def main(_):
with tf.device('/gpu:0'):
# Input
x = tf.placeholder(tf.float32,
[None, model.time_step, model.num_input])
y_ = tf.placeholder(tf.float32, [None, model.num_class])
# Create lstm model
y_lstm, keep_prob = cross.lstm(x)
# Define loss
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_, logits=y_lstm)
cross_entropy = tf.reduce_mean(cross_entropy)
# Define optimizer
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(learn_rate).minimize(
cross_entropy)
# Create the node to calculate ccc
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_lstm, 1),
tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
# Create the node to calculate auc
with tf.name_scope('auc'):
labels = tf.reshape(
tf.slice(tf.cast(y_, dtype=tf.bool), [0, 0], [-1, 1]), [-1])
predictions = tf.reshape(
tf.subtract(tf.slice(y_lstm, [0, 0], [-1, 1]),
tf.slice(y_lstm, [0, 1], [-1, 1])), [-1])
# Min Max Normalization
Y_pred = (predictions - tf.reduce_min(predictions)) / (
tf.reduce_max(predictions) - tf.reduce_min(predictions))
roc_auc, roc_auc_update_op = tf.metrics.auc(labels,
Y_pred,
curve='ROC',
name='roc')
# Create the node to calculate acc
with tf.name_scope('metrics'):
acc, acc_op = tf.metrics.accuracy(tf.argmax(y_, 1),
tf.argmax(y_lstm, 1))
rec, rec_op = tf.metrics.recall(tf.argmax(y_, 1),
tf.argmax(y_lstm, 1))
all_pos = tf.reduce_sum(tf.argmin(y_lstm, 1))
all_neg = tf.reduce_sum(tf.argmax(y_lstm, 1))
fn, fn_op = tf.metrics.false_negatives(tf.argmax(y_, 1),
tf.argmax(y_lstm, 1))
fp, fp_op = tf.metrics.false_positives(tf.argmax(y_, 1),
tf.argmax(y_lstm, 1))
# Add ops to save and restore all the variables
saver = tf.train.Saver()
sess = tf.InteractiveSession()
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)) as sess:
for seed in range(1, seeds_num + 1):
print('*' * 30, 'seed=', seed, '*' * 30)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(
LOGS_DIRECTORY, graph=tf.get_default_graph())
sum_acc = 0
sum_auc = 0
sum_spec = 0
sum_recall = 0
record_fn = 0
record_fp = 0
training_accuracy_list = []
all_piRNA = input_data.read_all(TRAIN_IMAGES,
TRAIN_LABELS,
test_size=test_size,
seed=seed,
is_display=is_display)
test_accuracy_list = []
for fold in range(10):
print('fold %d:' % fold)
piRNA = input_data.read_CV_datasets(
fold, int(DATA_NUM * (1 - test_size)), all_piRNA)
for i in range(TOTAL_BATCH):
batch_x, batch_y = piRNA.train.next_batch(batch_size)
batch_x = batch_x.reshape(batch_size, model.time_step,
model.num_input)
step, training_accuracy = sess.run(
[train_step, accuracy],
feed_dict={
x: batch_x,
y_: batch_y,
keep_prob: 0.5
})
# print out results
if i % 50 == 0:
print('step %d, training accuracy %g' %
(i, training_accuracy))
training_accuracy_list.append(training_accuracy)
if i % 1000 == 0:
print('test accuracy %g' % accuracy.eval(
feed_dict={
x:
piRNA.test.images.reshape(
-1, model.time_step, model.num_input),
y_:
piRNA.test.labels,
keep_prob:
1.0
}))
auc, acc, recall, pred_neg, false_nega, false_posi, pred_pos = sess.run(
[
roc_auc_update_op, acc_op, rec_op, all_neg, fn_op,
fp_op, all_pos
],
feed_dict={
x:
piRNA.validation.images.reshape(
-1, model.time_step, model.num_input),
y_:
piRNA.validation.labels,
keep_prob:
1.0
})
# update specificity
current_fn = false_nega - record_fn
current_fp = false_posi - record_fp
true_nega = pred_neg - current_fn # fp_op accumulate every loop
spec = true_nega / (true_nega + current_fp)
record_fn = false_nega
record_fp = false_posi
test_accuracy = accuracy.eval(
feed_dict={
x:
piRNA.test.images.reshape(-1, model.time_step,
model.num_input),
y_:
piRNA.test.labels,
keep_prob:
1.0
})
test_accuracy_list.append(test_accuracy)
# Test Set
print('Test set accuracy %g' % test_accuracy)
# 10-CV metrices (acc, auc)
sum_acc = cv.acc(sum_acc, acc, fold, is_display=is_display)
sum_auc = cv.auc(sum_auc, auc, fold, is_display=is_display)
