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hyp_rnn.py
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hyp_rnn.py
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import os
import tensorflow as tf
import numpy as np
import pickle
import time
import random
from random import shuffle
import math
import argparse
from datetime import datetime
now = datetime.now()
def str2bool(answer):
answer = answer.lower()
if answer in ['y', 'yes']:
return True
if answer in ['n', 'no']:
return False
print('Invalid answer: ' + answer)
print('Exiting..')
exit()
parser = argparse.ArgumentParser()
parser.add_argument("--base_name", type=str, help="", default='')
# Path to the folder where your *_dataset/ folders live.
parser.add_argument("--root_path", type=str, help="Root path", default='/path/to/your/data/folders/')
parser.add_argument("--dataset", type=str, help="SNLI/PRFX10/PRFX30/PRFX50", default='SNLI')
# Word embeddings params
parser.add_argument("--word_dim", type=int, help="Word and hidden state embedding dimensions", default=5)
parser.add_argument("--word_init_avg_norm", type=float, help="Word init max val per dim.", default=0.001)
parser.add_argument("--inputs_geom", type=str, help="Input geometry: eucl/hyp.", default='eucl')
# RNN params
parser.add_argument("--cell_type", type=str, help="rnn/gru/TFrnn/TFgru/TFlstm", default='rnn')
parser.add_argument("--cell_non_lin", type=str, help="id/relu/tanh/sigmoid.", default='id')
parser.add_argument("--sent_geom", type=str, help="Sentence geometry eucl/hyp", default='eucl')
parser.add_argument("--bias_geom", type=str, help="RNN bias geometry: eucl/hyp.", default='eucl')
parser.add_argument("--fix_biases", type=str2bool, help="Biases are not trainable: y/n", default='n')
parser.add_argument("--fix_matrices", type=str2bool, help="y/n: If y, matrix weights are kept fixed as eye matrices", default='n')
parser.add_argument("--matrices_init_eye", type=str2bool, help="Matrix weights are initialized as eye matrices: y/n", default='n')
# FFNN and MLR params
parser.add_argument("--before_mlr_dim", type=int, help="Embedding dimension after FFNN, but before MLR.", default=5)
parser.add_argument("--ffnn_non_lin", type=str, help="id/relu/tanh/sigmoid.", default='id')
parser.add_argument("--ffnn_geom", type=str, help="FFNN geometry: eucl/hyp.", default='eucl')
parser.add_argument("--additional_features", type=str, help="Input of the final FFNN, besides sentence emb. Either empty string or dsq (distance squared, Euclidean or hyperbolic).", default='')
parser.add_argument("--dropout", type=float, help="dropout probability for FFNN layers", default=1.0)
parser.add_argument("--mlr_geom", type=str, help="MLR geometry: eucl/hyp.", default='eucl')
parser.add_argument("--proj_eps", type=float, help="", default=1e-5)
# L2 regularization:
parser.add_argument("--reg_beta", type=float, help="", default=0.0)
# Optimization params
parser.add_argument("--hyp_opt", type=str, help="Optimization technique. Only when inputs_geom is hyp. projsgd/rsgd.", default='rsgd')
parser.add_argument("--lr_ffnn", type=float, help="learning rate for the FFNN and MLR layers", default=0.01)
parser.add_argument("--lr_words", type=float, help="learning rate for words (updated rarely).", default=0.1)
parser.add_argument("--batch_size", type=int, help="", default=64)
parser.add_argument("--burnin", type=str2bool, help="y/n on whether to do burnin", default='n') ##### Seems to hurt
