Keras layer asks for different shape than in the summary - keras

Question

I'm writing a U-net CNN in keras, and trying to use fit_generator for training. In order for this to work, I used a generator script, that could feed the images and labels for my network (simple fit function is working but I want to train a big dataset which cannot fit into the memory).
My problem is that in the model summary, it says correctly that, the output layer has a shape: (None, 288, 512, 4)
https://i.imgur.com/69xG8pO.jpg
but when I try actual training I get this error:
https://i.imgur.com/j7H6sHX.jpg
I don't get why keras wants (288, 512, 1) when in the summary it expects (288, 512, 4)
I tried it with my own unet code, and copied a working code from github also, but both of them has the exact same problem which leads me to believe that my generator script is the weak link. Below is the code I used (the image and label array functions used here were already working when I used them with "fit" in a previous CNN):
def generator(img_path, label_path, batch_size, height, width, num_classes):
input_pairs = get_pairs(img_path, label_path) # rewrite if param name changes
random.shuffle(input_pairs)
iterate_pairs = itertools.cycle(input_pairs)
while True:
X = []
Y = []
for _ in range(batch_size):
im, lab = next(iterate_pairs)
appended_im = next(iter(im))
appended_lab = next(iter(lab))
X.append(input_image_array(appended_im, width, height))
Y.append(input_label_array(appended_lab, width, height, num_classes, palette))
yield (np.array(X), np.array(Y))
I tried the generator out and the provided batches has the shapes of (for batch size of 15):
(15, 288, 512, 3)
(15, 288, 512, 4)
So I really do not know what could be the problem here.
EDIT: Here is the model code I used:
def conv_block(input_tensor, n_filter, kernel=(3, 3), padding='same', initializer="he_normal"):
x = Conv2D(n_filter, kernel, padding=padding, kernel_initializer=initializer)(input_tensor)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = Conv2D(n_filter, kernel, padding=padding, kernel_initializer=initializer)(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
return x
def deconv_block(input_tensor, residual, n_filter, kernel=(3, 3), strides=(2, 2), padding='same'):
y = Conv2DTranspose(n_filter, kernel, strides, padding)(input_tensor)
y = concatenate([y, residual], axis=3)
y = conv_block(y, n_filter)
return y
# NETWORK - n_classes is the desired number of classes, filters are fixed
def Unet(input_height, input_width, n_classes=4, filters=64):
# Downsampling
input_layer = Input(shape=(input_height, input_width, 3), name='input')
conv_1 = conv_block(input_layer, filters)
conv_1_out = MaxPooling2D(pool_size=(2, 2))(conv_1)
conv_2 = conv_block(conv_1_out, filters*2)
conv_2_out = MaxPooling2D(pool_size=(2, 2))(conv_2)
conv_3 = conv_block(conv_2_out, filters*4)
conv_3_out = MaxPooling2D(pool_size=(2, 2))(conv_3)
conv_4 = conv_block(conv_3_out, filters*8)
conv_4_out = MaxPooling2D(pool_size=(2, 2))(conv_4)
conv_4_drop = Dropout(0.5)(conv_4_out)
conv_5 = conv_block(conv_4_drop, filters*16)
conv_5_drop = Dropout(0.5)(conv_5)
# Upsampling
deconv_1 = deconv_block(conv_5_drop, conv_4, filters*8)
deconv_1_drop = Dropout(0.5)(deconv_1)
deconv_2 = deconv_block(deconv_1_drop, conv_3, filters*4)
deconv_2_drop = Dropout(0.5)(deconv_2)
deconv_3 = deconv_block(deconv_2_drop, conv_2, filters*2)
deconv_3 = deconv_block(deconv_3, conv_1, filters)
# Output - mapping each 64-component feature vector to number of classes
output = Conv2D(n_classes, (1, 1))(deconv_3)
output = BatchNormalization()(output)
output = Activation("softmax")(output)
# embed into functional API
model = Model(inputs=input_layer, outputs=output, name="Unet")
return model

Answer 1

Change your loss to categorical_crossentropy.
When using the sparse_categorical_crossentropy loss, your targets
should be integer targets.