我根据link中的指南实现了一个序列生成器对象
import tensorflow as tf
from cv2 import imread, resize
from sklearn.utils import shuffle
from cv2 import imread, resize
import numpy as np
from tensorflow.keras import utils
import math
import keras as ks
class reader(tf.keras.utils.Sequence):
def __init__(self, x, y, batch_size, n_class):
self.x, self.y = x, y
self.batch_size = batch_size
self.n_class = n_class
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def __getitem__(self, idx):
print('getitem', idx)
batch_x = self.x[idx * self.batch_size:(idx + 1) *
self.batch_size]
batch_y = self.y[idx * self.batch_size:(idx + 1) *
self.batch_size]
data_x = list()
for batch in batch_x:
tmp = list()
for img_path in batch:
try:
img = imread(img_path)
tmp.append(img)
except Exception as e:
print(e)
print('failed to find path {}'.format(img_path))
data_x.append(tmp)
#
data_x = np.array(data_x, dtype='object')
data_y = np.array(batch_y)
data_y = utils.to_categorical(data_y, self.n_class)
print('return item')
print(data_x.shape)
return (data_x, data_y)
def on_epoch_end(self):
# option method to run some logic at the end of each epoch: e.g. reshuffling
print('on epoch end')
seed = np.random.randint()
self.x = shuffle(self.x, random_state=seed)
self.y = shuffle(self.y, random_state=seed)
但是,它不适用于tensorflow模型的fit api。下面是我用来复制这个问题的简单模型架构
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv3D(10, input_shape=(TEMPORAL_LENGTH,HEIGHT,WIDTH,CHANNEL), kernel_size=(2,2,2), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
model.summary()
让我创建一个读者
r1 = reader(x_train, y_train, 20, 10)
然后我调用model.fit api
train_history = model.fit(r1, epochs=3, steps_per_epoch=5, verbose=1)
### output ###
getitem 0
return item
(20, 16, 192, 256, 3)
WARNING:tensorflow:sample_weight modes were coerced from
...
to
['...']
Train for 5 steps
Epoch 1/3
如果我不打断的话,它将永远像这样。出于好奇,我用从KerasAPI创建的模型尝试了这种方法,令我惊讶的是,它居然奏效了
model = ks.models.Sequential()
model.add(ks.layers.Conv3D(10, input_shape=(TEMPORAL_LENGTH,HEIGHT,WIDTH,CHANNEL), kernel_size=(2,2,2), strides=2))
model.add(ks.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(ks.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(ks.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(ks.layers.Flatten())
model.add(ks.layers.Dense(10))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
train_history = model.fit(r1, epochs=3, steps_per_epoch=5, verbose=1)
### output ###
Epoch 1/3
getitem 586
return item
(20, 16, 192, 256, 3)
getitem 169
1/5 [=====>........................] - ETA: 22s - loss: 11.0373 - accuracy: 0.0000e+00return item
(20, 16, 192, 256, 3)
getitem 601
2/5 [===========>..................] - ETA: 12s - loss: 7.9983 - accuracy: 0.0250 return item
(20, 16, 192, 256, 3)
getitem 426
3/5 [=================>............] - ETA: 8s - loss: 10.7049 - accuracy: 0.2500return item
(20, 16, 192, 256, 3)
getitem 243
4/5 [=======================>......] - ETA: 3s - loss: 8.5093 - accuracy: 0.1875
依赖关系
老年人。我很抱歉反应太晚。我已找到解决此问题的方法。 我需要更改的只是将函数self中的数据_x转换为dtype='float32'。getitem()。要复制该问题,只需将数据类型更改为“object”
除此之外,请允许我分享类ActionDataGenerator是从Anujshah's教程修改而来的
输出
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