ochuman_model_purple.py

def get_model():
    in1 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3 ))

    conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(in1)
    conv1 = Dropout(0.2)(conv1)
    conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv1)
    pool1 = MaxPooling2D((2, 2))(conv1)

    conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool1)
    conv2 = Dropout(0.2)(conv2)
    conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv2)
    pool2 = MaxPooling2D((2, 2))(conv2)

    conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool2)
    conv3 = Dropout(0.2)(conv3)
    conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv3)
    pool3 = MaxPooling2D((2, 2))(conv3)

    conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool3)
    conv4 = Dropout(0.2)(conv4)
    conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv4)

    up1 = concatenate([UpSampling2D((2, 2))(conv4), conv3], axis=-1)
    conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up1)
    conv5 = Dropout(0.2)(conv5)
    conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv5)
    
    up2 = concatenate([UpSampling2D((2, 2))(conv5), conv2], axis=-1)
    conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)
    conv6 = Dropout(0.2)(conv6)
    conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv6)

    up2 = concatenate([UpSampling2D((2, 2))(conv6), conv1], axis=-1)
    conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)
    conv7 = Dropout(0.2)(conv7)
    conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv7)
    segmentation = Conv2D(3, (1, 1), activation='sigmoid', name='seg')(conv7)

    model = Model(inputs=[in1], outputs=[segmentation])

    losses = {'seg': 'binary_crossentropy'
            }

    metrics = {'seg': ['acc']
                }
    model.compile(optimizer="adam", loss = losses, metrics=metrics)

    return model