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I've trained my NN in Tensorflow and saved the model like this:

def neural_net(x):

   layer_1 = tf.layers.dense(inputs=x, units=195, activation=tf.nn.sigmoid)

   out_layer = tf.layers.dense(inputs=layer_1, units=6)

   return out_layer



train_x = pd.read_csv("data_x.csv", sep=" ")

train_y = pd.read_csv("data_y.csv", sep=" ")

train_x = train_x / 6 - 0.5



train_size = 0.9

train_cnt = int(floor(train_x.shape[0] * train_size))

x_train = train_x.iloc[0:train_cnt].values

y_train = train_y.iloc[0:train_cnt].values

x_test = train_x.iloc[train_cnt:].values

y_test = train_y.iloc[train_cnt:].values



x = tf.placeholder("float", [None, 386])

y = tf.placeholder("float", [None, 6])



nn_output = neural_net(x)



cost = tf.reduce_mean(tf.losses.mean_squared_error(labels=y, predictions=nn_output))

optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)



training_epochs = 5000

display_step = 1000

batch_size = 30



keep_prob = tf.placeholder("float")



saver = tf.train.Saver()

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    for epoch in range(training_epochs):

        total_batch = int(len(x_train) / batch_size)

        x_batches = np.array_split(x_train, total_batch)

        y_batches = np.array_split(y_train, total_batch)

        for i in range(total_batch):

            batch_x, batch_y = x_batches[i], y_batches[i]

            _, c = sess.run([optimizer, cost], 

                            feed_dict={

                                x: batch_x, 

                                y: batch_y, 

                                keep_prob: 0.8

                            })

    saver.save(sess, 'trained_model', global_step=1000)

Now I want to use the trained model in a different file. Of course there are many many examples of restoring and saving the model, I went through lots of them. Still I couldn't make any of them work, there is always some kind of error. So this is my restore file, could you please help me to make it restore the saved model?

saver = tf.train.import_meta_graph('trained_model-1000.meta')

y_pred = 

with tf.Session() as sess:

    saver.restore(sess, tf.train.latest_checkpoint('./'))

    sess.run([y_pred], feed_dict={x: input_values})

E.g. this attempt gave me the error "The session graph is empty. Add operations to the graph before calling run()." So what operation should I add to the graph and how? I don't know what that operation should be in my model... I don't understand this whole concept of saving/restoring in Tensorflow. Or should I do the restoring completely differently? Thanks in advance!

Forgive me if I am wrong but tf.train.Saver() only saves the variable values not the graph itself. This means that if you want to load the model in a different file you need to rebuild the graph or somehow load the graph as well. Tensorflow documentation states:

The tf.train.Saver object not only saves variables to checkpoint files, it also restores variables. Note that when you restore variables from a file you do not have to initialize them beforehand.

Consider the following example:

One file that saves the model:

# Create some variables.

v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer) 

v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)



inc_v1 = v1.assign(v1+1)

dec_v2 = v2.assign(v2-1)



# Add an op to initialize the variables.

init_op = tf.global_variables_initializer()



# Add ops to save and restore all the variables.

saver = tf.train.Saver()



# Later, launch the model, initialize the variables, do some work, and save the

# variables to disk.

with tf.Session() as sess:

    sess.run(init_op)

    # Do some work with the model.

    inc_v1.op.run()

    dec_v2.op.run()

    # Save the variables to disk.

    save_path = saver.save(sess, "/tmp/model.ckpt")

    print("Model saved in file: %s" % save_path)

The other file that loads the previously saved model:

tf.reset_default_graph()



# Create some variables.

v1 = tf.get_variable("v1", shape=[3])

v2 = tf.get_variable("v2", shape=[5])



# Add ops to save and restore all the variables.

saver = tf.train.Saver()



# Later, launch the model, use the saver to restore variables from disk, and

# do some work with the model.

with tf.Session() as sess:

   # Restore variables from disk.

   saver.restore(sess, "/tmp/model.ckpt")

   print("Model restored.")

   # Check the values of the variables

   print("v1 : %s" % v1.eval())

   print("v2 : %s" % v2.eval())

 output = sess.run(nn_output, feed_dict={ x: batch_x, keep_prob: 0.8 })

Where nn_output is the name is the output variable of the last layer of you network. You can save you variable using:

saver = tf.train.Saver([nn_output])

saver.save(sess, 'my_test_model',global_step=1000) # save every 1000 steps

and therefore in your code:

out_layer = tf.layers.dense(inputs=layer_1, units=6)

should be :

out_layer = tf.layers.dense(inputs=layer_1, units=6, name='nn_output')

To restore:

with tf.Session() as sess:    

saver = tf.train.import_meta_graph('my_test_model')

saver.restore(sess,tf.train.latest_checkpoint('./'))

Now you should have access to that node of the graph. If the name is not specified, it is difficult to recover that particular layer.

You can know use tf.saved_model.builder.SavedModelBuilder function.

The main lines for the saving:

builder = tf.saved_model.builder.SavedModelBuilder(graph_location)



builder.add_meta_graph_and_variables(sess, ["cnn_mnist"])



builder.save()

A code to save the model :

...

def main(_):

  # Import data

  mnist = input_data.read_data_sets(FLAGS.data_dir)



  # Create the model

  x = tf.placeholder(tf.float32, [None, 784])



  # Define loss and optimizer

  y_ = tf.placeholder(tf.int64, [None])



  # Build the graph for the deep net

  y_conv, keep_prob = deepnn(x) # an unknow model model



  with tf.name_scope('loss'):

    cross_entropy = tf.losses.sparse_softmax_cross_entropy(

        labels=y_, logits=y_conv)

  cross_entropy = tf.reduce_mean(cross_entropy)



  with tf.name_scope('adam_optimizer'):

    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)



  with tf.name_scope('accuracy'):

    correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)

    correct_prediction = tf.cast(correct_prediction, tf.float32)

  accuracy = tf.reduce_mean(correct_prediction)



  graph_location ="tmp/"

  print('Saving graph to: %s' % graph_location)

  **builder = tf.saved_model.builder.SavedModelBuilder(graph_location)**



  train_writer = tf.summary.FileWriter(graph_location)

  train_writer.add_graph(tf.get_default_graph())



  saver = tf.train.Saver(max_to_keep=1)



  with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    **builder.add_meta_graph_and_variables(sess, ["cnn_mnist"])**

    for i in range(20000):

      batch = mnist.train.next_batch(50)

      if i % 100 == 0:

        train_accuracy = accuracy.eval(feed_dict={

            x: batch[0], y_: batch[1], keep_prob: 1.0})

        print('step %d, training accuracy %g' % (i, train_accuracy))

      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})



    print('test accuracy %g' % accuracy.eval(feed_dict={

        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))



    **builder.save()**

    saver.save(sess, "tmp/my_checkpoint.ckpt", global_step =0)



if __name__ == '__main__':

  parser = argparse.ArgumentParser()

  parser.add_argument('--data_dir', type=str,

                      default='/tmp/tensorflow/mnist/input_data',

                      help='Directory for storing input data')

  FLAGS, unparsed = parser.parse_known_args()

  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

`

A code to restore the model :

import tensorflow as tf



# récupération des poids 



export_dir = "tmp"

sess = tf.Session()

tf.saved_model.loader.load(sess,["cnn_mnist"], export_dir)



#trainable_var = tf.trainable_variables()

trainable_var = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)

for var in trainable_var:

    print(var.name)`