feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
step, summaries, loss, accuracy = sess.run(
[global_step, dev_summary_op, cnn.loss, cnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
if writer:
writer.add_summary(summaries, step)
# Generate batches
batches = corpus_handel.batch_iter(list(zip(x_train, y_train)),
FLAGS.batch_size, FLAGS.num_epochs)
# Training loop. For each batch...
for batch in batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
dev_step(x_dev, y_dev, writer=dev_summary_writer)
print("")
if current_step % FLAGS.checkpoint_every == 0:
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_x = graph.get_operation_by_name("input_x").outputs[0]
# input_y = graph.get_operation_by_name("input_y").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
# Generate batches for one epoch
batches = corpus_handel.batch_iter(x_test, 30, 1, shuffle=False)
# Collect the predictions here
all_predictions = []
for x_test_batch in batches:
batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
print batch_predictions
all_predictions = np.concatenate([all_predictions, batch_predictions])
# print all_predictions
if(all_predictions[0] == 0):
print 'Who'
elif(all_predictions[0] == 1):
print 'When'
def train(online, sentence, label):
# Data loading params
tf.flags.DEFINE_float(
"dev_sample_percentage", .1,
"Percentage of the training data to use for validation")
tf.flags.DEFINE_string("positive_data_file",
"./data/rt-polaritydata/rt-polarity.pos",
"Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file",
"./data/rt-polaritydata/rt-polarity.neg",
"Data source for the negative data.")
# Model Hyperparameters
tf.flags.DEFINE_integer(
"embedding_dim", 128,
"Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5",
"Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer(
"num_filters", 128, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5,
"Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0,
"L2 regularization lambda (default: 0.0)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", epochs,
"Number of training epochs (default: 200)")
tf.flags.DEFINE_integer(
"evaluate_every", 100,
"Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", ckpts,
"Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 5,
"Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True,
"Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False,
"Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
#FLAGS._parse_flags()
#print("\nParameters:")
#for attr, value in sorted(FLAGS.__flags.items()):
# print("{}={}".format(attr.upper(), value))
#print("")
load_model = False
# Data Preparation
# ==================================================
# Load data
#print("Loading data...")
x, y, vocabulary, vocabulary_inv = corpus_handel.load_data(trainingDir)
if online:
x, y, vocabulary, vocabulary_inv = corpus_handel.load_data_online(
trainingDir, sentence, int(label))
with open("export/" + trainingDir + "/dictionary.json",
"w",
encoding="utf8") as outfile:
json.dump(vocabulary, outfile)
x_shuffled = np.array([x[0]])
y_shuffled = np.array([y[0]])
if not online:
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
x_train, x_dev = x_shuffled[:-10], x_shuffled[-10:]
y_train, y_dev = y_shuffled[:-10], y_shuffled[-10:]
if online:
x_train, x_dev = x_shuffled, x_shuffled
y_train, y_dev = y_shuffled, y_shuffled
del x, y, x_shuffled, y_shuffled
sentences, dump = corpus_handel.load_data_and_labels(trainingDir)
sequence_length = max(len(x) for x in sentences)
shape = 70 #
#print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
#print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))
# Training
# ==================================================
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=len(vocabulary),
embedding_size=FLAGS.embedding_dim,
filter_sizes=list(
map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
l2_reg_lambda=FLAGS.l2_reg_lambda)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram(
"{}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar(
"{}/grad/sparsity".format(v.name),
tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
# Output directory for models and summaries
#timestamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs", trainingDir))
#print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", cnn.loss)
acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)
# Train Summaries
train_summary_op = tf.summary.merge(
[loss_summary, acc_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(
train_summary_dir, sess.graph)
# Dev summaries
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir,
sess.graph)
if os.path.exists("runs/" + trainingDir + "/checkpoints"):
load_model = True
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(
os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# Write vocabulary
#vocab_processor.save(os.path.join(out_dir, "vocab"))
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
saver = tf.train.Saver() #
if load_model:
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_dir))
# Initialize all variables
if not load_model:
sess.run(init_op)
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
}
if online:
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: FLAGS.dropout_keep_prob,
tf.placeholder(tf.float32, name="learning_rate"): 0.1
}
_, step, summaries, loss, accuracy = sess.run([
train_op, global_step, train_summary_op, cnn.loss,
cnn.accuracy
], feed_dict)
time_str = datetime.datetime.now().isoformat()
if step % 10 == 0:
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
train_summary_writer.add_summary(summaries, step)
def dev_step(x_batch, y_batch, writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
step, summaries, loss, accuracy = sess.run(
[global_step, dev_summary_op, cnn.loss, cnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
if writer:
writer.add_summary(summaries, step)
# Generate batches
batches = corpus_handel.batch_iter(list(zip(x_train, y_train)),
FLAGS.batch_size,
FLAGS.num_epochs)
if online:
train_step(x_train, y_train)
current_step = tf.train.global_step(sess, global_step)
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
export_path_base = "export"
try:
shutil.rmtree(export_path_base)
except OSError:
pass
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(FLAGS.model_version)))
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING])
builder.save(True)
else:
# Training loop. For each batch...
for idx, batch in enumerate(batches):
if idx < steps * epochs:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
dev_step(x_dev, y_dev, writer=dev_summary_writer)
print("")
if current_step % FLAGS.checkpoint_every == 0:
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print(
"Saved model checkpoint to {}\n".format(path))
export_path_base = "export"
try:
shutil.rmtree(export_path_base)
except OSError:
pass
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(trainingDir))
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(
export_path)
# Build the signature_def_map.
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING])
builder.save(True)