fix the readme

README.md

@@ -20,3 +20,22 @@ To propose a model for inclusion please submit a pull request.
 - [textsum](textsum) -- sequence-to-sequence with attention model for text summarization.
 - [transformer](transformer) -- spatial transformer network, which allows the spatial manipulation of data within the network
 - [im2txt](im2txt) -- image-to-text neural network for image captioning.
+=======
+Implementation of the Neural Programmer model described in https://openreview.net/pdf?id=ry2YOrcge
+
+Download the data from http://www-nlp.stanford.edu/software/sempre/wikitable/
+Change the data_dir FLAG to the location of the data
+
+Training:
+python neural_programmer.py
+
+The models are written to FLAGS.output_dir 
+
+
+Testing:
+python neural_programmer.py --evaluator_job=True
+
+The models are loaded from FLAGS.output_dir.
+The evaluation is done on development data.
+
+Maintained by Arvind Neelakantan (arvind2505)

neural_programmer.py

+# 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.
+# ==============================================================================
+"""Implementation of the Neural Programmer model described in https://openreview.net/pdf?id=ry2YOrcge
+
+This file calls functions to load & pre-process data, construct the TF graph
+and performs training or evaluation as specified by the flag evaluator_job
+Author: aneelakantan (Arvind Neelakantan)
+"""
+import time
+from random import Random
+import numpy as np
+import tensorflow as tf
+import model
+import wiki_data
+import parameters
+import data_utils
+
+tf.flags.DEFINE_integer("train_steps", 100001, "Number of steps to train")
+tf.flags.DEFINE_integer("eval_cycle", 500,
+                        "Evaluate model at every eval_cycle steps")
+tf.flags.DEFINE_integer("max_elements", 100,
+                        "maximum rows that are  considered for processing")
+tf.flags.DEFINE_integer(
+    "max_number_cols", 15,
+    "maximum number columns that are considered for processing")
+tf.flags.DEFINE_integer(
+    "max_word_cols", 25,
+    "maximum number columns that are considered for processing")
+tf.flags.DEFINE_integer("question_length", 62, "maximum question length")
+tf.flags.DEFINE_integer("max_entry_length", 1, "")
+tf.flags.DEFINE_integer("max_passes", 4, "number of operation passes")
+tf.flags.DEFINE_integer("embedding_dims", 256, "")
+tf.flags.DEFINE_integer("batch_size", 20, "")
+tf.flags.DEFINE_float("clip_gradients", 1.0, "")
+tf.flags.DEFINE_float("eps", 1e-6, "")
+tf.flags.DEFINE_float("param_init", 0.1, "")
+tf.flags.DEFINE_float("learning_rate", 0.001, "")
+tf.flags.DEFINE_float("l2_regularizer", 0.0001, "")
+tf.flags.DEFINE_float("print_cost", 50.0,
+                      "weighting factor in the objective function")
+tf.flags.DEFINE_string("job_id", "temp", """job id""")
+tf.flags.DEFINE_string("output_dir", "../model/",
+                       """output_dir""")
+tf.flags.DEFINE_string("data_dir", "../data/",
+                       """data_dir""")
+tf.flags.DEFINE_integer("write_every", 500, "wrtie every N")
+tf.flags.DEFINE_integer("param_seed", 150, "")
+tf.flags.DEFINE_integer("python_seed", 200, "")
+tf.flags.DEFINE_float("dropout", 0.8, "dropout keep probability")
+tf.flags.DEFINE_float("rnn_dropout", 0.9,
+                      "dropout keep probability for rnn connections")
+tf.flags.DEFINE_float("pad_int", -20000.0,
+                      "number columns are padded with pad_int")
+tf.flags.DEFINE_string("data_type", "double", "float or double")
+tf.flags.DEFINE_float("word_dropout_prob", 0.9, "word dropout keep prob")
+tf.flags.DEFINE_integer("word_cutoff", 10, "")
+tf.flags.DEFINE_integer("vocab_size", 10800, "")
+tf.flags.DEFINE_boolean("evaluator_job", False,
+                        "wehther to run as trainer/evaluator")
+tf.flags.DEFINE_float(
+    "bad_number_pre_process", -200000.0,
+    "number that is added to a corrupted table entry in a number column")
+tf.flags.DEFINE_float("max_math_error", 3.0,
+                      "max square loss error that is considered")
+tf.flags.DEFINE_float("soft_min_value", 5.0, "")
+FLAGS = tf.flags.FLAGS
+
+
+class Utility:
+  #holds FLAGS and other variables that are used in different files
+  def __init__(self):
+    global FLAGS
+    self.FLAGS = FLAGS
+    self.unk_token = "UNK"
+    self.entry_match_token = "entry_match"
+    self.column_match_token = "column_match"
+    self.dummy_token = "dummy_token"
+    self.tf_data_type = {}
+    self.tf_data_type["double"] = tf.float64
+    self.tf_data_type["float"] = tf.float32
+    self.np_data_type = {}
+    self.np_data_type["double"] = np.float64
+    self.np_data_type["float"] = np.float32
+    self.operations_set = ["count"] + [
+        "prev", "next", "first_rs", "last_rs", "group_by_max", "greater",
+        "lesser", "geq", "leq", "max", "min", "word-match"
+    ] + ["reset_select"] + ["print"]
+    self.word_ids = {}
+    self.reverse_word_ids = {}
+    self.word_count = {}
+    self.random = Random(FLAGS.python_seed)
+
+
+def evaluate(sess, data, batch_size, graph, i):
+  #computes accuracy
