7,7) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(x)
   x = Dropout(0.5)(x)
   x = (Convolution2D(4096,(1,1) , activation='relu' , padding='same',data_format=IMAGE_ORDERING))(x)
   x = Dropout(0.5)(x)
   
   #First parameter = number of classes+1 (de la background)
   x = (Convolution2D(20,(1,1) ,kernel_initializer='he_normal' ,data_format=IMAGE_ORDERING))(x)
   x = Convolution2DTranspose(20,kernel_size=(64,64), strides=(32,32),use_bias=False,data_format=IMAGE_ORDERING)(x)
   o_shape = Model(img_input,x).output_shape
   
   outputHeight = o_shape[1]
   print('Output Height is:', outputHeight)
   outputWidth = o_shape[2]
   print('Output Width is:', outputWidth)
   #https://keras.io/layers/core/#reshape
   x = (Reshape((20,outputHeight*outputWidth)))(x)
   #https://keras.io/layers/core/#permute
   x = (Permute((2, 1)))(x)
   print("Output shape before softmax is", o_shape)
   x = (Activation('softmax'))(x)
   print("Output shape after softmax is", o_shape)
   model = Model(inputs = img_input,outputs = x)
   model.outputWidth = outputWidth
   model.outputHeight = outputHeight
   model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics =['accuracy'])

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