placeholder_dict = datapipeline.get_placeholder_dict()
feed_dict_train = datapipeline.get_feed_dict(mode=TRAIN)
feed_dict_val = datapipeline.get_feed_dict(mode=VALIDATION)
feed_dict_test = datapipeline.get_feed_dict(mode=TEST)
sparse_model_params = SparseModelParams(
num_elements=datapipeline.num_elements,
feature_size=datapipeline.feature_size
)
model = select_model(model_name=model_params.model_name)(
model_params = model_params,
sparse_model_params = sparse_model_params,
placeholder_dict = placeholder_dict
)
sess = tf.Session()
K.set_session(sess)
sess.run(tf.global_variables_initializer())
for epoch in range(model_params.epochs):
start_time = time()
loss, accuracy, opt = sess.run([model.loss, model.accuracy, model.optimizer_op], feed_dict=feed_dict_train)
loss_val, accuracy_val = sess.run([model.loss, model.accuracy], feed_dict=feed_dict_test)
print(accuracy_val)
# print(accuracy)
def run(model_params, data_dir, dataset_name, experiment=None):
datapipeline = DataPipelineAE(model_params=model_params,
data_dir=data_dir,
dataset_name=dataset_name)
feed_dict_train = datapipeline.get_feed_dict(mode=TRAIN)
feed_dict_val = datapipeline.get_feed_dict(mode=VALIDATION)
feed_dict_test = datapipeline.get_feed_dict(mode=TEST)
sparse_model_params = datapipeline.get_sparse_model_params()
autoencoder_model_params = datapipeline.get_autoencoder_model_params()
placeholder_dict = datapipeline.get_placeholder_dict()
if (experiment):
experiment.add_config(sparse_model_params.get_variables())
experiment.add_config(autoencoder_model_params.get_variables())
sess = tf.Session()
K.set_session(sess)
train_loss_runs = []
validation_loss_runs = []
test_aucscore_runs = []
test_apr_runs = []
for num_exp in range(model_params.num_exp):
model = select_model(model_name=model_params.model_name)(
model_params=model_params,
sparse_model_params=sparse_model_params,
placeholder_dict=placeholder_dict,
autoencoder_model_params=autoencoder_model_params)
if (model_params.tensorboard_logs_dir):
train_writer = tf.summary.FileWriter(
model_params.tensorboard_logs_dir + model_params.model_name +
"/" + TRAIN, sess.graph)
val_writer = tf.summary.FileWriter(
model_params.tensorboard_logs_dir + model_params.model_name +
"/" + VALIDATION, sess.graph)
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer()
])
train_loss_list = []
validation_loss_list = []
test_aucscore_list = []
test_apr_list = []
for epoch in range(model_params.epochs):
loss, accuracy, opt, summary = sess.run([
model.loss, model.accuracy, model.optimizer_op,
model.summary_op
],
feed_dict=feed_dict_train)
loss_val, accuracy_val, summary_val = sess.run(
[model.loss, model.accuracy, model.summary_op],
feed_dict=feed_dict_val)
if (model_params.tensorboard_logs_dir):
train_writer.add_summary(summary, epoch)
val_writer.add_summary(summary_val, epoch)
embedding, predictions_test, labels_test, mask_test, loss_test, accuracy_test, summary_test = sess.run(
[
model.embeddings, model.logits, model.labels, model.mask,
model.loss, model.accuracy, model.summary_op
],
feed_dict=feed_dict_test)
auc_score = compute_auc_score(labels=labels_test,
predictions=predictions_test,
mask=mask_test)
test_aucscore_list.append(auc_score)
apr = compute_average_precision_recall(
labels=labels_test,
predictions=predictions_test,
mask=mask_test)
test_apr_list.append(apr)
train_loss_list.append(loss)
validation_loss_list.append(loss_val)
print(
"For epoch:run {}:{}, training_loss = {}, validation_loss = {}, test_auc = {}, test_apr = {}"
.format(epoch, num_exp, loss, loss_val, auc_score, apr))
train_loss_runs.append(train_loss_list)
validation_loss_runs.append(validation_loss_list)
test_aucscore_runs.append(test_aucscore_list)
test_apr_runs.append(test_apr_list)
plot_loss_curves(train_loss_runs,
