def train_cnn():
"""Training CNN model."""
# Load sentences, labels, and training parameters
logger.info('✔︎ Loading data...')
logger.info('✔︎ Training data processing...')
train_data = data_helpers.load_data_and_labels(FLAGS.training_data_file, FLAGS.num_classes, FLAGS.embedding_dim)
logger.info('✔︎ Validation data processing...')
validation_data = \
data_helpers.load_data_and_labels(FLAGS.validation_data_file, FLAGS.num_classes, FLAGS.embedding_dim)
logger.info('Recommand padding Sequence length is: {}'.format(FLAGS.pad_seq_len))
logger.info('✔︎ Training data padding...')
x_train, y_train = data_helpers.pad_data(train_data, FLAGS.pad_seq_len)
logger.info('✔︎ Validation data padding...')
x_validation, y_validation = data_helpers.pad_data(validation_data, FLAGS.pad_seq_len)
y_validation_bind = validation_data.labels_bind
# Build vocabulary
VOCAB_SIZE = data_helpers.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = data_helpers.load_word2vec_matrix(VOCAB_SIZE, FLAGS.embedding_dim)
# Build a graph and cnn object
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
session_conf.gpu_options.allow_growth = FLAGS.gpu_options_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(
sequence_length=FLAGS.pad_seq_len,
num_classes=FLAGS.num_classes,
vocab_size=VOCAB_SIZE,
fc_hidden_size=FLAGS.fc_hidden_size,
embedding_size=FLAGS.embedding_dim,
embedding_type=FLAGS.embedding_type,
filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
l2_reg_lambda=FLAGS.l2_reg_lambda,
pretrained_embedding=pretrained_word2vec_matrix)
# Define Training procedure
# learning_rate = tf.train.exponential_decay(learning_rate=FLAGS.learning_rate, global_step=cnn.global_step,
# decay_steps=FLAGS.decay_steps, decay_rate=FLAGS.decay_rate,
# staircase=True)
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=cnn.global_step, name="train_op")
# 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
if FLAGS.train_or_restore == 'R':
MODEL = input("☛ Please input the checkpoints model you want to restore, "
"it should be like(1490175368): ") # The model you want to restore
while not (MODEL.isdigit() and len(MODEL) == 10):
MODEL = input('✘ The format of your input is illegal, please re-input: ')
logger.info('✔︎ The format of your input is legal, now loading to next step...')
checkpoint_dir = 'runs/' + MODEL + '/checkpoints/'
out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", MODEL))
logger.info("✔︎ Writing to {}\n".format(out_dir))
else:
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
logger.info("✔︎ 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, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
# Validation summaries
validation_summary_op = tf.summary.merge([loss_summary])
validation_summary_dir = os.path.join(out_dir, "summaries", "validation")
validation_summary_writer = tf.summary.FileWriter(validation_summary_dir, sess.graph)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)
if FLAGS.train_or_restore == 'R':
# Load cnn model
logger.info("✔ Loading model...")