parser.add_argument("--c", type=float, help="c", default=1.0)
parser.add_argument("--restore_model", type=str2bool, help="y/n: restore model", default='n')
parser.add_argument("--restore_from_path", type=str, help="", default="")
args = parser.parse_args()
######################### Parse arguments ####################################
num_classes = 2
num_epochs = 30
root_path = args.root_path
PROJ_EPS = args.proj_eps
import util
util.PROJ_EPS = PROJ_EPS
import rnn_impl
dataset = args.dataset
assert dataset in ['SNLI', 'PRFX10', 'PRFX30', 'PRFX50']
c_val = args.c
cell_non_lin = args.cell_non_lin
ffnn_non_lin = args.ffnn_non_lin
cell_type = args.cell_type
word_dim = args.word_dim
hidden_dim = word_dim
word_init_avg_norm = args.word_init_avg_norm
additional_features = args.additional_features
assert additional_features in ['', 'dsq']
dropout = args.dropout
hyp_opt = args.hyp_opt
sent_geom = args.sent_geom
inputs_geom = args.inputs_geom
bias_geom = args.bias_geom
ffnn_geom = args.ffnn_geom
mlr_geom = args.mlr_geom
before_mlr_dim = args.before_mlr_dim
assert hyp_opt in ['rsgd', 'projsgd']
assert sent_geom in ['eucl', 'hyp']
assert inputs_geom in ['eucl', 'hyp']
assert bias_geom in ['eucl', 'hyp']
assert mlr_geom in ['eucl', 'hyp']
assert ffnn_geom in ['eucl', 'hyp']
if sent_geom == 'eucl':
assert inputs_geom == 'eucl'
assert bias_geom == 'eucl'
assert ffnn_geom == 'eucl'
assert mlr_geom == 'eucl'
if ffnn_geom == 'hyp':
assert sent_geom == 'hyp'
if ffnn_geom == 'eucl':
assert mlr_geom == 'eucl'
if mlr_geom == 'hyp':
assert ffnn_geom == 'hyp'
assert sent_geom == 'hyp'
fix_biases = args.fix_biases
fix_biases_str = ''
if fix_biases:
fix_biases_str = 'FIX'
fix_matrices = args.fix_matrices
matrices_init_eye = args.matrices_init_eye
mat_str = ''
if fix_matrices or matrices_init_eye:
mat_str = 'W'
if fix_matrices:
mat_str = mat_str + 'FIXeye'
elif matrices_init_eye:
mat_str = mat_str + 'eye'
burnin = args.burnin
lr_ffnn = args.lr_ffnn
lr_words = args.lr_words
batch_size = args.batch_size
reg_beta = args.reg_beta
restore_model = args.restore_model
assert restore_model == False
restore_from_path = args.restore_from_path
if inputs_geom == 'hyp' or bias_geom == 'hyp' or ffnn_geom =='hyp' or mlr_geom == 'hyp':
hyp_opt_str = hyp_opt + '_lrW' + str(lr_words) + '_lrFF' + str(lr_ffnn) + '_'
else:
hyp_opt_str = ''
if c_val != 1.0:
c_str = 'C' + str(c_val) + '_'
else:
c_str = ''
if dropout != 1.0:
drp_str = 'drp' + str(dropout) + '_'
else:
drp_str = ''
burnin_str = ''
if burnin:
burnin_str = 'burn' + str(burnin).lower()
reg_beta_str = ''
if reg_beta > 0.0:
reg_beta_str = 'reg' + str(reg_beta) + '_'
additional_features_str = additional_features
if additional_features != '':
additional_features_str = additional_features + '_'
tensorboard_name = args.base_name + '_' +\
dataset + '_' +\
'W' + str(word_dim) + 'd,' + str(word_init_avg_norm) + 'init_' + \
cell_type + '_' + \
'cellNonL' + cell_non_lin + '_' +\
'SENT' + sent_geom + '_' + \
'INP' + inputs_geom + '_' + \
'BIAS' + bias_geom + fix_biases_str + '_' + mat_str +\
'FFNN' + ffnn_geom + str(before_mlr_dim) + ffnn_non_lin + '_' +\
additional_features_str + \
drp_str +\
'MLR' + mlr_geom + '_' + \
reg_beta_str + \
hyp_opt_str + \
c_str +\
'prje' + str(PROJ_EPS) + '_' + \
'bs' + str(batch_size) + '_' +\
burnin_str + '__' + now.strftime("%H:%M:%S,%dM")
name_experiment = tensorboard_name
logger = util.setup_logger(name_experiment, logs_dir= os.path.join(root_path, 'logs/'), also_stdout=True)