+  num_examples = 0.0
+  gc = 0.0
+  for j in range(0, len(data) - batch_size + 1, batch_size):
+    [ct] = sess.run([graph.final_correct],
+                    feed_dict=data_utils.generate_feed_dict(data, j, batch_size,
+                                                            graph))
+    gc += ct * batch_size
+    num_examples += batch_size
+  print "dev set accuracy   after ", i, " : ", gc / num_examples
+  print num_examples, len(data)
+  print "--------"
+
+
+def Train(graph, utility, batch_size, train_data, sess, model_dir,
+          saver):
+  #performs training
+  curr = 0
+  train_set_loss = 0.0
+  utility.random.shuffle(train_data)
+  start = time.time()
+  for i in range(utility.FLAGS.train_steps):
+    curr_step = i
+    if (i > 0 and i % FLAGS.write_every == 0):
+      model_file = model_dir + "/model_" + str(i)
+      saver.save(sess, model_file)
+    if curr + batch_size >= len(train_data):
+      curr = 0
+      utility.random.shuffle(train_data)
+    step, cost_value = sess.run(
+        [graph.step, graph.total_cost],
+        feed_dict=data_utils.generate_feed_dict(
+            train_data, curr, batch_size, graph, train=True, utility=utility))
+    curr = curr + batch_size
+    train_set_loss += cost_value
+    if (i > 0 and i % FLAGS.eval_cycle == 0):
+      end = time.time()
+      time_taken = end - start
+      print "step ", i, " ", time_taken, " seconds "
+      start = end
+      print " printing train set loss: ", train_set_loss / utility.FLAGS.eval_cycle
+      train_set_loss = 0.0
+
+
+def master(train_data, dev_data, utility):
+  #creates TF graph and calls trainer or evaluator
+  batch_size = utility.FLAGS.batch_size 
+  model_dir = utility.FLAGS.output_dir + "/model" + utility.FLAGS.job_id + "/"
+  #create all paramters of the model
+  param_class = parameters.Parameters(utility)
+  params, global_step, init = param_class.parameters(utility)
+  key = "test" if (FLAGS.evaluator_job) else "train"
+  graph = model.Graph(utility, batch_size, utility.FLAGS.max_passes, mode=key)
+  graph.create_graph(params, global_step)
+  prev_dev_error = 0.0
+  final_loss = 0.0
+  final_accuracy = 0.0
+  #start session
+  with tf.Session() as sess:
+    sess.run(init.name)
+    sess.run(graph.init_op.name)
+    to_save = params.copy()
+    saver = tf.train.Saver(to_save, max_to_keep=500)
+    if (FLAGS.evaluator_job):
+      while True:
+        selected_models = {}
+        file_list = tf.gfile.ListDirectory(model_dir)
+        for model_file in file_list:
+          if ("checkpoint" in model_file or "index" in model_file or
+              "meta" in model_file):
+            continue
+          if ("data" in model_file):
+            model_file = model_file.split(".")[0]
+          model_step = int(
+              model_file.split("_")[len(model_file.split("_")) - 1])
+          selected_models[model_step] = model_file
+        file_list = sorted(selected_models.items(), key=lambda x: x[0])
+        if (len(file_list) > 0):
+          file_list = file_list[0:len(file_list) - 1]
+	print "list of models: ", file_list
+        for model_file in file_list:
+          model_file = model_file[1]
+          print "restoring: ", model_file
+          saver.restore(sess, model_dir + "/" + model_file)
+          model_step = int(
+              model_file.split("_")[len(model_file.split("_")) - 1])
+          print "evaluating on dev ", model_file, model_step
+          evaluate(sess, dev_data, batch_size, graph, model_step)
+    else:
+      ckpt = tf.train.get_checkpoint_state(model_dir)
+      print "model dir: ", model_dir
+      if (not (tf.gfile.IsDirectory(model_dir))):
+        print "create dir: ", model_dir
+        tf.gfile.MkDir(model_dir)
+      Train(graph, utility, batch_size, train_data, sess, model_dir,
+            saver)
+
+def main(args):
+  utility = Utility()
+  train_name = "random-split-1-train.examples"
+  dev_name = "random-split-1-dev.examples"
+  test_name = "pristine-unseen-tables.examples"
+  #load data
+  dat = wiki_data.WikiQuestionGenerator(train_name, dev_name, test_name, FLAGS.data_dir)
+  train_data, dev_data, test_data = dat.load()
+  utility.words = []
+  utility.word_ids = {}
+  utility.reverse_word_ids = {}
+  #construct vocabulary
+  data_utils.construct_vocab(train_data, utility)
+  data_utils.construct_vocab(dev_data, utility, True)
+  data_utils.construct_vocab(test_data, utility, True)
+  data_utils.add_special_words(utility)
+  data_utils.perform_word_cutoff(utility)
+  #convert data to int format and pad the inputs
+  train_data = data_utils.complete_wiki_processing(train_data, utility, True)
+  dev_data = data_utils.complete_wiki_processing(dev_data, utility, False)
+  test_data = data_utils.complete_wiki_processing(test_data, utility, False)
+  print "# train examples ", len(train_data)
+  print "# dev examples ", len(dev_data)
+  print "# test examples ", len(test_data)
+  print "running open source"
+  #construct TF graph and train or evaluate
+  master(train_data, dev_data, utility)
+
+
+if __name__ == "__main__":
+  tf.app.run()