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
logger.info(checkpoint_file)
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
else:
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
current_step = sess.run(cnn.global_step)
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,
cnn.is_training: True
}
_, step, summaries, loss = sess.run(
[train_op, cnn.global_step, train_summary_op, cnn.loss], feed_dict)
time_str = datetime.datetime.now().isoformat()
logger.info("{}: step {}, loss {:g}"
.format(time_str, step, loss))
train_summary_writer.add_summary(summaries, step)
def validation_step(x_validation, y_validation, y_validation_bind, writer=None):
"""Evaluates model on a validation set"""
batches_validation = data_helpers.batch_iter(
list(zip(x_validation, y_validation, y_validation_bind)), 8 * FLAGS.batch_size, FLAGS.num_epochs)
eval_loss, eval_rec, eval_acc, eval_counter = 0.0, 0.0, 0.0, 0
for batch_validation in batches_validation:
x_batch_validation, y_batch_validation, y_batch_validation_bind = zip(*batch_validation)
feed_dict = {
cnn.input_x: x_batch_validation,
cnn.input_y: y_batch_validation,
cnn.dropout_keep_prob: 1.0,
cnn.is_training: False
}
step, summaries, logits, cur_loss = sess.run(
[cnn.global_step, validation_summary_op, cnn.logits, cnn.loss], feed_dict)
if FLAGS.use_classbind_or_not == 'Y':
predicted_labels = data_helpers.get_label_using_logits_and_classbind(
logits, y_batch_validation_bind, top_number=FLAGS.top_num)
if FLAGS.use_classbind_or_not == 'N':
predicted_labels = data_helpers.get_label_using_logits(logits, top_number=FLAGS.top_num)
cur_rec, cur_acc = 0.0, 0.0
for index, predicted_label in enumerate(predicted_labels):
rec_inc, acc_inc = data_helpers.cal_rec_and_acc(predicted_label, y_batch_validation[index])
cur_rec, cur_acc = cur_rec + rec_inc, cur_acc + acc_inc
cur_rec = cur_rec / len(y_batch_validation)
cur_acc = cur_acc / len(y_batch_validation)
eval_loss, eval_rec, eval_acc, eval_counter = eval_loss + cur_loss, eval_rec + cur_rec, \
eval_acc + cur_acc, eval_counter + 1
logger.info("✔︎ validation batch {} finished.".format(eval_counter))
if writer:
writer.add_summary(summaries, step)
eval_loss = float(eval_loss / eval_counter)
eval_rec = float(eval_rec / eval_counter)
eval_acc = float(eval_acc / eval_counter)
return eval_loss, eval_rec, eval_acc
# Generate batches
batches_train = data_helpers.batch_iter(
list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
# Training loop. For each batch...
for batch_train in batches_train:
x_batch_train, y_batch_train = zip(*batch_train)
train_step(x_batch_train, y_batch_train)
current_step = tf.train.global_step(sess, cnn.global_step)
if current_step % FLAGS.evaluate_every == 0:
logger.info("\nEvaluation:")
eval_loss, eval_rec, eval_acc = validation_step(x_validation, y_validation, y_validation_bind,
writer=validation_summary_writer)
time_str = datetime.datetime.now().isoformat()
logger.info("{}: step {}, loss {:g}, rec {:g}, acc {:g}"
.format(time_str, current_step, eval_loss, eval_rec, eval_acc))
if current_step % FLAGS.checkpoint_every == 0:
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
logger.info("✔︎ Saved model checkpoint to {}\n".format(path))
logger.info("✔︎ Done.")
def test_fasttext():
"""Test FASTTEXT model."""
# Load data
logger.info("✔ Loading data...")
logger.info('Recommand padding Sequence length is: {}'.format(
FLAGS.pad_seq_len))
logger.info('✔︎ Test data processing...')
test_data = data_helpers.load_data_and_labels(FLAGS.test_data_file,
FLAGS.num_classes,
FLAGS.embedding_dim)
logger.info('✔︎ Test data padding...')
x_test, y_test = data_helpers.pad_data(test_data, FLAGS.pad_seq_len)
y_test_bind = test_data.labels_bind
# Build vocabulary
VOCAB_SIZE = data_helpers.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = data_helpers.load_word2vec_matrix(
VOCAB_SIZE, FLAGS.embedding_dim)
# Load fasttext model
logger.info("✔ Loading model...")
