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)

[[2]
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 [2]]

[[2]
 [2]
 [1]]

[[2]
 [2]
 [1]]

[[2]
 [2]
 [1]]

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!")

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!")

/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

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