logger.info('PARAMS : ' + name_experiment)
logger.info('')
logger.info(args)
if dataset.startswith('PRFX'):
if dataset == 'PRFX10':
suffix = 'prefix_10_dataset'
if dataset == 'PRFX30':
suffix = 'prefix_30_dataset'
if dataset == 'PRFX50':
suffix = 'prefix_50_dataset'
word_to_id_file_path = os.path.join(root_path, suffix, 'word_to_id')
id_to_word_file_path = os.path.join(root_path, suffix, 'id_to_word')
training_data_file_path = os.path.join(root_path, suffix, 'train')
test_data_file_path = os.path.join(root_path, suffix, 'test')
dev_data_file_path = os.path.join(root_path, suffix, 'dev')
elif dataset == 'SNLI':
word_to_id_file_path = os.path.join(root_path, 'snli_dataset/', 'word_to_id')
id_to_word_file_path = os.path.join(root_path, 'snli_dataset/', 'id_to_word')
suffix = '_' + str(num_classes) + 'class'
training_data_file_path = os.path.join(root_path, 'snli_dataset/', 'train' + suffix)
test_data_file_path = os.path.join(root_path, 'snli_dataset/', 'test' + suffix)
dev_data_file_path = os.path.join(root_path, 'snli_dataset/', 'dev' + suffix)
dtype = tf.float64
class HyperbolicRNNModel:
def __init__(self, word_to_id, id_to_word):
self.word_to_id = word_to_id
self.id_to_word = id_to_word
self.construct_placeholders()
self.construct_execution_graph()
def construct_placeholders(self):
self.label_placeholder = tf.placeholder(tf.int32,
shape=[batch_size],
name='label_placeholder')
self.word_ids_1 = tf.placeholder(tf.int32, shape=[batch_size, None],
name='word_ids_1_placeholder')
self.word_ids_2 = tf.placeholder(tf.int32, shape=[batch_size, None],
name='word_ids_2_placeholder')
self.num_words_1 = tf.placeholder(tf.int32, shape=[batch_size],
name='num_words_1_placeholder')
self.num_words_2 = tf.placeholder(tf.int32, shape=[batch_size],
name='num_words_2_placeholder')
self.burn_in_factor = tf.placeholder(dtype, name='burn_in_factor_placeholder')
self.dropout_placeholder = tf.placeholder(dtype, name='dropout_placeholder')
###############################################################################################
def construct_execution_graph(self):
# Collect vars separately. Word embeddings are not used here.
eucl_vars = []
hyp_vars = []
################## word embeddings ###################
# Initialize word embeddings close to 0, to have average norm equal to word_init_avg_norm.
maxval = (3. * (word_init_avg_norm ** 2) / (2. * word_dim)) ** (1. / 3)
initializer = tf.random_uniform_initializer(minval=-maxval, maxval=maxval, dtype=dtype)
self.embeddings = tf.get_variable('embeddings',
dtype=dtype,
shape=[len(self.word_to_id), word_dim],
initializer=initializer)
if inputs_geom == 'eucl':
eucl_vars += [self.embeddings]
################## RNNs for sentence embeddings ###################
if cell_type == 'TFrnn':
assert sent_geom == 'eucl'
cell_class = lambda h_dim: tf.contrib.rnn.BasicRNNCell(h_dim)
elif cell_type == 'TFgru':
assert sent_geom == 'eucl'
cell_class = lambda h_dim: tf.contrib.rnn.GRUCell(h_dim)
elif cell_type == 'TFlstm':
assert sent_geom == 'eucl'
cell_class = lambda h_dim: tf.contrib.rnn.BasicLSTMCell(h_dim)
elif cell_type == 'rnn' and sent_geom == 'eucl':
cell_class = lambda h_dim: rnn_impl.EuclRNN(h_dim, dtype=dtype)
elif cell_type == 'gru' and sent_geom == 'eucl':
cell_class = lambda h_dim: rnn_impl.EuclGRU(h_dim, dtype=dtype)
elif cell_type == 'rnn' and sent_geom == 'hyp':
cell_class = lambda h_dim: rnn_impl.HypRNN(num_units=h_dim,
inputs_geom=inputs_geom,
bias_geom=bias_geom,
