Export a basic Tensorflow model to Google Cloud ML

I am trying to export my local tensorflow model to use it on Google Cloud ML and run predictions on it.

I am following the tensorflow serving example with mnist data. There is quite a bit of difference in the way they have processed and used their input/output vectors and it is not what you find in typical examples online.

I am unsure how to set the parameters of my signatures :

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)

Here is the rest of my code:

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)

What exactly are the steps to upload and use this on Google Cloud ML? Their walkthroughs seem to be for models trained on the cloud itself and not on local machines.

Tensorflow Serving and Google Cloud ML are two different things, don't mix them up. Cloud ML is a fully managed solution (ML as a service), whereas TF Serving requires you to set up and maintain your infrastructure - it's just a server. They are unrelated and have different requirements in input/output handling.

The guide that you should follow is this one. Instead of using graph signatures you add inputs and outputs into collections. The changes in your code would then be something like this:

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)

I moved some things in your code a little (and haven't actually tested it), but that should give you a starting point.