Python 3.x 在迁移学习中尝试生成数据时出现tensorflow图错误
我试图在预训练的初始模型上使用迁移学习，因此我创建了一个用于从模型中提取特征的类：Python 3.x 在迁移学习中尝试生成数据时出现tensorflow图错误,python-3.x,tensorflow,keras,Python 3.x,Tensorflow,Keras,我试图在预训练的初始模型上使用迁移学习，因此我创建了一个用于从模型中提取特征的类： from prototype import Dataset, VideoStreamHandler import numpy import random from keras.applications.inception_v3 import preprocess_input from keras.preprocessing import image from scipy.misc import imresize
from prototype import Dataset, VideoStreamHandler
import numpy
import random
from keras.applications.inception_v3 import preprocess_input
from keras.preprocessing import image
from scipy.misc import imresize
import time
class Extractor(Dataset.Dataset):
"""
"""
def __init__(self, path_to_data, seq_len, base_model, image_shape=(299, 299, 3)):
super().__init__(path_to_data, seq_len, input_shape=image_shape)
self._extractor = base_model
def extract_features(self, batch_size):
"""
passes the data through the base model to get the feature map to later train on
:return: feature map
"""
class_one_hot = self.one_hot_encode() # get the one hot for the classes
data = self.clean_data(self.get_data(), self._input_shape[0])
print("Processing {} videos".format(len(self.get_data())))
transfer_maps, labels = [], []
rand = random.SystemRandom()
while True:
for _ in range(batch_size):
row = rand.choice(data)
sequence = self.get_frames(row[0])
if len(sequence) > self._input_shape[0]:
sequence = self.rescale_frame_list(sequence, self._input_shape[0])
print("{} video processing is complete".format(row[0].split('\\')[-1]))
features = []
for frame in sequence:
frame_arr = image.img_to_array(frame) # turn image to numpy array
frame_arr = numpy.expand_dims(frame_arr, axis=0)
frame_arr = preprocess_input(frame_arr)
features.append(self._extractor.predict(frame_arr))
transfer_maps.append(features)
labels.append(class_one_hot[row[1]])
yield numpy.array(transfer_maps), numpy.array(labels)
def get_frames(self, pth):
"""
:type: string
:param pth: path to the specific file from which we take the frames
:return: the frames in the file
"""
f_queue = VideoStreamHandler.VideoStream(pth) # This object opens a thread that reads frames with opencv
# capture independently from the frame processing to prevent i/o delay and speed up processing
f_queue.start()
time.sleep(1.0) # wait a moment so the thread could start reading frames
sequence = []
while f_queue.isnt_empty():
frame = f_queue.read()
# resize is used to keep all frames from all videos the same size
frame = imresize(frame, (self._input_shape[1], self._input_shape[2]))
sequence.append(frame)
f_queue.close() # close the thread
return sequence
my_model.fit_generator(generator=train_gen, epochs=10, steps_per_epoch=steps_per_epoch, verbose=1, workers=4)
您确定这是fit_generator的问题，而不是模型的问题吗？
my_model.fit（）