sum_spec = cv.spec(sum_spec,
spec,
fold,
is_display=is_display)
sum_recall = cv.recall(sum_recall,
recall,
fold,
is_display=is_display)
test_accuracy_average = cv.average(test_accuracy_list)
auc_average = cv.average(cv.auc_list)
acc_average = cv.average(cv.acc_list)
spec_average = cv.average(cv.spec_list)
recall_average = cv.average(cv.recall_list)
acc_list.append(acc_average)
auc_list.append(auc_average)
spec_list.append(spec_average)
recall_list.append(recall_average)
test_acc_list.append(test_accuracy_average)
if is_display:
print('*** Test accuracy is:', test_accuracy_list)
print('*** The average test accuracy is:%.3f' %
test_accuracy_average)
print('acc', acc_average)
print('auc', auc_average)
print('spec', spec_average)
print('recall', recall_average)
data_frame = pd.DataFrame({
'AUC': auc_list,
'ACC': acc_list,
'SP': spec_list,
'SN': recall_list,
'Test ACC': test_acc_list
})
data_frame.to_csv('human1vs3cross.csv',
index=True,
columns=['AUC', 'ACC', 'SP', 'SN', 'Test ACC'])
def main(_):
# Import data
# piRNA = input_data.read_data_sets()
# Create the model
x = tf.placeholder(tf.float32, [None, 175])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 2])
# Build the graph for the deep net
# y_conv, keep_prob = ctm.deepnn(x)
y_conv, keep_prob = cnn_lin_model.CNN(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
# Create a summary to monitor loss tensor
tf.summary.scalar('loss', cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
# define auc monitor
with tf.name_scope('auc'):
# labels = tf.slice(tf.greater(y_, 0.5), [0, 0], [-1, 1])
# tf.reshape(a, [-1])
labels = tf.reshape(tf.slice(tf.cast(y_, dtype=tf.bool), [0,0],[-1,1]), [-1])
predictions = tf.reshape(tf.subtract(tf.slice(y_conv, [0,0], [-1,1]), tf.slice(y_conv, [0,1], [-1,1])), [-1])
# Min Max Normalization
Y_pred = (predictions-tf.reduce_min(predictions))/(tf.reduce_max(predictions) - tf.reduce_min(predictions))
roc_auc, roc_auc_update_op = tf.metrics.auc(labels, Y_pred, curve='ROC', name='roc')
# create a summary to monitor accuracy tensor
tf.summary.scalar('acc', accuracy)
# Merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()
# Add ops to save and restore all the variables
saver = tf.train.Saver()
sess = tf.InteractiveSession()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(LOGS_DIRECTORY, graph=tf.get_default_graph())
max_acc = 0
sum_acc = 0
sum_auc = 0
# shuffle the training set
all_piRNA = input_data.read_all()
for fold in range(5):
print('fold %d:' % fold)
piRNA = input_data.read_CV_datasets(fold, all_piRNA)
# piRNA = input_data.read_data_sets(fold)
for i in range(TOTAL_BATCH):
batch = piRNA.train.next_batch(50)
step, training_accuracy, summary = sess.run([train_step, accuracy, merged_summary_op], feed_dict={x:batch[0], y_:batch[1], keep_prob:0.5})
# Write logs at every iteration
summary_writer.add_summary(summary, i)
# print out results
if i % 50 == 0:
print('step %d, training accuracy %g' % (i, training_accuracy))
if i % 1000 == 0:
print('test accuracy %g' % accuracy.eval(feed_dict={x: piRNA.test.images, y_:piRNA.test.labels, keep_prob: 1.0}))
# Validation Set
acc = accuracy.eval(feed_dict={x:piRNA.validation.images, y_:piRNA.validation.labels, keep_prob:1.0})
auc = sess.run(roc_auc_update_op, feed_dict={x: piRNA.validation.images, y_: piRNA.validation.labels, keep_prob:1.0})
# labels, predictions, Y_pred = sess.run([labels, predictions, Y_pred], feed_dict={x:piRNA.validation.images, y_:piRNA.validation.labels, keep_prob:1.0}) # For Debug
# print(labels)
# print(predictions)
# print(Y_pred)
# Test Set
print('Test Set accuracy %g' % accuracy.eval(feed_dict={x: piRNA.test.images, y_:piRNA.test.labels, keep_prob: 1.0}))
# 5-CV metrices (acc, auc)
sum_acc = cv.acc(sum_acc, acc, fold)
sum_auc = cv.auc(sum_auc, auc, fold)