Я применяю сеть биомедицинской сегментации UNET для данных цветных изображений и наземной правды из 50 изображений (преобразованных в файл NPY размером 50 256 256,3 каждый), но ошибка выше и сеть не может перейти к обучению. Краткое описание модели приведено ниже:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 256, 256, 64) 1792 input_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 256, 256, 64) 36928 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 128, 128, 64) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 128, 128, 128 73856 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 128, 128, 128 147584 conv2d_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 64, 64, 128) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 64, 64, 256) 295168 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 64, 64, 256) 590080 conv2d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 32, 32, 256) 0 conv2d_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 32, 32, 512) 1180160 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 32, 32, 512) 2359808 conv2d_7[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 512) 0 conv2d_8[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 16, 16, 512) 0 dropout_1[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 16, 16, 1024) 4719616 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 16, 16, 1024) 9438208 conv2d_9[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 16, 16, 1024) 0 conv2d_10[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, 32, 32, 1024) 0 dropout_2[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 32, 32, 512) 2097664 up_sampling2d_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 32, 32, 1024) 0 dropout_1[0][0]
conv2d_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 32, 32, 512) 4719104 concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 32, 32, 512) 2359808 conv2d_12[0][0]
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, 64, 64, 512) 0 conv2d_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 64, 64, 256) 524544 up_sampling2d_2[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 64, 64, 512) 0 conv2d_6[0][0]
conv2d_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 64, 64, 256) 1179904 concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 64, 64, 256) 590080 conv2d_15[0][0]
__________________________________________________________________________________________________
up_sampling2d_3 (UpSampling2D) (None, 128, 128, 256 0 conv2d_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 128, 128, 128 131200 up_sampling2d_3[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 128, 128, 256 0 conv2d_4[0][0]
conv2d_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 128, 128, 128 295040 concatenate_3[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 128, 128, 128 147584 conv2d_18[0][0]
__________________________________________________________________________________________________
up_sampling2d_4 (UpSampling2D) (None, 256, 256, 128 0 conv2d_19[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 256, 256, 64) 32832 up_sampling2d_4[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 256, 256, 128 0 conv2d_2[0][0]
conv2d_20[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 256, 256, 64) 73792 concatenate_4[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 256, 256, 64) 36928 conv2d_21[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 256, 256, 2) 1154 conv2d_22[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 256, 256, 1) 3 conv2d_23[0][0]
==================================================================================================
Total params: 31,032,837
Trainable params: 31,032,837
Non-trainable params: 0
__________________________________________________________________________________________________
также модель архитектуры выглядит следующим образом:
import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
def unet(pretrained_weights = None,input_size = (256,256,3)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
Но я получаю эту ошибку:
ValueError: Ошибка при проверке цели: ожидалось, что conv2d_24 будет иметь форму (256, 256, 1), но получил массив с формой (256, 256, 3)
Что я могу сделать, чтобы это исправить?