在Keras中训练我的顺序模型时遇到了一些问题。我是这个主题的新手，因此涉及很多以下教程和代码片段...
我正在构建一个基本的CNN，以基于正射影像来区分线性基础结构和地貌特征。由于图像非常大，因此我必须创建一个遵循Keras Docs的数据生成器。 该模型的编译工作正常。但是每次我运行model.fit_generator（）命令时，都会出现错误，我的训练图像之一丢失了（不是）。我设置了五个纪元开始，该错误已在第一个纪元中发生。
我感谢任何想法可能出了问题。
我正在使用带有iypthon笔记本theano后端的Ubuntu 16.04.5 LTS进行工作。
train_path = '/path/to/train/folder'
tree_top = os.listdir(train_path)
training_filenames = os.listdir('%s/%s/' %(train_path, tree_top[0])) + os.listdir('%s/%s/' %(train_path, tree_top[1]))
valid_path = '/path/to/valid/folder'
tree_top = os.listdir(valid_path)
valid_filenames = os.listdir('%s/%s/' %(valid_path, tree_top[0])) + os.listdir('%s/%s/' %(valid_path, tree_top[1]))
数据生成器
from skimage.io import imread
from skimage.transform import resize
import numpy as np
class MY_Generator(Sequence): # inherits from Sequence class
def __init__(self, image_filenames, labels, batch_size):
self.image_filenames, self.labels = image_filenames, labels
self.batch_size = batch_size
def __len__(self): # computes number of batches by dividing sample size by the batch_size
return np.ceil(len(self.image_filenames) / float(self.batch_size))
num_training_samples = len(self.image_filenames)
return num_training_samples
def __getitem__(self, idx):
batch_x = self.image_filenames[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_y = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]
return np.array([
resize(imread(file_name), (200, 200))
for file_name in batch_x]), np.array(batch_y)
my_training_batch_generator = MY_Generator(training_filenames, tree_top, batch_size)
my_validation_batch_generator = MY_Generator(valid_filenames, tree_top, batch_size)
convnet
model = Sequential([
Conv2D(3, (3, 3), activation='relu', input_shape=(300,400,3)),
Flatten(),
Dense(2, activation='softmax'),
])
model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.fit_generator(generator=my_training_batch_generator,
steps_per_epoch=(len(my_training_batch_generator.image_filenames) // batch_size),
epochs=5,
verbose=1,
validation_data=my_validation_batch_generator,
validation_steps=(len(my_validation_batch_generator.image_filenames) // batch_size)
)
输出如下：