TensorFlow2.0部署

1. 安裝tensorflow2.0

這裏的環境都是基於Linux上進行
先升級pip
python3 -m pip install --upgrade pip
接着
python3 -m pip install tensorflow==2.0.0-beta1

官方安裝文檔

假如網速太慢，可以離線下載whl安裝包https://pypi.tuna.tsinghua.edu.cn/simple/tensorflow/

2. 生成tflite格式的模型

注意：把tensorflow模型部署到android，需要將tensorflow模型轉化爲tflite格式的模型；（實際上，keras部署也需要轉成tflite格式，並且tensorflow2.0提供了很方便的api操作，並且集成了keras，使用的時候需要在原來keras的基礎上加tensorflow，即tensorflow.keras）

這裏以keras爲例：
（1）編寫keras模型代碼（cnn模型）

//keras_cnn.py
from __future__ import print_function
import tensorflow.keras as keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K


batch_size = 128
num_classes = 10
epochs = 2  #12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

model.save('mnist_cnn.h5')

（2）讀取h5模型，轉tflite格式
注意：這裏的h5模型要使用tensorflow.keras的形式生成

//turn_keras_cnn.py
from tensorflow.keras.models import load_model
from tensorflow.python.keras import backend as k
import tensorflow as tf
model = load_model('mnist_cnn.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("mnist_cnn.tflite", "wb").write(tflite_model)

官方api

注意：圖中標識的爲2.0的api；如果安裝的不是2.0,應該參考上面的api