c_val=c_val,
non_lin=cell_non_lin,
fix_biases=fix_biases,
fix_matrices=fix_matrices,
matrices_init_eye=matrices_init_eye,
dtype=dtype)
elif cell_type == 'gru' and sent_geom == 'hyp':
cell_class = lambda h_dim: rnn_impl.HypGRU(num_units=h_dim,
inputs_geom=inputs_geom,
bias_geom=bias_geom,
c_val=c_val,
non_lin=cell_non_lin,
fix_biases=fix_biases,
fix_matrices=fix_matrices,
matrices_init_eye=matrices_init_eye,
dtype=dtype)
else:
logger.error('Not valid cell type: %s and sent_geom %s' % (cell_type, sent_geom))
exit()
# RNN 1
with tf.variable_scope(cell_type + '1'):
word_embeddings_1 = tf.nn.embedding_lookup(self.embeddings, self.word_ids_1) # bs x num_w_s1 x dim
cell_1 = cell_class(hidden_dim)
initial_state_1 = cell_1.zero_state(batch_size, dtype)
outputs_1, state_1 = tf.nn.dynamic_rnn(cell=cell_1,
inputs=word_embeddings_1,
dtype=dtype,
initial_state=initial_state_1,
sequence_length=self.num_words_1)
if cell_type == 'TFlstm':
self.sent_1 = state_1[1]
else:
self.sent_1 = state_1
sent1_norm = util.tf_norm(self.sent_1)
# RNN 2
with tf.variable_scope(cell_type + '2'):
word_embeddings_2 = tf.nn.embedding_lookup(self.embeddings, self.word_ids_2)
# tf.summary.scalar('word_emb2', tf.reduce_mean(tf.norm(word_embeddings_2, axis=2)))
cell_2 = cell_class(hidden_dim)
initial_state_2 = cell_2.zero_state(batch_size, dtype)
outputs_2, state_2 = tf.nn.dynamic_rnn(cell=cell_2,
inputs=word_embeddings_2,
dtype=dtype,
initial_state=initial_state_2,
sequence_length=self.num_words_2)
if cell_type == 'TFlstm':
self.sent_2 = state_2[1]
else:
self.sent_2 = state_2
sent2_norm = util.tf_norm(self.sent_2)
tf.summary.scalar('RNN/word_emb1', tf.reduce_mean(tf.norm(word_embeddings_1, axis=2)))
tf.summary.scalar('RNN/sent1', tf.reduce_mean(sent1_norm))
tf.summary.scalar('RNN/sent2', tf.reduce_mean(sent2_norm))
eucl_vars += cell_1.eucl_vars + cell_2.eucl_vars
if sent_geom == 'hyp':
hyp_vars += cell_1.hyp_vars + cell_2.hyp_vars
## Compute d(s1, s2)
if sent_geom == 'eucl':
d_sq_s1_s2 = util.tf_euclid_dist_sq(self.sent_1, self.sent_2)
else:
d_sq_s1_s2 = util.tf_poinc_dist_sq(self.sent_1, self.sent_2, c = c_val)
##### Some summaries:
# For summaries and debugging, we need these:
pos_labels = tf.reshape(tf.cast(self.label_placeholder, tf.float64), [-1, 1])
neg_labels = 1. - pos_labels
weights_pos_labels = pos_labels / tf.reduce_sum(pos_labels)
weights_neg_labels = neg_labels / tf.reduce_sum(neg_labels)
################## first feed forward layer ###################
# Define variables for the first feed-forward layer: W1 * s1 + W2 * s2 + b + bd * d(s1,s2)
W_ff_s1 = tf.get_variable('W_ff_s1',
dtype=dtype,
shape=[hidden_dim, before_mlr_dim],
initializer= tf.contrib.layers.xavier_initializer())
W_ff_s2 = tf.get_variable('W_ff_s2',
dtype=dtype,
shape=[hidden_dim, before_mlr_dim],
initializer= tf.contrib.layers.xavier_initializer())
b_ff = tf.get_variable('b_ff',
dtype=dtype,
shape=[1, before_mlr_dim],
initializer=tf.constant_initializer(0.0))
b_ff_d = tf.get_variable('b_ff_d',
dtype=dtype,
shape=[1, before_mlr_dim],
initializer=tf.constant_initializer(0.0))
eucl_vars += [W_ff_s1, W_ff_s2]
if ffnn_geom == 'eucl' or bias_geom == 'eucl':
eucl_vars += [b_ff]
if additional_features == 'dsq':
eucl_vars += [b_ff_d]
else:
hyp_vars += [b_ff]
if additional_features == 'dsq':
hyp_vars += [b_ff_d]
if ffnn_geom == 'eucl' and sent_geom == 'hyp': # Sentence embeddings are Euclidean after log, except the proper distance (Eucl or hyp) is kept!
