将基本的Tensorflow模型导出到Google Cloud ML

Question

我试图导出我的本地张量流模型以在Google Cloud ML上使用它并对其运行预测。我正在关注tensorflow serving example with mnist data。他们处理和使用他们的输入/输出向量的方式有很多不同之处，而这并不是您在在线典型示例中找到的。

我不能确定如何设置我的签名的参数：

model_exporter.init(
    sess.graph.as_graph_def(), 
    init_op = init_op, 
    default_graph_signature = exporter.classification_signature(
     input_tensor = "**UNSURE**" , 
     scores_tensor = "**UNSURE**"), 
    named_graph_signatures = { 
     'inputs' : "**UNSURE**", 
     'outputs': "**UNSURE**" 
    } 

    ) 
model_exporter.export(export_path, "**UNSURE**", sess) 

这里是我的代码的其余部分：

import sys 
import tensorflow as tf 
from tensorflow.contrib.session_bundle import exporter 

import numpy as np 
from newpreprocess import create_feature_sets_and_labels 

train_x,train_y,test_x,test_y = create_feature_sets_and_labels() 

x = tf.placeholder('float', [None, 13]) 
y = tf.placeholder('float', [None, 1]) 

n_nodes_hl1 = 20 
n_nodes_hl2 = 20 

n_classes = 1 
batch_size = 100 

def neural_network_model(data): 

    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([13, n_nodes_hl1])), 
         'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))} 

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 
         'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))} 

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_classes])), 
        'biases':tf.Variable(tf.random_normal([n_classes]))} 


    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases']) 
    l1 = tf.tanh(l1) 

    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases']) 
    l2 = tf.tanh(l2) 

    output = tf.add(tf.matmul(l2, output_layer['weights']), output_layer['biases']) 
    return output 



def train_neural_network(x): 
    output = neural_network_model(x) 
    cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output, y)) 
    optimizer = tf.train.AdamOptimizer(0.003).minimize(cost) 

    hm_epochs = 700 

    with tf.Session() as sess: 
     sess.run(tf.initialize_all_variables()) 

     for epoch in range(hm_epochs): 
      epoch_loss = 0 
      i = 0 
      while i < len(train_x): 
       start = i 
       end = i + batch_size 
       batch_x = np.array(train_x[start:end]) 
     batch_y = np.array(train_y[start:end]) 

     _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, 
               y: batch_y}) 
     epoch_loss += c 
     i+=batch_size 

      print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss/(len(train_x)/batch_size)) 


     prediction = tf.sigmoid(output) 
     predicted_class = tf.greater(prediction,0.5) 
     correct = tf.equal(predicted_class, tf.equal(y,1.0)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 

     print('Accuracy:', accuracy.eval({x: test_x, y: test_y})) 

     export_path = "~/Documents/cloudcomputing/Project/RNN_timeseries/" 
     print ("Exporting trained model to %s", %export_path) 
     init_op = tf.group(tf.initialize_all_tables(), name="init_op") 
     saver = tf.train.Saver(sharded = True) 
     model_exporter = exporter.Exporter(saver) 
     model_exporter.init(
      sess.graph.as_graph_def(), 
      init_op = init_op, 
      default_graph_signature = exporter.classification_signature(
       input_tensor = , 
       scores_tensor =), 
      named_graph_signatures = { 
       'inputs' : , 
       'outputs': 
      } 

      ) 
     model_exporter.export(export_path, tf.constant(1), sess) 
     print("Done exporting!") 



train_neural_network(x) 

究竟是上传和使用在谷歌的步骤Cloud ML？他们的演练似乎是在云本身而不是本地机器上训练的模型。

Answer 1

Tensorflow服务和Google Cloud ML是两回事，不要混淆。 Cloud ML是一个完全托管的解决方案（ML作为服务），而TF服务则要求您设置和维护您的基础架构 - 它只是一台服务器。它们不相关，在输入/输出处理方面有不同的要求。

您应遵循的指南是this one。您可以将输入和输出添加到集合中，而不是使用图形签名。然后在你的代码的变化将是这样的：

import sys 
import tensorflow as tf 
from tensorflow.contrib.session_bundle import exporter 

import numpy as np 
from newpreprocess import create_feature_sets_and_labels 
import json 
import os 

train_x,train_y,test_x,test_y = create_feature_sets_and_labels() 

n_nodes_hl1 = 20 
n_nodes_hl2 = 20 
n_classes = 1 
batch_size = 100 

x = tf.placeholder('float', [None, 13]) 
y = tf.placeholder('float', [None, 1]) 
keys_placeholder = tf.placeholder(tf.int64, shape=(None,)) 

keys = tf.identity(keys_placeholder) 

def neural_network_model(data): 
    hidden_1_layer = {'weights':tf.Variable(tf.random_normal([13, n_nodes_hl1])), 
         'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))} 
    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 
         'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))} 
    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_classes])), 
        'biases':tf.Variable(tf.random_normal([n_classes]))} 
    l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases']) 
    l1 = tf.tanh(l1) 
    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases']) 
    l2 = tf.tanh(l2) 
    output = tf.add(tf.matmul(l2, output_layer['weights']), output_layer['biases']) 
    return output 

output = neural_network_model(x) 
prediction = tf.sigmoid(output) 
predicted_class = tf.greater(prediction,0.5) 


inputs = {'key': keys_placeholder.name, 'x': x.name} 
tf.add_to_collection('inputs', json.dumps(inputs)) 

outputs = {'key': keys.name, 
      'prediction': predicted_class.name} 
tf.add_to_collection('outputs', json.dumps(outputs)) 


def train_neural_network(x): 
    cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output, y)) 
    optimizer = tf.train.AdamOptimizer(0.003).minimize(cost) 
    hm_epochs = 700 

    with tf.Session() as sess: 
     sess.run(tf.initialize_all_variables()) 
     for epoch in range(hm_epochs): 
      epoch_loss = 0 
      i = 0 
      while i < len(train_x): 
       start = i 
       end = i + batch_size 
       batch_x = np.array(train_x[start:end]) 
       batch_y = np.array(train_y[start:end]) 

       _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, 
               y: batch_y}) 
       epoch_loss += c 
       i+=batch_size 
      print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss/(len(train_x)/batch_size)) 

     correct = tf.equal(predicted_class, tf.equal(y,1.0)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Accuracy:', accuracy.eval({x: test_x, y: test_y})) 

     export_path = "~/Documents/cloudcomputing/Project/RNN_timeseries/" 
     print ("Exporting trained model to %s", %export_path) 
     init_op = tf.group(tf.initialize_all_tables(), name="init_op") 

     saver = tf.train.Saver(sharded = True) 
     saver.save(sess, os.path.join(export_path, 'export')) 

     print("Done exporting!") 

train_neural_network(x) 

我有些感动的东西在你的代码一点点（并没有实际测试过），但应该给你一个起点。