โมเดล Keras - การแบ่งส่วนรูปภาพ Unet

ฉันกำลังพยายามสร้าง UNet ขึ้นใหม่โดยใช้ Keras model API ฉันได้รวบรวมรูปภาพของเซลล์ และเวอร์ชันที่แบ่งส่วนแล้ว และฉันกำลังพยายามฝึกโมเดลด้วย ในการทำเช่นนั้น ฉันสามารถอัปโหลดเซลล์อื่นและรับรูปภาพที่แบ่งส่วนเป็นการคาดการณ์ได้

https://github.com/JamilGafur/Unet

from __future__ import print_function

from matplotlib import pyplot as plt
from keras import losses
import os
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam
import cv2
import numpy as np
# training data 
image_location = "C:/Users/JamilG-Lenovo/Desktop/train/"
image = image_location+"image"
label = image_location +"label"


class train_data():

    def __init__(self, image, label):
        self.image = []
        self.label = []
        for file in os.listdir(image):
            if file.endswith(".tif"):
                self.image.append(cv2.imread(image+"/"+file,0))

        for file in os.listdir(label):
            if file.endswith(".tif"):
                #print(label+"/"+file)
                self.label.append(cv2.imread(label+"/"+file,0))

    def get_image(self):
        return np.array(self.image)

    def get_label(self):
        return np.array(self.label)



def get_unet(rows, cols):
    inputs = Input((rows, cols, 1))
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)

    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)

    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)

    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)

    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])
    model.compile(optimizer=Adam(lr=1e-5), loss = losses.mean_squared_error)

    return model



def main():
    # load all the training images
    train_set = train_data(image, label)
    # get the training image
    train_images = train_set.get_image()
    # get the segmented image
    train_label = train_set.get_label()
    print("type of train_images" + str(type(train_images[0])))
    print("type of train_label" + str(type(train_label[0])))
    print('\n')
    print("shape of train_images" + str(train_images[0].shape))
    print("shape of train_label" + str(train_label[0].shape))


    plt.imshow(train_images[0], interpolation='nearest')
    plt.title("actual image")
    plt.show()

    plt.imshow(train_label[0], interpolation='nearest')
    plt.title("segmented image")
    plt.show()
    # create a UNet (512,512)
    unet = get_unet(train_label[0].shape[0],
                    train_label[0].shape[1])

    # look at the summary of the unet
    unet.summary()
    #-----------errors start here-----------------

    # fit the unet with the actual image, train_images
    # and the output, train_label
    unet.fit(train_images, train_label, batch_size=16, epochs=10)

main()

เมื่อฉันพยายามเรียกใช้ ฉันคาดหวังว่ามันจะเหมาะสมมากกว่า 10 ยุค แต่กลับแสดงข้อผิดพลาดต่อไปนี้:

File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", 
line 144, in _standardize_input_data str(array.shape))

ValueError: Error when checking input: expected input_5 to have shape (None, 
512, 512, 1) but got array with shape (1, 30, 512, 512)

หากมีใครสามารถบอกฉันว่าฉันทำอะไรผิด รหัสควรเป็นอย่างไร หรือชี้ทิศทางที่ถูกต้องให้ฉัน ฉันจะขอบคุณมาก

ขอบคุณ!