nn_utils.py

+# 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.
+# ==============================================================================
+"""Author: aneelakantan (Arvind Neelakantan)
+"""
+
+import tensorflow as tf
+
+def get_embedding(word, utility, params):
+  return tf.nn.embedding_lookup(params["word"], word)
+
+
+def apply_dropout(x, dropout_rate, mode):
+  if (dropout_rate > 0.0):
+    if (mode == "train"):
+      x = tf.nn.dropout(x, dropout_rate)
+    else:
+      x = x
+  return x
+
+
+def LSTMCell(x, mprev, cprev, key, params):
+  """Create an LSTM cell.
+
+  Implements the equations in pg.2 from
+  "Long Short-Term Memory Based Recurrent Neural Network Architectures
+  For Large Vocabulary Speech Recognition",
+  Hasim Sak, Andrew Senior, Francoise Beaufays.
+
+  Args:
+    w: A dictionary of the weights and optional biases as returned
+      by LSTMParametersSplit().
+    x: Inputs to this cell.
+    mprev: m_{t-1}, the recurrent activations (same as the output)
+      from the previous cell.
+    cprev: c_{t-1}, the cell activations from the previous cell.
+    keep_prob: Keep probability on the input and the outputs of a cell.
+
+  Returns:
+    m: Outputs of this cell.
+    c: Cell Activations.
+    """
+
+  i = tf.matmul(x, params[key + "_ix"]) + tf.matmul(mprev, params[key + "_im"])
+  i = tf.nn.bias_add(i, params[key + "_i"])
+  f = tf.matmul(x, params[key + "_fx"]) + tf.matmul(mprev, params[key + "_fm"])
+  f = tf.nn.bias_add(f, params[key + "_f"])
+  c = tf.matmul(x, params[key + "_cx"]) + tf.matmul(mprev, params[key + "_cm"])
+  c = tf.nn.bias_add(c, params[key + "_c"])
+  o = tf.matmul(x, params[key + "_ox"]) + tf.matmul(mprev, params[key + "_om"])
+  o = tf.nn.bias_add(o, params[key + "_o"])
+  i = tf.sigmoid(i, name="i_gate")
+  f = tf.sigmoid(f, name="f_gate")
+  o = tf.sigmoid(o, name="o_gate")
+  c = f * cprev + i * tf.tanh(c)
+  m = o * c
+  return m, c

