# Copyright 2016 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """A library showing off sequence recognition and generation with the simple example of names. We use recurrent neural nets to learn complex functions able to recognize and generate sequences of a given form. This can be used for natural language syntax recognition, dynamically generating maps or puzzles and of course baby name generation. Before using this module, it is recommended to read the Tensorflow tutorial on recurrent neural nets, as it explains the basic concepts of this model, and will show off another module, the PTB module on which this model bases itself. Here is an overview of the functions available in this module: * RNN Module for sequence functions based on PTB * Name recognition specifically for recognizing names, but can be adapted to recognizing sequence patterns * Name generations specifically for generating names, but can be adapted to generating arbitrary sequence patterns """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import time import tensorflow as tf import numpy as np from model import NamignizerModel import data_utils class SmallConfig(object): """Small config.""" init_scale = 0.1 learning_rate = 1.0 max_grad_norm = 5 num_layers = 2 num_steps = 20 hidden_size = 200 max_epoch = 4 max_max_epoch = 13 keep_prob = 1.0 lr_decay = 0.5 batch_size = 20 vocab_size = 27 epoch_size = 100 class LargeConfig(object): """Medium config.""" init_scale = 0.05 learning_rate = 1.0 max_grad_norm = 5 num_layers = 2 num_steps = 35 hidden_size = 650 max_epoch = 6 max_max_epoch = 39 keep_prob = 0.5 lr_decay = 0.8 batch_size = 20 vocab_size = 27 epoch_size = 100 class TestConfig(object): """Tiny config, for testing.""" init_scale = 0.1 learning_rate = 1.0 max_grad_norm = 1 num_layers = 1 num_steps = 2 hidden_size = 2 max_epoch = 1 max_max_epoch = 1 keep_prob = 1.0 lr_decay = 0.5 batch_size = 20 vocab_size = 27 epoch_size = 100 def run_epoch(session, m, names, counts, epoch_size, eval_op, verbose=False): """Runs the model on the given data for one epoch Args: session: the tf session holding the model graph m: an instance of the NamignizerModel names: a set of lowercase names of 26 characters counts: a list of the frequency of the above names epoch_size: the number of batches to run eval_op: whether to change the params or not, and how to do it Kwargs: verbose: whether to print out state of training during the epoch Returns: cost: the average cost during the last stage of the epoch """ start_time = time.time() costs = 0.0 iters = 0 for step, (x, y) in enumerate(data_utils.namignizer_iterator(names, counts, m.batch_size, m.num_steps, epoch_size)): cost, _ = session.run([m.cost, eval_op], {m.input_data: x, m.targets: y, m.weights: np.ones(m.batch_size * m.num_steps)}) costs += cost iters += m.num_steps if verbose and step % (epoch_size // 10) == 9: print("%.3f perplexity: %.3f speed: %.0f lps" % (step * 1.0 / epoch_size, np.exp(costs / iters), iters * m.batch_size / (time.time() - start_time))) if step >= epoch_size: break return np.exp(costs / iters) def train(data_dir, checkpoint_path, config): """Trains the model with the given data Args: data_dir: path to the data for the model (see data_utils for data format) checkpoint_path: the path to save the trained model checkpoints config: one of the above configs that specify the model and how it should be run and trained Returns: None """ # Prepare Name data. print("Reading Name data in %s" % data_dir) names, counts = data_utils.read_names(data_dir) with tf.Graph().as_default(), tf.Session() as session: initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale) with tf.variable_scope("model", reuse=None, initializer=initializer): m = NamignizerModel(is_training=True, config=config) tf.global_variables_initializer().run() for i in range(config.max_max_epoch): lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0) m.assign_lr(session, config.learning_rate * lr_decay) print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr))) train_perplexity = run_epoch(session, m, names, counts, config.epoch_size, m.train_op, verbose=True) print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity)) m.saver.save(session, checkpoint_path, global_step=i) def namignize(names, checkpoint_path, config): """Recognizes names and prints the Perplexity of the model for each names in the list Args: names: a list of names in the model format checkpoint_path: the path to restore the trained model from, should not include the model name, just the path to config: one of the above configs that specify the model and how it should be run and trained Returns: None """ with tf.Graph().as_default(), tf.Session() as session: with tf.variable_scope("model"): m = NamignizerModel(is_training=False, config=config) m.saver.restore(session, checkpoint_path) for name in names: x, y = data_utils.name_to_batch(name, m.batch_size, m.num_steps) cost, loss, _ = session.run([m.cost, m.loss, tf.no_op()], {m.input_data: x, m.targets: y, m.weights: np.concatenate(( np.ones(len(name)), np.zeros(m.batch_size * m.num_steps - len(name))))}) print("Name {} gives us a perplexity of {}".format( name, np.exp(cost))) def namignator(checkpoint_path, config): """Generates names randomly according to a given model Args: checkpoint_path: the path to restore the trained model from, should not include the model name, just the path to config: one of the above configs that specify the model and how it should be run and trained Returns: None """ # mutate the config to become a name generator config config.num_steps = 1 config.batch_size = 1 with tf.Graph().as_default(), tf.Session() as session: with tf.variable_scope("model"): m = NamignizerModel(is_training=False, config=config) m.saver.restore(session, checkpoint_path) activations, final_state, _ = session.run([m.activations, m.final_state, tf.no_op()], {m.input_data: np.zeros((1, 1)), m.targets: np.zeros((1, 1)), m.weights: np.ones(1)}) # sample from our softmax activations next_letter = np.random.choice(27, p=activations[0]) name = [next_letter] while next_letter != 0: activations, final_state, _ = session.run([m.activations, m.final_state, tf.no_op()], {m.input_data: [[next_letter]], m.targets: np.zeros((1, 1)), m.initial_state: final_state, m.weights: np.ones(1)}) next_letter = np.random.choice(27, p=activations[0]) name += [next_letter] print(map(lambda x: chr(x + 96), name)) if __name__ == "__main__": train("data/SmallNames.txt", "model/namignizer", SmallConfig) namignize(["mary", "ida", "gazorbazorb", "mmmhmm", "bob"], tf.train.latest_checkpoint("model"), SmallConfig) namignator(tf.train.latest_checkpoint("model"), SmallConfig)