属性错误：模块'tensorflow'在 Keras 模型中没有属性'lite'到 Tensorflow Lite 转换 - Python

Question

我尝试使用以下代码将以下keras模型转换为tflite，以便在移动平台中托管。我安装了tensorflow版本=1.12 python版本=3.6.7 keras版本=2.2.4当我运行此代码时，我会出现以下错误。

转换器=tf.lite.TFLiteConverter.from_keras_model_file(keras_file(AttributeError：模块"tensorflow"没有属性"lite">

这个错误的原因是什么？如何解决？

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import tensorflow as tf
# dimensions of our images.
img_width, img_height = 150, 150
train_data_dir = 'D:\My Projects\Dataset\dataset6_2clz\train'
validation_data_dir = 'D:\My Projects\Dataset\dataset6_2clz\validation'

nb_train_samples = 75
nb_validation_samples = 50
#epochs = 50
#batch_size = 16
epochs = 5
batch_size = 4
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])

train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)

# Save tf.keras model in HDF5 format.
keras_file = "7_try.h5"
model.save('7_try.h5')

# Convert to TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_keras_model_file(keras_file)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)

Answer 1

在tensorflow 1.12中converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file(keras_file)

参见https://www.tensorflow.org/lite/convert/python_api#pre_tensorflow_1.12我想你们读过以下参考资料https://www.tensorflow.org/lite/convert/python_api，但请注意以下注意

注意：这些文档描述了TensorFlow中的转换器发行版，使用pip-install-tf-nighty安装。用于描述旧版本参考"从TensorFlow 1.12转换模型"。

此外，有关详细信息，您可以看到此提交消息https://github.com/tensorflow/tensorflow/commit/61c6c84964b4aec80aeace187aab8cb2c3e55a72