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
logger.info(checkpoint_file)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
session_conf.gpu_options.allow_growth = FLAGS.gpu_options_allow_growth
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]
# pre-trained_word2vec
pretrained_embedding = graph.get_operation_by_name(
"embedding/embedding").outputs[0]
# Tensors we want to evaluate
logits = graph.get_operation_by_name("output/logits").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(list(
zip(x_test, y_test, y_test_bind)),
FLAGS.batch_size,
1,
shuffle=False)
# Collect the predictions here
all_predicitons = []
eval_loss, eval_rec, eval_acc, eval_counter = 0.0, 0.0, 0.0, 0
for batch_test in batches:
x_batch_test, y_batch_test, y_batch_test_bind = zip(
*batch_test)
feed_dict = {input_x: x_batch_test, dropout_keep_prob: 1.0}
batch_logits = sess.run(logits, feed_dict)
if FLAGS.use_classbind_or_not == 'Y':
predicted_labels = data_helpers.get_label_using_logits_and_classbind(
batch_logits,
y_batch_test_bind,
top_number=FLAGS.top_num)
if FLAGS.use_classbind_or_not == 'N':
predicted_labels = data_helpers.get_label_using_logits(
batch_logits, top_number=FLAGS.top_num)
all_predicitons = np.append(all_predicitons, predicted_labels)
cur_rec, cur_acc = 0.0, 0.0
for index, predicted_label in enumerate(predicted_labels):
rec_inc, acc_inc = data_helpers.cal_rec_and_acc(
predicted_label, y_batch_test[index])
cur_rec, cur_acc = cur_rec + rec_inc, cur_acc + acc_inc
cur_rec = cur_rec / len(y_batch_test)
cur_acc = cur_acc / len(y_batch_test)
eval_rec, eval_acc, eval_counter = eval_rec + cur_rec, eval_acc + cur_acc, eval_counter + 1
logger.info(
"✔︎ validation batch {} finished.".format(eval_counter))
eval_rec = float(eval_rec / eval_counter)
eval_acc = float(eval_acc / eval_counter)
logger.info("☛ Recall {:g}, Accuracy {:g}".format(
eval_rec, eval_acc))
np.savetxt(SAVE_FILE, list(zip(all_predicitons)), fmt='%s')
logger.info("✔ Done.")
def test_cnn():
"""Test CNN model."""
# Load data
logger.info("✔ Loading data...")
logger.info('Recommended padding Sequence length is: {0}'.format(
FLAGS.pad_seq_len))
logger.info('✔︎ Test data processing...')
test_data = dh.load_data_and_labels(FLAGS.test_data_file,
FLAGS.embedding_dim)
logger.info('✔︎ Test data padding...')
x_test_front, x_test_behind, y_test = dh.pad_data(test_data,
FLAGS.pad_seq_len)
# Build vocabulary
VOCAB_SIZE = dh.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = dh.load_word2vec_matrix(
VOCAB_SIZE, FLAGS.embedding_dim)
# Load cnn model
logger.info("✔ Loading model...")
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
logger.info(checkpoint_file)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
session_conf.gpu_options.allow_growth = FLAGS.gpu_options_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{0}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_x_front = graph.get_operation_by_name(
"input_x_front").outputs[0]
input_x_behind = graph.get_operation_by_name(
"input_x_behind").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]
is_training = graph.get_operation_by_name("is_training").outputs[0]
# pre-trained word2vec
pretrained_embedding = graph.get_operation_by_name(
"embedding/embedding").outputs[0]
# Tensors we want to evaluate
scores = graph.get_operation_by_name("output/scores").outputs
predictions = graph.get_operation_by_name(
"output/predictions").outputs[0]
softmax_scores = graph.get_operation_by_name(
"output/SoftMax_scores").outputs[0]
topKPreds = graph.get_operation_by_name(
"output/topKPreds").outputs[0]
accuracy = graph.get_operation_by_name(
"accuracy/accuracy").outputs[0]
loss = graph.get_operation_by_name("loss/loss").outputs[0]
# Split the output nodes name by '|' if you have several output nodes
output_node_names = 'output/scores|output/predictions|output/SoftMax_scores|output/topKPreds'
# Save the .pb model file
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_node_names.split("|"))
