python - Keras 中的自定义生成器:做出预测
问题描述
我使用自定义生成器实现了双输入模型,如下所示:Create a mixed data generator (images,csv) in keras
import random
import pandas as pd
import numpy as np
from glob import glob
from keras.preprocessing import image as krs_image
# Create the arguments for image preprocessing
data_gen_args = dict(
horizontal_flip=True,
brightness_range=[0.5, 1.5],
shear_range=10,
channel_shift_range=50,
rescale=1. / 255,
)
# Create an empty data generator
datagen = ImageDataGenerator()
# Read the image list and csv
image_file_list = glob(f'{images_dir}/{split}/**/*.JPG', recursive=True)
df = pd.read_csv(f'{csv_dir}/{split}.csv', index_col=csv_data[0])
random.shuffle(image_file_list)
def custom_generator(images_list, dataframe, batch_size):
i = 0
while True:
batch = {'images': [], 'csv': [], 'labels': []}
for b in range(batch_size):
if i == len(images_list):
i = 0
random.shuffle(images_list)
# Read image from list and convert to array
image_path = images_list[i]
image_name = os.path.basename(image_path).replace('.JPG', '')
image = krs_image.load_img(image_path, target_size=(img_height, img_width))
image = datagen.apply_transform(image, data_gen_args)
image = krs_image.img_to_array(image)
# Read data from csv using the name of current image
csv_row = dataframe.loc[image_name, :]
label = csv_row['class']
csv_features = csv_row.drop(labels='class')
batch['images'].append(image)
batch['csv'].append(csv_features)
batch['labels'].append(label)
i += 1
batch['images'] = np.array(batch['images'])
batch['csv'] = np.array(batch['csv'])
# Convert labels to categorical values
batch['labels'] = np.eye(num_classes)[batch['labels']]
yield [batch['images'], batch['csv']], batch['labels']
并为模型安装:
history = model.fit(custom_generator(path_train, df_train, batch_size),
steps_per_epoch= train_steps,
epochs=epochs,
verbose=1,
#callbacks=callbacks,
validation_data=custom_generator(path_valid, df_valid, batch_size),
validation_steps=val_steps)
path_train & path_valid是图像文件夹训练和验证数据的路径。df_train和df_valid是元数据的数据帧。
但是,我不确定如何对装有自定义生成器的模型进行预测。我应该这样做吗?
predictions = model.predict(custom_generator(test_path, df_test, batch_size=1), verbose =1)
任何帮助是极大的赞赏。
解决方案
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