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KerasモデルAPIを使用してUNetを再作成しようとしていますが、セルの画像とセグメント化されたバージョンを収集しており、モデルを訓練しようとしています。そうすることで、別のセルをアップロードして、セグメント化されたバージョンのイメージを予測として取得できます。Kerasモデル - Unet Image Segmentation
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)
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()
誰かが私に何が間違っているのか、コードがどんなものか、正しい方向に向いているかを教えてもらえれば、それ。
ありがとうございました！