model

model.py

resnet50 = tf.keras.applications.resnet50
conv_model = resnet50.ResNet50(weights='imagenet', include_top=False, input_shape=(228,228,3))
for layer in conv_model.layers: 
    layer.trainable = False
x = Conv2D(128, (1, 1), activation = 'relu', name='block6_conv1_table')(conv_model.output)
x = Dropout(0.8, name='block6_dropout_1')(x)

x = Conv2D(128, (1, 1), activation = 'relu', name='block6_conv2_table')(x)
x = Dropout(0.8, name='block6_dropout_2')(x)

table_mask = fun_table(x, conv_model.output)
column_mask = fun_column(x, conv_model.output)

full_model = tf.keras.models.Model(inputs=conv_model.input, outputs=[table_mask, column_mask],name="tablenet")