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

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 + 1import tensorflow as tfimport numpy as npimport randomdef 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 out4def 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

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

需要注意：
compile(name: ‘tensorflow-android-1.6.0-rc0’, ext: ‘aar’)
“name: “:后要有空格
“ext: ” :后要有空格

添加模型文件

编写tensorflow mobile API的封装

最后在Activity调就可以了

给出完整项目的GitHub链接:
https://github.com/imu-hupeng/TensorflowTestApp

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