keras的ImageDataGenerator和flow()的用法说明_Python

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

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