parameters.py

+# 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.
+# ==============================================================================
+"""Author: aneelakantan (Arvind Neelakantan)
+"""
+
+import numpy as np
+import tensorflow as tf
+
+
+class Parameters:
+
+  def __init__(self, u):
+    self.utility = u
+    self.init_seed_counter = 0
+    self.word_init = {}
+
+  def parameters(self, utility):
+    params = {}
+    inits = []
+    embedding_dims = self.utility.FLAGS.embedding_dims
+    params["unit"] = tf.Variable(
+        self.RandomUniformInit([len(utility.operations_set), embedding_dims]))
+    params["word"] = tf.Variable(
+        self.RandomUniformInit([utility.FLAGS.vocab_size, embedding_dims]))
+    params["word_match_feature_column_name"] = tf.Variable(
+        self.RandomUniformInit([1]))
+    params["controller"] = tf.Variable(
+        self.RandomUniformInit([2 * embedding_dims, embedding_dims]))
+    params["column_controller"] = tf.Variable(
+        self.RandomUniformInit([2 * embedding_dims, embedding_dims]))
+    params["column_controller_prev"] = tf.Variable(
+        self.RandomUniformInit([embedding_dims, embedding_dims]))
+    params["controller_prev"] = tf.Variable(
+        self.RandomUniformInit([embedding_dims, embedding_dims]))
+    global_step = tf.Variable(1, name="global_step")
+    #weigths of question and history RNN (or LSTM)
+    key_list = ["question_lstm"]
+    for key in key_list:
+      # Weights going from inputs to nodes.
+      for wgts in ["ix", "fx", "cx", "ox"]:
+        params[key + "_" + wgts] = tf.Variable(
+            self.RandomUniformInit([embedding_dims, embedding_dims]))
+      # Weights going from nodes to nodes.
+      for wgts in ["im", "fm", "cm", "om"]:
+        params[key + "_" + wgts] = tf.Variable(
+            self.RandomUniformInit([embedding_dims, embedding_dims]))
+      #Biases for the gates and cell
+      for bias in ["i", "f", "c", "o"]:
+        if (bias == "f"):
+          print "forget gate bias"
+          params[key + "_" + bias] = tf.Variable(
+              tf.random_uniform([embedding_dims], 1.0, 1.1, self.utility.
+                                tf_data_type[self.utility.FLAGS.data_type]))
+        else:
+          params[key + "_" + bias] = tf.Variable(
+              self.RandomUniformInit([embedding_dims]))
+    params["history_recurrent"] = tf.Variable(
+        self.RandomUniformInit([3 * embedding_dims, embedding_dims]))
+    params["history_recurrent_bias"] = tf.Variable(
+        self.RandomUniformInit([1, embedding_dims]))
+    params["break_conditional"] = tf.Variable(
+        self.RandomUniformInit([2 * embedding_dims, embedding_dims]))
+    init = tf.initialize_all_variables()
+    return params, global_step, init
+
+  def RandomUniformInit(self, shape):
+    """Returns a RandomUniform Tensor between -param_init and param_init."""
+    param_seed = self.utility.FLAGS.param_seed
+    self.init_seed_counter += 1
+    return tf.random_uniform(
+        shape, -1.0 *
+        (np.float32(self.utility.FLAGS.param_init)
+        ).astype(self.utility.np_data_type[self.utility.FLAGS.data_type]),
+        (np.float32(self.utility.FLAGS.param_init)
+        ).astype(self.utility.np_data_type[self.utility.FLAGS.data_type]),
+        self.utility.tf_data_type[self.utility.FLAGS.data_type],
+        param_seed + self.init_seed_counter)