this is the way i built the model

```
def conv_block(inputs=None, n_filters=32, dropout_prob=0, max_pooling=True):
conv = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(inputs)
conv = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv)
if dropout_prob > 0:
conv = Dropout(dropout_prob)(conv)
if max_pooling:
next_layer = MaxPooling2D(pool_size=(2, 2))(conv)
else:
next_layer = conv
skip_connection = conv
return next_layer, skip_connection
def upsampling_block(expansive_input, contractive_input, n_filters=32):
up = Conv2DTranspose(
n_filters,
(3,3),
strides=(2,2),
padding='same')(expansive_input)
merge = concatenate([up, contractive_input], axis=3)
conv = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge)
conv = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv)
return conv
def unet_model(input_size=(256, 256, 3), n_filters=32, n_classes=23):
inputs = Input(input_size)
cblock1 = conv_block(inputs, n_filters)
cblock2 = conv_block(cblock1[0], n_filters * 2)
cblock3 = conv_block(cblock2[0], n_filters * 4)
cblock4 = conv_block(cblock3[0], n_filters * 8, dropout_prob=0.3)
cblock5 = conv_block(cblock4[0], n_filters * 16, dropout_prob=0.3, max_pooling=False)
ublock6 = upsampling_block(cblock5[0], cblock4[1], n_filters * 8)
ublock7 = upsampling_block(ublock6, cblock3[1], n_filters * 4)
ublock8 = upsampling_block(ublock7, cblock2[1], n_filters * 2)
ublock9 = upsampling_block(ublock8, cblock1[1], n_filters)
conv9 = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(ublock9)
conv10 = Conv2D(n_classes, 1, padding='same')(conv9)
model = tf.keras.Model(inputs=inputs, outputs=conv10)
return model
```