Tensorflow：Android调用Tensorflow Mobile版本API（2）-基于Android的调用

最新推荐文章于 2024-08-18 16:27:38 发布

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最新推荐文章于 2024-08-18 16:27:38 发布

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分类专栏： android python3

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本文介绍如何在Android项目中导入1.6.0版本的TensorFlow Mobile库，详细步骤包括创建新项目、添加离线库依赖、放置模型文件及封装TensorFlow Mobile API。通过这些操作，可以在Android应用中顺利调用TensorFlow模型。项目源码已上传至GitHub，供读者参考。

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对上一篇博客中代码略做修改，在训练完成之后进行模型导出操作

# y = x^2 + 1

import tensorflow as tf
import numpy as np
import random

def get_batch(size=128):
    xs = []
    ys = []
    for i in range(size):
        x = random.random() * 2
        y = x * x + 1
        xs.append(x)
        ys.append(y)
    return np.array(xs), np.array(ys)




X = tf.placeholder(tf.float32, [None,1], name='input')
Y = tf.placeholder(tf.float32, [None,1])
def my_dnn():
    x = tf.reshape(X, shape=[-1, 1])
    w1 = tf.Variable(tf.random_normal(shape=[1,256], mean=0.0,
                                      stddev=1))
    b1 = tf.Variable(tf.random_normal([256]))
    out1 = tf.nn.bias_add(tf.matmul(x,w1),b1)
    out1 = tf.nn.relu(out1)
    w2= tf.Variable(tf.random_normal(shape=[256,256]))
    b2 = tf.Variable(tf.random_normal([256]))
    out2= tf.nn.bias_add(tf.matmul(out1, w2),b2)
    out2 = tf.nn.relu(out2)
    w3 = tf.Variable(tf.random_normal(shape=[256, 256]))
    b3 = tf.Variable(tf.random_normal([256]))
    out3 = tf.nn.bias_add(tf.matmul(out2, w3),b3)
    out3 = tf.nn.relu(out3)
    w4 = tf.Variable(tf.random_normal(shape=[256, 1]))
    b4 = tf.Variable(tf.random_normal([1]))
    out4 = tf.nn.bias_add(tf.matmul(out3, w4), b4, name='output')


    return out4
def train():
    out = my_dnn()
    loss = tf.reduce_mean(tf.square(Y - out))
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())
        step = 0
        while True:
            batch_x, batch_y = get_batch(64)
            batch_x = batch_x.reshape([-1, 1])
            batch_y = batch_y.reshape([-1, 1])
            _, loss_ = sess.run([optimizer, loss], feed_dict={X:batch_x, Y:batch_y})
            print(loss_)
            if loss_ < 0.0001:
                saver.save(sess, "./1.model", global_step=step)
                break
            step += 1


# train()

def eval():
    out = my_dnn()
    saver = tf.train.Saver()
    with tf.Session() as sess:
        saver.restore(sess, tf.train.latest_checkpoint('.'))
        for i in range(100):
            x = random.random() * 2
            x = np.array([x]).reshape([-1,1])
            y = sess.run(out, feed_dict={X:x})
            print("x=%.5f 正确的y=%.5f 预测的 y=%.5f" % (x, x*x + 1, y))
def exportModel():
    out = my_dnn()
    saver = tf.train.Saver()
    with tf.Session() as sess:
        # 恢复模型参数
        saver.restore(sess, tf.train.latest_checkpoint('.'))
        from tensorflow.python.framework.graph_util import convert_variables_to_constants
        output_graph_def = convert_variables_to_constants(sess, sess.graph_def, output_node_names=['output'])
        with tf.gfile.FastGFile('1.pb', mode='wb') as f:
            f.write(output_graph_def.SerializeToString())

if __name__ == '__main__':
    # 训练
    # train()

    # 评估
    # eval()

    # 导出模型
    exportModel()

新建一个Android项目
这里写图片描述
导入tensorflow-mobile的库
可以选择导在线的库，在这里导入离线的库
https://jcenter.bintray.com/org/tensorflow/tensorflow-android/

我添加1.6.0版本的,修改了gradle文件，完成了添加