self.sent_1 = util.tf_log_map_zero(self.sent_1, c_val)
self.sent_2 = util.tf_log_map_zero(self.sent_2, c_val)
####### Build output_ffnn #######
if ffnn_geom == 'eucl':
output_ffnn = tf.matmul(self.sent_1, W_ff_s1) + tf.matmul(self.sent_2, W_ff_s2) + b_ff
if additional_features == 'dsq': # [u, v, d(u,v)^2]
output_ffnn = output_ffnn + d_sq_s1_s2 * b_ff_d
else:
assert sent_geom == 'hyp'
ffnn_s1 = util.tf_mob_mat_mul(W_ff_s1, self.sent_1, c_val)
ffnn_s2 = util.tf_mob_mat_mul(W_ff_s2, self.sent_2, c_val)
output_ffnn = util.tf_mob_add(ffnn_s1, ffnn_s2, c_val)
hyp_b_ff = b_ff
if bias_geom == 'eucl':
hyp_b_ff = util.tf_exp_map_zero(b_ff, c_val)
output_ffnn = util.tf_mob_add(output_ffnn, hyp_b_ff, c_val)
if additional_features == 'dsq': # [u, v, d(u,v)^2]
hyp_b_ff_d = b_ff_d
if bias_geom == 'eucl':
hyp_b_ff_d = util.tf_exp_map_zero(b_ff_d, c_val)
output_ffnn = util.tf_mob_add(output_ffnn,
util.tf_mob_scalar_mul(d_sq_s1_s2, hyp_b_ff_d, c_val),
c_val)
if ffnn_geom == 'eucl':
output_ffnn = util.tf_eucl_non_lin(output_ffnn, non_lin=ffnn_non_lin)
else:
output_ffnn = util.tf_hyp_non_lin(output_ffnn,
non_lin=ffnn_non_lin,
hyp_output = (mlr_geom == 'hyp' and dropout == 1.0),
c=c_val)
# Mobius dropout
if dropout < 1.0:
# If we are here, then output_ffnn should be Euclidean.
output_ffnn = tf.nn.dropout(output_ffnn, keep_prob=self.dropout_placeholder)
if (mlr_geom == 'hyp'):
output_ffnn = util.tf_exp_map_zero(output_ffnn, c_val)
################## MLR ###################
# output_ffnn is batch_size x before_mlr_dim
A_mlr = []
P_mlr = []
logits_list = []
for cl in range(num_classes):
A_mlr.append(tf.get_variable('A_mlr' + str(cl),
dtype=dtype,
shape=[1, before_mlr_dim],
initializer=tf.contrib.layers.xavier_initializer()))
eucl_vars += [A_mlr[cl]]
P_mlr.append(tf.get_variable('P_mlr' + str(cl),
dtype=dtype,
shape=[1, before_mlr_dim],
initializer=tf.constant_initializer(0.0)))
if mlr_geom == 'eucl':
eucl_vars += [P_mlr[cl]]
logits_list.append(tf.reshape(util.tf_dot(-P_mlr[cl] + output_ffnn, A_mlr[cl]), [-1]))
elif mlr_geom == 'hyp':
hyp_vars += [P_mlr[cl]]
minus_p_plus_x = util.tf_mob_add(-P_mlr[cl], output_ffnn, c_val)
norm_a = util.tf_norm(A_mlr[cl])
lambda_px = util.tf_lambda_x(minus_p_plus_x, c_val)
px_dot_a = util.tf_dot(minus_p_plus_x, tf.nn.l2_normalize(A_mlr[cl]))
logit = 2. / np.sqrt(c_val) * norm_a * tf.asinh(np.sqrt(c_val) * px_dot_a * lambda_px)
logits_list.append(tf.reshape(logit, [-1]))
self.logits = tf.stack(logits_list, axis=1)
self.argmax_idx = tf.argmax(self.logits, axis=1, output_type=tf.int32)
self.loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.label_placeholder,
logits=self.logits))
tf.summary.scalar('classif/unreg_loss', self.loss)
if reg_beta > 0.0:
assert num_classes == 2
distance_regularizer = tf.reduce_mean(
(tf.cast(self.label_placeholder, dtype=dtype) - 0.5) * d_sq_s1_s2)
self.loss = self.loss + reg_beta * distance_regularizer
self.acc = tf.reduce_mean(tf.to_float(tf.equal(self.argmax_idx, self.label_placeholder)))
tf.summary.scalar('classif/accuracy', self.acc)
######################################## OPTIMIZATION ######################################
all_updates_ops = []