tf.train.write_graph(output_graph_def,
'graph',
'graph-cnn-{0}.pb'.format(MODEL_LOG),
as_text=False)
# Generate batches for one epoch
batches = dh.batch_iter(list(
zip(x_test_front, x_test_behind, y_test)),
FLAGS.batch_size,
1,
shuffle=False)
# Collect the predictions here
all_scores = []
all_softmax_scores = []
all_predictions = []
all_topKPreds = []
for index, x_test_batch in enumerate(batches):
x_batch_front, x_batch_behind, y_batch = zip(*x_test_batch)
feed_dict = {
input_x_front: x_batch_front,
input_x_behind: x_batch_behind,
input_y: y_batch,
dropout_keep_prob: 1.0,
is_training: False
}
batch_scores = sess.run(scores, feed_dict)
all_scores = np.append(all_scores, batch_scores)
batch_softmax_scores = sess.run(softmax_scores, feed_dict)
all_softmax_scores = np.append(all_softmax_scores,
batch_softmax_scores)
batch_predictions = sess.run(predictions, feed_dict)
all_predictions = np.concatenate(
[all_predictions, batch_predictions])
batch_topKPreds = sess.run(topKPreds, feed_dict)
all_topKPreds = np.append(all_topKPreds, batch_topKPreds)
batch_loss = sess.run(loss, feed_dict)
batch_acc = sess.run(accuracy, feed_dict)
logger.info(
"✔︎ Test batch {0}: loss {1:g}, accuracy {2:g}.".format(
(index + 1), batch_loss, batch_acc))
os.makedirs(SAVE_DIR)
np.savetxt(SAVE_DIR + '/result_sub_' + SUBSET + '.txt',
list(zip(all_predictions, all_topKPreds)),
fmt='%s')
logger.info("✔ Done.")
def test_cnn():
"""Test CNN model."""
# Load data
logger.info("✔ Loading data...")
logger.info('Recommand padding Sequence length is: {}'.format(
FLAGS.pad_seq_len))
logger.info('✔︎ Test data processing...')
test_data = data_helpers.load_data_and_labels(FLAGS.test_data_file,
FLAGS.embedding_dim)
logger.info('✔︎ Test data padding...')
x_test_front, x_test_behind, y_test = data_helpers.pad_data(
test_data, FLAGS.pad_seq_len)
# Build vocabulary
VOCAB_SIZE = data_helpers.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = data_helpers.load_word2vec_matrix(
VOCAB_SIZE, FLAGS.embedding_dim)
# Load cnn model
logger.info("✔ Loading model...")
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
logger.info(checkpoint_file)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
session_conf.gpu_options.allow_growth = FLAGS.gpu_options_allow_growth
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_front = graph.get_operation_by_name(
"input_x_front").outputs[0]
input_x_behind = graph.get_operation_by_name(
"input_x_behind").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]
# pre-trained_word2vec
pretrained_embedding = graph.get_operation_by_name(
"embedding/embedding").outputs[0]
# Tensors we want to evaluate
scores = graph.get_operation_by_name("output/scores").outputs
predictions = graph.get_operation_by_name(
"output/predictions").outputs[0]
softmaxScores = graph.get_operation_by_name(
"output/softmaxScores").outputs[0]
topKPreds = graph.get_operation_by_name(
"output/topKPreds").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(list(
zip(x_test_front, x_test_behind)),
FLAGS.batch_size,
1,
shuffle=False)
# Collect the predictions here
all_scores = []
all_softMaxScores = []
all_predictions = []
all_topKPreds = []
for x_test_batch in batches:
x_batch_front, x_batch_behind = zip(*x_test_batch)
feed_dict = {
input_x_front: x_batch_front,
input_x_behind: x_batch_behind,
dropout_keep_prob: 1.0
}
batch_scores = sess.run(scores, feed_dict)
all_scores = np.append(all_scores, batch_scores)
batch_softmax_scores = sess.run(softmaxScores, feed_dict)
all_softMaxScores = np.append(all_softMaxScores,
batch_softmax_scores)
batch_predictions = sess.run(predictions, feed_dict)
all_predictions = np.concatenate(
[all_predictions, batch_predictions])
batch_topKPreds = sess.run(topKPreds, feed_dict)
all_topKPreds = np.append(all_topKPreds, batch_topKPreds)
np.savetxt(SAVE_FILE,
list(zip(all_predictions, all_topKPreds)),
fmt='%s')
logger.info("✔ Done.")
def train_cnn():
"""Training CNN model."""
