```
def ConvolutionNetworks(kernel_size=3, stride_size=2):
def conv(model):
    model = Conv2D(24, (9, 9), strides=(stride_size, stride_size),activation='relu',input_shape=(100, 100, 3), data_format='channels_last')(model)
    model = BatchNormalization()(model)
    model = Conv2D(24, (7, 7), strides=(stride_size, stride_size),activation='relu')(model)
    model = BatchNormalization()(model)
    model = Conv2D(24, (kernel_size, kernel_size), strides=(stride_size, stride_size),activation='relu')(model)
    model = BatchNormalization()(model)
    model = Conv2D(24, (5, 5), strides=(1, 1),activation='relu')(model)
    model = BatchNormalization()(model)
    return model
return conv
```

```
def build_tag(conv):
d = K.int_shape(conv)[2]
tag = np.zeros((d,d,2))
print("tagging in process")
for i in range(d):
    for j in range(d):
        tag[i,j,0] = float(int(i%d))/(d-1)*2-1
        tag[i,j,1] = float(int(j%d))/(d-1)*2-1
tag = K.variable(tag)
tag = K.expand_dims(tag, axis=0)
batch_size = K.shape(conv)[0]
tag = K.tile(tag, [batch_size,1,1,1])
print("tagging done")
return Input(tensor=tag)
```

```
visual_scene = Input((100, 100, 3))

visual_conv = ConvolutionNetworks()(visual_scene)
tag = build_tag(visual_conv)
visual_conv = Concatenate()([tag, visual_conv])
visual_RN = RelationNetworks(visual_conv)
visual_out = Dense(4, activation='softmax')(visual_RN)
VisualModel = Model(inputs=[tag,visual_scene], outputs=visual_out)
print("model made")
```

```
try:
parallel_model = multi_gpu_model(VisualModel, cpu_merge=True,
                                  cpu_relocation=True,gpus=2)
print("Training using multiple GPUs..")
except:
parallel_model = model
print("Training using single GPU or CPU..")
```

```
datagen=ImageDataGenerator(rescale=1./255)
train_generator=datagen.flow_from_dataframe(dataframe=training_df,
                directory=image_dir, x_col="image", y_col="lesion",
          class_mode="categorical",target_size=(IMG_SIZE,IMG_SIZE),
          batch_size=batchsize,shuffle=True)
```

```
parallel_model.fit_generator(generator = train_generator,
          steps_per_epoch = (training_df.shape[0])//batchsize,
                       validation_data = validation_generator,
            validation_steps = (validation_df.shape[0])//batchsize,
                         epochs = 30,verbose=1,callbacks=
                         [checkpoint, csv_logger,tensorboard],
                        use_multiprocessing=True,workers=workers)