###### Update Euclidean parameters using Adam.
optimizer_euclidean_params = tf.train.AdamOptimizer(learning_rate=1e-3)
eucl_grads = optimizer_euclidean_params.compute_gradients(self.loss, eucl_vars)
capped_eucl_gvs = [(tf.clip_by_norm(grad, 1.), var) for grad, var in eucl_grads] ###### Clip gradients
all_updates_ops.append(optimizer_euclidean_params.apply_gradients(capped_eucl_gvs))
###### Update Hyperbolic parameters, i.e. word embeddings and some biases in our case.
def rsgd(v, riemannian_g, learning_rate):
if hyp_opt == 'rsgd':
return util.tf_exp_map_x(v, -self.burn_in_factor * learning_rate * riemannian_g, c=c_val)
else:
# Use approximate RSGD based on a simple retraction.
updated_v = v - self.burn_in_factor * learning_rate * riemannian_g
# Projection op after SGD update. Need to make sure embeddings are inside the unit ball.
return util.tf_project_hyp_vecs(updated_v, c_val)
if inputs_geom == 'hyp':
grads_and_indices_hyp_words = tf.gradients(self.loss, self.embeddings)
grads_hyp_words = grads_and_indices_hyp_words[0].values
repeating_indices = grads_and_indices_hyp_words[0].indices
unique_indices, idx_in_repeating_indices = tf.unique(repeating_indices)
agg_gradients = tf.unsorted_segment_sum(grads_hyp_words,
idx_in_repeating_indices,
tf.shape(unique_indices)[0])
agg_gradients = tf.clip_by_norm(agg_gradients, 1.) ######## Clip gradients
unique_word_emb = tf.nn.embedding_lookup(self.embeddings, unique_indices) # no repetitions here
riemannian_rescaling_factor = util.riemannian_gradient_c(unique_word_emb, c=c_val)
rescaled_gradient = riemannian_rescaling_factor * agg_gradients
all_updates_ops.append(tf.scatter_update(self.embeddings,
unique_indices,
rsgd(unique_word_emb, rescaled_gradient, lr_words))) # Updated rarely
if len(hyp_vars) > 0:
hyp_grads = tf.gradients(self.loss, hyp_vars)
capped_hyp_grads = [tf.clip_by_norm(grad, 1.) for grad in hyp_grads] ###### Clip gradients
for i in range(len(hyp_vars)):
riemannian_rescaling_factor = util.riemannian_gradient_c(hyp_vars[i], c=c_val)
rescaled_gradient = riemannian_rescaling_factor * capped_hyp_grads[i]
all_updates_ops.append(tf.assign(hyp_vars[i], rsgd(hyp_vars[i], rescaled_gradient, lr_ffnn))) # Updated frequently
self.all_optimizer_var_updates_op = tf.group(*all_updates_ops)
self.summary_merged = tf.summary.merge_all()
self.test_summary_writer = tf.summary.FileWriter(
os.path.join(root_path, 'tb_28may/' + tensorboard_name + '/'))
###############################################################################################
###############################################################################################
###############################################################################################
def test(self, sess, test_or_valid_data, name, summary_i):
N = len(test_or_valid_data)
i = 0
predictions = []
avg_loss = 0.0
num_b_in_loss = 0
while i < N:
batch_word_ids_1, batch_num_words_1, batch_word_ids_2, batch_num_words_2, batch_label = self.next_batch(
i=i, N=N, data=test_or_valid_data)
if name == 'test':
summary, loss, argmax_idx = \
sess.run([self.summary_merged, self.loss, self.argmax_idx], feed_dict={
self.word_ids_1: batch_word_ids_1,
self.num_words_1: batch_num_words_1,
self.word_ids_2: batch_word_ids_2,
self.num_words_2: batch_num_words_2,
self.label_placeholder: batch_label,
self.dropout_placeholder: 1.0
})
self.test_summary_writer.add_summary(summary, summary_i)
else:
loss, argmax_idx = \
