问题描述
我正在使用类似的混合生成器
import numpy as np
from tensorflow.keras.utils import Sequence
class MixupGenerator(Sequence):
def __init__(self,x_train,y_train,batch_size=32,alpha=0.2,shuffle=True):
self.X_train = x_train
self.y_train = y_train
self.batch_size = batch_size
self.alpha = alpha
self.shuffle = shuffle
self.sample_num = len(x_train)
self.lock = threading.Lock()
def __iter__(self):
return self
#@threadsafe_generator
def __call__(self):
with self.lock:
while True:
indexes = self.__get_exploration_order()
itr_num = int(len(indexes) // (self.batch_size * 2))
for i in range(itr_num):
batch_ids = indexes[i * self.batch_size * 2:(i + 1) * self.batch_size * 2]
X,y = self.__data_generation(batch_ids)
yield X,y
def __get_exploration_order(self):
indexes = np.arange(self.sample_num)
if self.shuffle:
np.random.shuffle(indexes)
return indexes
def __data_generation(self,batch_ids):
_,h,w,c = self.X_train.shape
l = np.random.beta(self.alpha,self.alpha,self.batch_size)
X_l = l.reshape(self.batch_size,1,1)
y_l = l.reshape(self.batch_size,1)
X1 = self.X_train[batch_ids[:self.batch_size]]
X2 = self.X_train[batch_ids[self.batch_size:]]
X = X1 * X_l + X2 * (1.0 - X_l)
if isinstance(self.y_train,list):
y = []
for y_train_ in self.y_train:
y1 = y_train_[batch_ids[:self.batch_size]]
y2 = y_train_[batch_ids[self.batch_size:]]
y.append(y1 * y_l + y2 * (1.0 - y_l))
else:
y1 = self.y_train[batch_ids[:self.batch_size]]
y2 = self.y_train[batch_ids[self.batch_size:]]
y = y1 * y_l + y2 * (1.0 - y_l)
return X,y
我在训练期间有 13965 个样本,在测试期间有 2970 个样本。我称之为适合:
history = model.fit_generator(train_datagen,validation_data=(val_x,val_y),epochs=epochs,steps_per_epoch=np.ceil((x.shape[0] - 1) / config.batch_size),callbacks=callbacks,verbose=tr_verbose)
batch_size = 32
verbose 比较少,是不是因为 epochs 和 batch size 是十进制的?
时代 49/500 436/437 [============================>.] - ETA:0s - 损失:0.1408 - categorical_accuracy:0.8295Epoch 1/ 500 2968/437 [============================================== ================================================== ================================================== ================================================== ========] - 6s 2ms/sample - 损失:0.2304 - categorical_accuracy:0.5162 437/437 [==============================] - 131 秒 299 毫秒/步 - 损失:0.1409 - categorical_accuracy:0.8294 - val_loss :0.2510 - val_categorical_accuracy:0.5162
解决方法
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