在情况下，任何人都是在寻找一个解决这个我已经实现一个自定义的发电机由延伸的序列从tensorflow:
class custom_generator(tf.keras.utils.Sequence):
def __init__(self, ecg_path, eeg_path, batch_size, img_shape, shuffle=True, X_col='filename', Y_col='class'):
self.batch_size = batch_size
self.img_shape = img_shape
self.shuffle = shuffle
self.X_col = X_col
self.Y_col = Y_col
self.class_mapping = {"sz": 1, "non-sz": 0}
self.ecg_path = ecg_path
self.eeg_path = eeg_path
self.eeg_df, self.ecg_df = self.__generate_data()
self.len = len(self.eeg_df)
self.n_name = self.ecg_df[self.Y_col].nunique()
def __generate_data(self):
eeg_class_dist = inspect_class_distribution(self.eeg_path)
ecg_class_dist = inspect_class_distribution(self.ecg_path)
max_n_images = get_lowest_distr(ecg_class_dist, eeg_class_dist)
balanced_ecg_data = limit_data(self.ecg_path, max_n_images).sort_values(by=[self.Y_col]).reset_index(drop=True)
balanced_eeg_data = limit_data(self.eeg_path, max_n_images).sort_values(by=[self.Y_col]).reset_index(drop=True)
return shuffle_order_dataframes(balanced_eeg_data, balanced_ecg_data)
def on_epoch_end(self):
if shuffle:
self.ecg_df, self.eeg_df = self.__generate_data()
def __get_input(self, path, target_size):
image = tf.keras.preprocessing.image.load_img(path)
image_arr = tf.keras.preprocessing.image.img_to_array(image)
image_arr = tf.image.resize(image_arr,(target_size[0], target_size[1])).numpy()
return image_arr/255.
def __get_output(self, label, num_classes):
categoric_label = self.class_mapping[label]
return tf.keras.utils.to_categorical(categoric_label, num_classes=num_classes)
def __get_data(self, x1_batches):
eeg_path_batch = x1_batches[self.X_col]
ecg_path_batch = x1_batches[self.X_col]
label_batch = x1_batches[self.Y_col]
x1_batch = np.asarray([self.__get_input(x, self.img_shape) for x in eeg_path_batch])
x2_batch = np.asarray([self.__get_input(x, self.img_shape) for x in ecg_path_batch])
y_batch = np.asarray([self.__get_output(y, self.n_name) for y in label_batch])
return tuple([x1_batch, x2_batch]), y_batch
def __getitem__(self, index):
n_batches = self.eeg_df[index * self.batch_size:(index + 1) * self.batch_size]
X, y = self.__get_data(n_batches)
return X, y
def __len__(self):
return self.len // self.batch_size
on_epoch_end是这里的关键。
