def run_eval(self, sess, data, summary_writer=None, step=0):
y, y_pred, loss_, metrics, p_k = list(), list(), 0.0, None, None
accuracy, loss = 0.0, 0.0
merged_summary = self.summarizer.merge_all()
i = 0
for X, Y, tot in self.data.next_batch(data):
feed_dict = {self.x: X, self.y: Y, self.z : np.random.random((Y.shape[0], self.config.solver.randomvar_dim)), self.keep_prob: 1}
if i == tot-1 and summary_writer is not None:
if data == "validation":
summ, loss_ = sess.run([merged_summary, self.loss], feed_dict=feed_dict)
else :
summ, loss_, accuracy_val = sess.run([merged_summary, self.loss, self.accuracy], feed_dict=feed_dict)
summary_writer.add_summary(summ, step)
else:
if data == "validation":
loss_, Y_pred= sess.run([self.loss, tf.nn.sigmoid(self.y_pred)], feed_dict=feed_dict)
p_k = patk(predictions=Y_pred, labels=Y)
else :
loss_, Y_pred, accuracy_val = sess.run([self.loss, tf.nn.sigmoid(self.y_pred), self.accuracy], feed_dict=feed_dict)
metrics = evaluate(predictions=Y_pred, labels=Y)
p_k = patk(predictions=Y_pred, labels=Y)
accuracy += accuracy_val #metrics['accuracy']
loss += loss_
i += 1
return loss / i , accuracy / self.config.batch_size, metrics, p_k
def run_eval(self,
sess,
data,
summary_writer=None,
step=0,
type_loss="NORMAL"):
y, y_pred, loss_, metrics, p_k, Y, Y_pred = list(), list(
), 0.0, None, None, None, None
accuracy, loss = 0.0, 0.0
merged_summary = self.summarizer.merge_all()
i = 0
for X, Y, tot in self.data.next_batch(data):
feed_dict = {self.x: X, self.y: Y, self.keep_prob: 1}
if (type_loss == "AUTO"):
summ, loss_ = sess.run([merged_summary, self.autoencoder_loss],
feed_dict=feed_dict)
else:
if i == tot - 1 and summary_writer is not None:
if data == "validation":
summ, loss_ = sess.run([merged_summary, self.loss],
feed_dict=feed_dict)
else:
summ, loss_, accuracy_val = sess.run(
[merged_summary, self.loss, self.accuracy],
feed_dict=feed_dict)
summary_writer.add_summary(summ, step)
else:
if data == "validation":
loss_, Y_pred = sess.run(
[self.loss, tf.nn.sigmoid(self.y_pred)],
feed_dict=feed_dict)
p_k = patk(predictions=Y_pred, labels=Y)
else:
loss_, Y_pred, accuracy_val = sess.run(
[
self.loss,
tf.nn.sigmoid(self.y_pred), self.accuracy
],
feed_dict=feed_dict)
metrics = evaluate(predictions=Y_pred, labels=Y)
accuracy += accuracy_val #metrics['accuracy']
loss += loss_
i += 1
if data == "test":
#X, Y = self.data.get_test()
p_k = patk(
predictions=Y_pred, labels=Y
) #sess.run(self.patk, feed_dict={self.x: X, self.y: Y, self.keep_prob: 1})
return loss / i, accuracy / self.config.batch_size, metrics, p_k
def get_metrics(self, sess, data):
accuracy, y_pred, i = 0.0, None, 0.0
for X, Y, tot in self.data.next_batch(data):
feed_dict = {self.x: X, self.y: Y, self.keep_prob: 1.0}
Y_pred, accuracy_val = sess.run([tf.nn.sigmoid(self.y_pred), self.accuracy], feed_dict=feed_dict)
metrics = evaluate(predictions=Y_pred, labels=Y)
p_k = patk(predictions=Y_pred, labels=Y)
accuracy += accuracy_val
i += 1
return metrics, accuracy / i, p_k