将 keras 模型导出到 tflite

Question

I am trying to combine this two examples and create the tflite file for my android app.

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

This is my code:

# 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)                     

And at this line:

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

I got an exception:

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)]]

I am a beginner in the machine learning and absolutely have no idea what is this error about :-(

Can somebody explain to me what is wrong? All that I need is processed several folders with many pics and make it possible to predict the relations of the new coming pictures to the certain folder. Thank you.

Answer 1

It is possible to directly convert a keras-model to .tflite using the tf.lite.TFLiteConverter.from_session function. Place the following code after fit_generator to export it (tested with tensorflow 1.3.1)

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

A bit late to the party but here's how you do it:

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

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