python - 将Keras模型导出到tflite

我试图将这两个例子结合起来，为我的android应用程序创建tflite文件。
https://medium.com/nybles/create-your-first-image-recognition-classifier-using-cnn-keras-and-tensorflow-backend-6eaab98d14dd
https://medium.com/@xianbao.qian/convert-keras-model-to-tflite-e2bdf28ee2d2
这是我的代码：

# Part 1 - Building the CNN

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
import tensorflow as tf
from keras.models import load_model


# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

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

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

classifier.fit_generator(training_set,
                         samples_per_epoch = 80,
                         nb_epoch = 1,
                         validation_data = test_set,
                         nb_val_samples = 20)



output_names = [node.op.name for node in classifier.outputs]
sess = tf.keras.backend.get_session()
frozen_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, output_names)


tflite_model = tf.contrib.lite.toco_convert(frozen_def, [inputs], output_names)
with tf.gfile.GFile(tflite_graph, 'wb') as f:
    f.write(tflite_model)

在这一行：

frozen_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, output_names)

我有个例外：

tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value conv2d_1/bias
[[Node: _retval_conv2d_1/bias_0_0 = _Retval[T=DT_FLOAT, index=0, _device="/job:localhost/replica:0/task:0/device:CPU:0"](conv2d_1/bias)]]

我是机器学习的初学者，完全不知道这个错误是关于什么的：-（
有人能告诉我怎么了吗？
我所需要的是处理几个文件夹和许多图片，使之能够预测新来的图片与特定文件夹的关系。
谢谢您。

最佳答案

可以使用.tflite函数将keras模型直接转换为tf.lite.TFLiteConverter.from_session。在fit_generator之后放置以下代码以导出它（使用tensorflow 1.3.1测试）

with tf.keras.backend.get_session() as sess:
    sess.run(tf.global_variables_initializer())
    converter = tf.lite.TFLiteConverter.from_session(sess, model.inputs, model.outputs)
    tflite_model = converter.convert()
    with open("model.tflite", "wb") as f:
        f.write(tflite_model)