CODEWITHSUNDEEP
neural-networktensorflowdeep-learninglstmrecurrent-neural-network
Sasgorilla
Sasgorilla

Reputation: 3130

How do I actually execute a saved TensorFlow model?

Tensorflow newbie here. I'm trying to build an RNN. My input data is a set of vector instances of size instance_size representing the (x,y) positions of a set of particles at each time step. (Since the instances already have semantic content, they do not require an embedding.) The goal is to learn to predict the positions of the particles at the next step.

Following the RNN tutorial and slightly adapting the included RNN code, I create a model more or less like this (omitting some details):

inputs, self._input_data = tf.placeholder(tf.float32, [batch_size, num_steps, instance_size])
self._targets = tf.placeholder(tf.float32, [batch_size, num_steps, instance_size])

with tf.variable_scope("lstm_cell", reuse=True):
  lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_size, forget_bias=0.0)
  if is_training and config.keep_prob < 1:
    lstm_cell = tf.nn.rnn_cell.DropoutWrapper(
        lstm_cell, output_keep_prob=config.keep_prob)
  cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * config.num_layers)

self._initial_state = cell.zero_state(batch_size, tf.float32)

from tensorflow.models.rnn import rnn
inputs = [tf.squeeze(input_, [1])
          for input_ in tf.split(1, num_steps, inputs)]
outputs, state = rnn.rnn(cell, inputs, initial_state=self._initial_state)

output = tf.reshape(tf.concat(1, outputs), [-1, hidden_size])
softmax_w = tf.get_variable("softmax_w", [hidden_size, instance_size])
softmax_b = tf.get_variable("softmax_b", [instance_size])
logits = tf.matmul(output, softmax_w) + softmax_b
loss = position_squared_error_loss(
    tf.reshape(logits, [-1]),
    tf.reshape(self._targets, [-1]),
)
self._cost = cost = tf.reduce_sum(loss) / batch_size
self._final_state = state

Then I create a saver = tf.train.Saver(), iterate over the data to train it using the given run_epoch() method, and write out the parameters with saver.save(). So far, so good.

But how do I actually use the trained model? The tutorial stops at this point. From the docs on tf.train.Saver.restore(), in order to read back in the variables, I need to either set up exactly the same graph I was running when I saved the variables out, or selectively restore particular variables. Either way, that means my new model will require inputs of size batch_size x num_steps x instance_size. However, all I want now is to do a single forward pass through the model on an input of size num_steps x instance_size and read out a single instance_size-sized result (the prediction for the next time step); in other words, I want to create a model that accepts a different-size tensor than the one I trained on. I can kludge it by passing the existing model my intended data batch_size times, but that doesn't seem like a best practice. What's the best way to do this?

Upvotes: 4

Views: 781

Answers (1)

RaduK
RaduK

Reputation: 1463

You have to create a new graph that has the same structure but with the batch_size = 1 and import the saved variables with tf.train.Saver.restore(). You can take a look at how they define multiple models with variable batch size in ptb_word_lm.py: https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/rnn/ptb/ptb_word_lm.py

So you can have a separate file for instance, where you instantiate the graph with the batch_size that you want, then restore the saved variables. Then you can execute your graph.

Upvotes: 2

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