keras的ImageDataGenerator和flow()的用法说明

ImageDataGenerator的参数自己看文档

from keras.preprocessing import image
import numpy as np

X_train=np.ones((3,123,123,1))
Y_train=np.array([[1],[2],[2]])
generator=image.ImageDataGenerator(featurewise_center=False,
  samplewise_center=False,
  featurewise_std_normalization=False,
  samplewise_std_normalization=False,
  zca_whitening=False,
  zca_epsilon=1e-6,
  rotation_range=180,
  width_shift_range=0.2,
  height_shift_range=0.2,
  shear_range=0,
  zoom_range=0.001,
  channel_shift_range=0,
  fill_mode='nearest',
  cval=0.,
  horizontal_flip=True,
  vertical_flip=True,
  rescale=None,
  preprocessing_function=None,
  data_format='channels_last')

a=generator.flow(X_train,Y_train,batch_size=20)#生成的是一个迭代器，可直接用于for循环
'''
batch_size如果小于X的第一维m，next生成的多维矩阵的第一维是为batch_size，输出是从输入中随机选取batch_size个数据
batch_size如果大于X的第一维m，next生成的多维矩阵的第一维是m，输出是m个数据，不过顺序随机
，输出的X，Y是一一对对应的
如果要直接用于tf.placeholder()，要求生成的矩阵和要与tf.placeholder相匹配

'''
X,Y=next(a)

print(Y)
X,Y=next(a)

print(Y)
X,Y=next(a)

print(Y)
X,Y=next(a)

输出

[[2]
 [1]
 [2]]

[[2]
 [2]
 [1]]

[[2]
 [2]
 [1]]

[[2]
 [2]
 [1]]

补充知识：tensorflow 与keras 混用之坑

在使用tensorflow与keras混用是model.save 是正常的但是在load_model的时候报错了在这里mark 一下

其中错误为：TypeError: tuple indices must be integers, not list

再一一番百度后无结果，上谷歌后找到了类似的问题。但是是一对鸟文不知道什么东西（翻译后发现是俄文）。后来谷歌翻译了一下找到了解决方法。故将原始问题文章贴上来警示一下

原训练代码

from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
 
#Каталог с данными для обучения
train_dir = 'train'
# Каталог с данными для проверки
val_dir = 'val'
# Каталог с данными для тестирования
test_dir = 'val'
 
# Размеры изображения
img_width, img_height = 800, 800
# Размерность тензора на основе изображения для входных данных в нейронную сеть
# backend Tensorflow, channels_last
input_shape = (img_width, img_height, 3)
# Количество эпох
epochs = 1
# Размер мини-выборки
batch_size = 4
# Количество изображений для обучения
nb_train_samples = 300
# Количество изображений для проверки
nb_validation_samples = 25
# Количество изображений для тестирования
nb_test_samples = 25
 
model = Sequential()
 
model.add(Conv2D(32, (7, 7), padding="same", input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(10, 10)))
 
model.add(Conv2D(64, (5, 5), padding="same"))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(10, 10)))
 
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
 
model.compile(loss='categorical_crossentropy',
       optimizer="Nadam",
       metrics=['accuracy'])
print(model.summary())
datagen = ImageDataGenerator(rescale=1. / 255)
 
train_generator = datagen.flow_from_directory(
  train_dir,
  target_size=(img_width, img_height),
  batch_size=batch_size,
  class_mode='categorical')
 
val_generator = datagen.flow_from_directory(
  val_dir,
  target_size=(img_width, img_height),
  batch_size=batch_size,
  class_mode='categorical')
 
test_generator = datagen.flow_from_directory(
  test_dir,
  target_size=(img_width, img_height),
  batch_size=batch_size,
  class_mode='categorical')
 
model.fit_generator(
  train_generator,
  steps_per_epoch=nb_train_samples // batch_size,
  epochs=epochs,
  validation_data=val_generator,
  validation_steps=nb_validation_samples // batch_size)
 
print('Сохраняем сеть')
model.save("grib.h5")
print("Сохранение завершено!")

模型载入

from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import load_model
 
print("Загрузка сети")
model = load_model("grib.h5")
print("Загрузка завершена!")

报错

/usr/bin/python3.5 /home/disk2/py/neroset/do.py
/home/mama/.local/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
 from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Загрузка сети
Traceback (most recent call last):
 File "/home/disk2/py/neroset/do.py", line 13, in <module>
  model = load_model("grib.h5")
 File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 243, in load_model
  model = model_from_config(model_config, custom_objects=custom_objects)
 File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 317, in model_from_config
  return layer_module.deserialize(config, custom_objects=custom_objects)
 File "/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py", line 55, in deserialize
  printable_module_name='layer')
 File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 144, in deserialize_keras_object
  list(custom_objects.items())))
 File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 1350, in from_config
  model.add(layer)
 File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 492, in add
  output_tensor = layer(self.outputs[0])
 File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 590, in __call__
  self.build(input_shapes[0])
 File "/usr/local/lib/python3.5/dist-packages/keras/layers/normalization.py", line 92, in build
  dim = input_shape[self.axis]
TypeError: tuple indices must be integers or slices, not list
 
Process finished with exit code 1

战斗种族解释

убераю BatchNormalization всё работает хорошо. Не подскажите в чём ошибка?Выяснил что сохранение keras и нормализация tensorflow не работают вместе нужно просто изменить строку импорта.（译文：整理BatchNormalization一切正常。 不要告诉我错误是什么？我发现保存keras和规范化tensorflow不能一起工作;只需更改导入字符串即可。)

强调文本 强调文本

keras.preprocessing.image import ImageDataGenerator
keras.models import Sequential
keras.layers import Conv2D, MaxPooling2D, BatchNormalization
keras.layers import Activation, Dropout, Flatten, Dense

##完美解决

##附上原文链接

https://qa-help.ru/questions/keras-batchnormalization

以上这篇keras的ImageDataGenerator和flow()的用法说明就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持易盾网络。