Keras模型- Unet图像分割

Question

我正在尝试使用Keras model API重新创建UNet，我已经收集了细胞的图像，以及它的分割版本，我正在尝试用它来训练一个模型。在这样做的时候，我可以上传一个不同的细胞，并获得图像的分割版本作为预测。

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()

当我尝试运行它时，我希望它能适应超过10个时期，但是，它抛出了以下错误：

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)

如果有人能告诉我我做错了什么，代码应该是什么，或者给我指明了正确的方向，我将不胜感激。

谢谢!