# Load sentences, labels, and training parameters
logger.info('✔︎ Loading data...')
logger.info('✔︎ Training data processing...')
train_data = data_helpers.load_data_and_labels(FLAGS.training_data_file,
FLAGS.embedding_dim)
logger.info('✔︎ Validation data processing...')
validation_data = data_helpers.load_data_and_labels(
FLAGS.validation_data_file, FLAGS.embedding_dim)
logger.info('Recommand padding Sequence length is: {}'.format(
FLAGS.pad_seq_len))
logger.info('✔︎ Training data padding...')
x_train_front, x_train_behind, y_train = data_helpers.pad_data(
train_data, FLAGS.pad_seq_len)
logger.info('✔︎ Validation data padding...')
x_validation_front, x_validation_behind, y_validation = data_helpers.pad_data(
validation_data, FLAGS.pad_seq_len)
# Build vocabulary
VOCAB_SIZE = data_helpers.load_vocab_size(FLAGS.embedding_dim)
pretrained_word2vec_matrix = data_helpers.load_word2vec_matrix(
VOCAB_SIZE, FLAGS.embedding_dim)
# Build a graph and cnn object
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
session_conf.gpu_options.allow_growth = FLAGS.gpu_options_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(sequence_length=FLAGS.pad_seq_len,
num_classes=y_train.shape[1],
vocab_size=VOCAB_SIZE,
embedding_size=FLAGS.embedding_dim,
embedding_type=FLAGS.embedding_type,
filter_sizes=list(
map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
l2_reg_lambda=FLAGS.l2_reg_lambda,
pretrained_embedding=pretrained_word2vec_matrix)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step,
name="train_op")
# 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", timestamp))
logger.info("✔︎ 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)
# Validation summaries
validation_summary_op = tf.summary.merge(
[loss_summary, acc_summary])
validation_summary_dir = os.path.join(out_dir, "summaries",
"validation")
validation_summary_writer = tf.summary.FileWriter(
validation_summary_dir, sess.graph)
# 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)
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=FLAGS.num_checkpoints)
# Initialize all variables
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
def train_step(x_batch_front, x_batch_behind, y_batch):
"""A single training step"""
feed_dict = {
cnn.input_x_front: x_batch_front,
cnn.input_x_behind: x_batch_behind,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
}
_, 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()
logger.info("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
train_summary_writer.add_summary(summaries, step)
def validation_step(x_batch_front,
x_batch_behind,
y_batch,
writer=None):
"""Evaluates model on a validation set"""
feed_dict = {
cnn.input_x_front: x_batch_front,
cnn.input_x_behind: x_batch_behind,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
step, summaries, scores, predictions, num_correct, \
loss, accuracy, recall, precision, f1, auc, topKPreds, = sess.run(
[global_step, validation_summary_op, cnn.scores, cnn.predictions, cnn.num_correct,
cnn.loss, cnn.accuracy, cnn.recall, cnn.precision, cnn.F1, cnn.AUC, cnn.topKPreds], feed_dict)
time_str = datetime.datetime.now().isoformat()
logger.info(
"{}: step {}, loss {:g}, acc {:g}, "
"recall {:g}, precision {:g}, f1 {:g}, AUC {}".format(
time_str, step, loss, accuracy, recall, precision, f1,
auc))
if writer:
writer.add_summary(summaries, step)
# Generate batches
batches = data_helpers.batch_iter(
list(zip(x_train_front, x_train_behind, y_train)),
FLAGS.batch_size, FLAGS.num_epochs)
# Training loop. For each batch...
for batch in batches:
x_batch_front, x_batch_behind, y_batch = zip(*batch)
train_step(x_batch_front, x_batch_behind, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
logger.info("\nEvaluation:")
validation_step(x_validation_front,
x_validation_behind,
y_validation,
writer=validation_summary_writer)
if current_step % FLAGS.checkpoint_every == 0:
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
logger.info(
"✔︎ Saved model checkpoint to {}\n".format(path))
logger.info("✔︎ Done.")