sess.run([self.loss, self.argmax_idx], feed_dict={
self.word_ids_1: batch_word_ids_1,
self.num_words_1: batch_num_words_1,
self.word_ids_2: batch_word_ids_2,
self.num_words_2: batch_num_words_2,
self.label_placeholder: batch_label,
self.dropout_placeholder: 1.0
})
avg_loss += loss
num_b_in_loss += 1
for ci in argmax_idx:
predictions.append(ci)
i += batch_size
avg_loss /= num_b_in_loss
num_correct = 0.0
glE_predE = 0.0
glN_predN = 0.0
glE_predN = 0.0
glN_predE = 0.0
predictions = predictions[:N]
for ind, predicted_label in enumerate(predictions):
gold_label = test_or_valid_data[ind][4]
if predicted_label == gold_label:
num_correct += 1.0
if predicted_label == 1:
glE_predE += 1.0
else:
glN_predN += 1.0
else:
if predicted_label == 1:
glN_predE += 1.0
else:
glE_predN += 1.0
accuracy = num_correct / (1.0 * N)
if name == 'test':
logger.info('For ' + name + ': ==> ' +
' glN_predN = ' + str(glN_predN) + '; glE_predN = ' + str(glE_predN) +
'; glN_predE = ' + str(glN_predE) + '; glE_predE = ' + str(glE_predE))
return accuracy
def next_batch(self, i, N, data):
it = i
to = min(i + batch_size, N)
MAX_PAD = 0
batch_word_ids_1 = []
batch_num_words_1 = []
batch_word_ids_2 = []
batch_num_words_2 = []
batch_label = []
while it < to:
word_ids_1 = data[it][0]
num_words_1 = data[it][1]
word_ids_2 = data[it][2]
num_words_2 = data[it][3]
label = data[it][4]
MAX_PAD = max(MAX_PAD, max(len(word_ids_1), len(word_ids_2)))
batch_word_ids_1.append(word_ids_1)
batch_num_words_1.append(num_words_1)
batch_word_ids_2.append(word_ids_2)
batch_num_words_2.append(num_words_2)
batch_label.append(label)
it += 1
if it == to and to == N:
it = 0
to = batch_size - len(batch_word_ids_1)
for ind in range(0, batch_size):
while len(batch_word_ids_1[ind]) < MAX_PAD:
batch_word_ids_1[ind].append(0)
while len(batch_word_ids_2[ind]) < MAX_PAD:
batch_word_ids_2[ind].append(0)
if len(batch_word_ids_1[ind]) != MAX_PAD or len(batch_word_ids_2[ind]) != MAX_PAD:
logger.error('THIS IS WRONG!: len1: %d\nlen2: %d\nMAX_PAD:%d\n' %
(len(batch_word_ids_1[ind]), len(batch_word_ids_2[ind]), MAX_PAD))
exit()
batch_word_ids_1 = np.array(batch_word_ids_1)
batch_num_words_1 = np.array(batch_num_words_1)
batch_word_ids_2 = np.array(batch_word_ids_2)
batch_num_words_2 = np.array(batch_num_words_2)
batch_label = np.array(batch_label)
return batch_word_ids_1, batch_num_words_1, batch_word_ids_2, batch_num_words_2, batch_label
def dataset_to_minibatches(self, dataset):
N = len(dataset)
i = 0
batches_list = []
while i < N:
batches_list.append(self.next_batch(i=i, N=N, data=dataset))
i += batch_size
return batches_list
def train(self, training_data, dev_data, test_data, restore_model=False, save_model=False,
restore_from_path=None, save_to_path=None):
training_data_batches = self.dataset_to_minibatches(training_data)
num_batches = len(training_data_batches)
N = len(training_data)
saver = tf.train.Saver()
best_test_accuracy = 0.0
best_validation_accuracy = 0.0
best_i = 0
PRINT_STEP = 2000
config = tf.ConfigProto(
intra_op_parallelism_threads=5,
inter_op_parallelism_threads=3
)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
if restore_model:
saver.restore(sess, restore_from_path)
else:
sess.run(tf.global_variables_initializer())
sess.graph.finalize()
burn_in_factor = 1.0
epoch = -1
while epoch < num_epochs:
epoch += 1
logger.info('Epoch: %d' % epoch)
cur_total_time = 0
shuffle(training_data_batches) # Shuffle training data after each epoch.
