| |
| """ |
| Provide functions and classes: |
| Model = Class for loading and using trained models from tensorflow |
| create_cell = function for creatting RNN cells with wrappers |
| """ |
| |
| import tensorflow.compat.v1 as tf |
| from tensorflow.python.ops.rnn_cell_impl import LSTMCell, ResidualWrapper, DropoutWrapper, MultiRNNCell |
|
|
| class Model(): |
| """Loading and running isolated tf graph.""" |
| def __init__(self, loc, operation='activation', input_name='x'): |
| """ |
| loc: location of file containing saved model |
| operation: name of operation for running the model |
| input_name: name of input placeholder |
| """ |
| self.input = input_name + ":0" |
| self.graph = tf.Graph() |
| self.sess = tf.Session(graph=self.graph) |
| with self.graph.as_default(): |
| saver = tf.train.import_meta_graph(loc + '.meta', clear_devices=True) |
| saver.restore(self.sess, loc) |
| self.op = self.graph.get_operation_by_name(operation).outputs[0] |
|
|
| def run(self, data): |
| """Run the specified operation on given data.""" |
| return self.sess.run(self.op, feed_dict={self.input: data}) |
| |
| def eval_feed(self, feed): |
| """Run the specified operation with given feed.""" |
| return self.sess.run(self.op, feed_dict=feed) |
| |
| def run_op(self, op, feed, output=True): |
| """Run given operation with the feed.""" |
| if output: |
| return self.sess.run( |
| self.graph.get_operation_by_name(op).outputs[0], |
| feed_dict=feed) |
| else: |
| self.sess.run( |
| self.graph.get_operation_by_name(op), |
| feed_dict=feed) |
| |
| |
| |
| def _create_single_cell(cell_fn, num_units, is_residual=False, is_dropout=False, keep_prob=None): |
| """Create single RNN cell based on cell_fn.""" |
| cell = cell_fn(num_units) |
| if is_dropout: |
| cell = DropoutWrapper(cell, input_keep_prob=keep_prob) |
| if is_residual: |
| cell = ResidualWrapper(cell) |
| return cell |
|
|
|
|
| def create_cell(num_units, num_layers, num_residual_layers, is_dropout=False, keep_prob=None, cell_fn=LSTMCell): |
| """Create corresponding number of RNN cells with given wrappers.""" |
| cell_list = [] |
| |
| for i in range(num_layers): |
| cell_list.append(_create_single_cell( |
| cell_fn=cell_fn, |
| num_units=num_units, |
| is_residual=(i >= num_layers - num_residual_layers), |
| is_dropout=is_dropout, |
| keep_prob=keep_prob |
| )) |
|
|
| if num_layers == 1: |
| return cell_list[0] |
| return MultiRNNCell(cell_list) |
