这里的环境都是基于Linux上进行
先升级pip
python3 -m pip install --upgrade pip
接着
python3 -m pip install tensorflow==2.0.0-beta1
官方安装文档
假如网速太慢,可以离线下载whl安装包https://pypi.tuna.tsinghua.edu.cn/simple/tensorflow/
注意:把tensorflow模型部署到android,需要将tensorflow模型转化为tflite格式的模型;(实际上,keras部署也需要转成tflite格式,并且tensorflow2.0提供了很方便的api操作,并且集成了keras,使用的时候需要在原来keras的基础上加tensorflow,即tensorflow.keras)
这里以keras为例:
(1)编写keras模型代码(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)读取h5模型,转tflite格式
注意:这里的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)