if burnin and epoch == 0:
burn_in_factor /= 10.0
i = -1
while i < num_batches - 1:
i += 1
# Training
batch_word_ids_1, batch_num_words_1, \
batch_word_ids_2, batch_num_words_2, \
batch_label = training_data_batches[i]
feed_dict = {
self.word_ids_1: batch_word_ids_1,
self.num_words_1: batch_num_words_1,
self.word_ids_2: batch_word_ids_2,
self.num_words_2: batch_num_words_2,
self.label_placeholder: batch_label,
self.burn_in_factor: burn_in_factor,
self.dropout_placeholder: dropout
}
sess_time_start = time.time()
curr_loss, _ = sess.run([self.loss, self.all_optimizer_var_updates_op], feed_dict=feed_dict)
cur_total_time += time.time() - sess_time_start
if i % PRINT_STEP == 0:
if i > 0:
avg_sec_per_sent = cur_total_time / (PRINT_STEP * batch_size)
logger.info('Num examples processed: %d. curr_loss: %.4f; sec_per_sent: %.4f' % (
epoch * N + i * batch_size, curr_loss, avg_sec_per_sent))
cur_total_time = 0
# Testing
validation_accuracy = self.test(sess,
dev_data,
'validation',
epoch * N + i * batch_size)
test_accuracy = self.test(sess,
test_data,
'test',
epoch * N + i * batch_size)
logger.info('CURRENT val accuracy: %.4f ; test accuracy: \033[92m %.4f \033[0m' %
(validation_accuracy, test_accuracy))
if validation_accuracy > best_validation_accuracy:
best_validation_accuracy = validation_accuracy
best_test_accuracy = test_accuracy
best_i = epoch * N + i * batch_size
logger.info(('BEST: i = %d, val acc: ' + '\033[94m' + ' %.2f' + '\033[0m' +
', test acc: ' + '\033[91m' + ' %.2f' + '\033[0m') %
(best_i, 100 * best_validation_accuracy, 100 * best_test_accuracy))
if save_model:
store_time_begin = time.time()
saver.save(sess, '%s_epoch_%d_it_%d.ckpt' % (save_to_path, epoch, i))
logger.info('Stored the model in %d seconds.' %
(time.time() - store_time_begin))
logger.info('EXPERIMENT = ' + name_experiment)
logger.info('=============================================================')
if np.isinf(curr_loss) or np.isnan(curr_loss):
logger.error('At example ' + str(epoch * N + i * batch_size) +
'; curr_loss: ' + str(curr_loss))
exit()
logger.info(('DONE -- BEST: i = %d, val acc: ' + '\033[94m' + ' %.2f' + '\033[0m' +
', test acc: ' + '\033[91m' + ' %.2f' + '\033[0m') %
(best_i, 100 * best_validation_accuracy, 100 * best_test_accuracy))
def get_datasets():
logger.info('Loading train - val - test data')
test_data = pickle.load(open(test_data_file_path, 'rb'))
dev_data = pickle.load(open(dev_data_file_path, 'rb'))
training_data = pickle.load(open(training_data_file_path, 'rb'))
logger.info('Training data size: %d' % len(training_data))
class_to_count = {1: 0.0, 0: 0.0}
for i in range(len(training_data)):
class_to_count[training_data[i][4]] += 1.0
for cl in class_to_count:
logger.info('Class %d has %.4f percent samples' % (cl, 100. * class_to_count[cl] / len(training_data)))
logger.info('Validation data size: %d' % len(dev_data))
class_to_count = {1: 0.0, 0: 0.0}
for i in range(len(test_data)):
class_to_count[test_data[i][4]] += 1.0
for cl in class_to_count:
logger.info('Class %d has %.4f percent samples' % (cl, 100. * class_to_count[cl] / len(dev_data)))
logger.info('Test data size: %d' % len(test_data))
class_to_count = {1: 0.0, 0: 0.0}
for i in range(len(test_data)):
class_to_count[test_data[i][4]] += 1.0
for cl in class_to_count:
logger.info('Class %d has %.4f percent samples' % (cl, 100. * class_to_count[cl] / len(test_data)))
return training_data, dev_data, test_data
def run():
word_to_id = pickle.load(open(word_to_id_file_path, 'rb'))
id_to_word = pickle.load(open(id_to_word_file_path, 'rb'))
model = HyperbolicRNNModel(word_to_id=word_to_id,
id_to_word=id_to_word)
training_data, dev_data, test_data = get_datasets()
model.train(training_data=training_data,
dev_data=dev_data,
test_data=test_data,
save_model=False,
save_to_path='models/' + name_experiment,
restore_model=restore_model,
restore_from_path=restore_from_path)
if __name__ == '__main__':
run()