validation_loss_runs,
dataset_name=dataset_name,
model_params=model_params)
print_stats(train_loss_runs,
validation_loss_runs,
test_metrics=[test_aucscore_runs, test_apr_runs],
test_metrics_labels=[AUCSCORE, AVERAGE_PRECISION_RECALL_SCORE])
data_dir = FLAGS.data_dir
dataset_name = FLAGS.dataset_name
datapipeline = DataPipelineAE(model_params=model_params,
data_dir=data_dir,
dataset_name=dataset_name)
feed_dict_train = datapipeline.get_feed_dict(mode=TRAIN)
feed_dict_val = datapipeline.get_feed_dict(mode=VALIDATION)
feed_dict_test = datapipeline.get_feed_dict(mode=TEST)
model = select_model(model_name=model_params.model_name)(
model_params=model_params,
sparse_model_params=datapipeline.get_sparse_model_params(),
placeholder_dict=datapipeline.get_placeholder_dict(),
autoencoder_model_params=datapipeline.get_autoencoder_model_params())
sess = tf.Session()
K.set_session(sess)
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
for epoch in range(model_params.epochs):
start_time = time()
loss, accuracy, opt = sess.run(
[model.loss, model.accuracy, model.optimizer_op],
feed_dict=feed_dict_train)
predictions_val, labels_val, mask_val, loss_val, accuracy_val = \
sess.run([model.logits, model.labels, model.mask, model.loss, model.accuracy], feed_dict=feed_dict_val)
print(
def run(model_params, data_dir, dataset_name, experiment=None):
datapipeline = DataPipeline(model_params=model_params,
data_dir=data_dir,
dataset_name=dataset_name)
placeholder_dict = datapipeline.get_placeholder_dict()
feed_dict_train = datapipeline.get_feed_dict(mode=TRAIN)
feed_dict_val = datapipeline.get_feed_dict(mode=VALIDATION)
feed_dict_test = datapipeline.get_feed_dict(mode=TEST)
sparse_model_params = SparseModelParams(
num_elements=datapipeline.num_elements,
feature_size=datapipeline.feature_size)
if (experiment):
experiment.add_config(sparse_model_params.get_variables())
sess = tf.Session()
K.set_session(sess)
train_loss_runs = []
validation_loss_runs = []
test_accuracy_runs = []
model = None
for _ in range(model_params.num_exp):
model = select_model(model_name=model_params.model_name)(
model_params=model_params,
sparse_model_params=sparse_model_params,
placeholder_dict=placeholder_dict)
if (model_params.tensorboard_logs_dir):
train_writer = tf.summary.FileWriter(
model_params.tensorboard_logs_dir + model_params.model_name +
"/" + TRAIN, sess.graph)
val_writer = tf.summary.FileWriter(
model_params.tensorboard_logs_dir + model_params.model_name +
"/" + VALIDATION, sess.graph)
sess.run(tf.global_variables_initializer())
train_loss_list = []
validation_loss_list = []
test_accuracy_list = []
for epoch in range(model_params.epochs):
loss, accuracy, opt, summary = sess.run([
model.loss, model.accuracy, model.optimizer_op,
model.summary_op
],
feed_dict=feed_dict_train)
loss_val, accuracy_val, summary_val = sess.run(
[model.loss, model.accuracy, model.summary_op],
feed_dict=feed_dict_val)
if (model_params.tensorboard_logs_dir):
train_writer.add_summary(summary, epoch)
val_writer.add_summary(summary_val, epoch)
train_loss_list.append(loss)
validation_loss_list.append(loss_val)
accuracy_test = sess.run([model.accuracy],
feed_dict=feed_dict_test)
test_accuracy_list.append(accuracy_test)
train_loss_runs.append(train_loss_list)
validation_loss_runs.append(validation_loss_list)
test_accuracy_runs.append(test_accuracy_list)
plot_loss_curves(train_loss_runs,
validation_loss_runs,
dataset_name=dataset_name,
model_params=model_params)
print_stats(train_loss_runs,
validation_loss_runs,
test_metrics=[test_accuracy_runs],
test_metrics_labels=[ACCURACY])
activations, khot_labels, mask = sess.run(
[model.activations, model.labels, model.mask],
feed_dict=feed_dict_train)
embedd_and_plot(node_representation=activations[-2],
labels=khot_labels,
mask=mask)