将 keras 模型导出到 tflite

Question

我正在尝试结合这两个示例并为我的 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)

我有一个例外：

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什么:-(

有人可以向我解释什么是错的吗？ 我所需要的只是处理多个包含许多图片的文件夹，并可以预测新图片与特定文件夹的关系。 谢谢你。

Answer 1

可以使用tf.lite.TFLiteConverter.from_session函数将 keras 模型直接转换为.tflite 。 将下面的代码fit_generator之后导出（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)   

Answer 2

参加聚会有点晚了，但您可以这样做：

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

来源： https : //www.tensorflow.org/lite/convert/python_api