ImageDataGenerator的參數(shù)自己看文檔
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import numpy as npX_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')'''batch_size如果小于X的第一維m，next生成的多維矩陣的第一維是為batch_size，輸出是從輸入中隨機(jī)選取batch_size個(gè)數(shù)據(jù)batch_size如果大于X的第一維m，next生成的多維矩陣的第一維是m，輸出是m個(gè)數(shù)據(jù)，不過順序隨機(jī)，輸出的X，Y是一一對(duì)對(duì)應(yīng)的如果要直接用于tf.placeholder()，要求生成的矩陣和要與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) |
輸出
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補(bǔ)充知識(shí)：tensorflow 與keras 混用之坑
在使用tensorflow與keras混用是model.save 是正常的但是在load_model的時(shí)候報(bào)錯(cuò)了在這里mark 一下
其中錯(cuò)誤為：TypeError: tuple indices must be integers, not list
再一一番百度后無結(jié)果，上谷歌后找到了類似的問題。但是是一對(duì)鳥文不知道什么東西（翻譯后發(fā)現(xiàn)是俄文）。后來谷歌翻譯了一下找到了解決方法。故將原始問題文章貼上來警示一下
原訓(xùn)練代碼
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from tensorflow.python.keras.preprocessing.image import ImageDataGeneratorfrom tensorflow.python.keras.models import Sequentialfrom tensorflow.python.keras.layers import Conv2D, MaxPooling2D, BatchNormalizationfrom tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense #Каталог с данными для обученияtrain_dir = 'train'# Каталог с данными для проверкиval_dir = 'val'# Каталог с данными для тестированияtest_dir = 'val' # Размеры изображенияimg_width, img_height = 800, 800# Размерность тензора на основе изображения для входных данных в нейронную сеть# backend Tensorflow, channels_lastinput_shape = (img_width, img_height, 3)# Количество эпохepochs = 1# Размер мини-выборкиbatch_size = 4# Количество изображений для обученияnb_train_samples = 300# Количество изображений для проверкиnb_validation_samples = 25# Количество изображений для тестированияnb_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!") |
模型載入
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from tensorflow.python.keras.preprocessing.image import ImageDataGeneratorfrom tensorflow.python.keras.models import Sequentialfrom tensorflow.python.keras.layers import Conv2D, MaxPooling2D, BatchNormalizationfrom tensorflow.python.keras.layers import Activation, Dropout, Flatten, Densefrom keras.models import load_model print("Загрузка сети")model = load_model("grib.h5")print("Загрузка завершена!") |
報(bào)錯(cuò)
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/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_convertersUsing TensorFlow backend.Загрузка сетиTraceback (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 |
戰(zhàn)斗種族解釋
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.（譯文：整理BatchNormalization一切正常。 不要告訴我錯(cuò)誤是什么？我發(fā)現(xiàn)保存keras和規(guī)范化tensorflow不能一起工作;只需更改導(dǎo)入字符串即可。)
強(qiáng)調(diào)文本 強(qiáng)調(diào)文本
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keras.preprocessing.image import ImageDataGeneratorkeras.models import Sequentialkeras.layers import Conv2D, MaxPooling2D, BatchNormalizationkeras.layers import Activation, Dropout, Flatten, Dense |
##完美解決
##附上原文鏈接
https://qa-help.ru/questions/keras-batchnormalization
以上這篇keras的ImageDataGenerator和flow()的用法說明就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://blog.csdn.net/CZ505632696/article/details/79515782
