有不同的方法来做这个。纯粹的，使用张量不是很灵活，但是相对简单。这种方法的不足之处在于，您必须重新生成图形并在恢复模型的代码中初始化变量。在 tensorflow skflow / contrib learn 中显示的方式更多优雅，但是这似乎并没有在目前功能和文件已过时。

我在github 这里上放了一个简短的例子，将GET或POST参数命名为瓶型REST部署的tensorflow模型。

然后，主要的代码在一个基于POST / GET数据的字典中：

$ parse_postget $ b $ def apply_model（$）$ b $（$）$ app $ b tf.reset_default_graph（）
with tf.Session（）as session：
n = 1
x = tf.placeholder（tf.float32，[n]，name ='x'）
y = tf.placeholder（tf.float32，[n]，name ='y'）
m = tf.Variable（[1.0]，name ='m'）
b = tf.Variable（[ 1.0]，name ='b'）
y = tf.add（tf.mul（m，x），b）＃fit y_i = m * x_i + b
y_act = tf.placeholder（tf。 （0.05）.minimize（float32，[n]，name ='y_'）
error = tf.sqrt（（y - y_act）*（y - y_act））
train_step = tf.train.AdamOptimizer （error）

feed_dict = {x：np.array（[float（d ['x_in']）]），y_act：np.array（[float（d ['y_star']）] ）}
saver = tf.train.Saver（）
saver.restore（session ，'linear.chk'）
y_i，_，_ = session.run（[y，m，b]，feed_dict）
返回jsonify（输出= float（y_i））

I want to deploy a simple TensorFlow model and run it in REST service like Flask. Did not find so far good example on github or here.

I am not ready to use TF Serving as suggested in other posts, it is perfect solution for Google but it overkill for my tasks with gRPC, bazel, C++ coding, protobuf...

There are different ways to do this. Purely, using tensorflow is not very flexible, however relatively straightforward. The downside of this approach is that you have to rebuild the graph and initialize variables in the code where you restore the model. There is a way shown in tensorflow skflow/contrib learn which is more elegant, however this doesn't seem to be functional at the moment and the documentation is out of date.

I put a short example together on github here that shows how you would named GET or POST parameters to a flask REST-deployed tensorflow model.

The main code is then in a function that takes a dictionary based on the POST/GET data:

@app.route('/model', methods=['GET', 'POST'])
@parse_postget
def apply_model(d):
    tf.reset_default_graph()
    with tf.Session() as session:
        n = 1
        x = tf.placeholder(tf.float32, [n], name='x')
        y = tf.placeholder(tf.float32, [n], name='y')
        m = tf.Variable([1.0], name='m')
        b = tf.Variable([1.0], name='b')
        y = tf.add(tf.mul(m, x), b) # fit y_i = m * x_i + b
        y_act = tf.placeholder(tf.float32, [n], name='y_')
        error = tf.sqrt((y - y_act) * (y - y_act))
        train_step = tf.train.AdamOptimizer(0.05).minimize(error)

        feed_dict = {x: np.array([float(d['x_in'])]), y_act: np.array([float(d['y_star'])])}
        saver = tf.train.Saver()
        saver.restore(session, 'linear.chk')
        y_i, _, _ = session.run([y, m, b], feed_dict)
    return jsonify(output=float(y_i))