|
243 | 243 | cv_acc_list = [] |
244 | 244 | cv_loss_list = [] |
245 | 245 | for v in range(0,len(cv_x)-int(len(cv_x) % validating_size),validating_size): |
246 | | - acc_on_cv,loss_on_cv,preds = sess.run([acc,cross_entropy,tf.nn.softmax(y_pred)],feed_dict= |
247 | | - {x:cv_x[v:v+validating_size] , |
248 | | - y_true:cv_y[v:v+validating_size] , |
249 | | - hold_prob1:1.0, |
250 | | - hold_prob2:1.0}) |
| 246 | + acc_on_cv,loss_on_cv,preds = sess.run([acc,cross_entropy,tf.nn.softmax(y_pred)], |
| 247 | +feed_dict={x:cv_x[v:v+validating_size] ,y_true:cv_y[v:v+validating_size] ,hold_prob1:1.0,hold_prob2:1.0}) |
| 248 | + |
251 | 249 | auc_on_cv = roc_auc_score(cv_y[v:v+validating_size],preds) |
252 | 250 | cv_acc_list.append(acc_on_cv) |
253 | 251 | cv_auc_list.append(auc_on_cv) |
|
264 | 262 | test_acc_list = [] |
265 | 263 | test_loss_list = [] |
266 | 264 | for v in range(0,len(test_x)-int(len(test_x) % validating_size),validating_size): |
267 | | - acc_on_test,loss_on_test,preds = sess.run([acc,cross_entropy,tf.nn.softmax(y_pred)],feed_dict=\ |
268 | | -{x:test_x[v:v+validating_size] ,\ |
269 | | -y_true:test_y[v:v+validating_size] ,\ |
270 | | -hold_prob1:1.0,\ |
271 | | -hold_prob2:1.0}) |
| 265 | + acc_on_test,loss_on_test,preds = sess.run([acc,cross_entropy,tf.nn.softmax(y_pred)], |
| 266 | +feed_dict={x:test_x[v:v+validating_size] ,y_true:test_y[v:v+validating_size] ,hold_prob1:1.0,hold_prob2:1.0}) |
272 | 267 |
|
273 | 268 | auc_on_test = roc_auc_score(test_y[v:v+validating_size],preds) |
274 | 269 | test_acc_list.append(acc_on_test) |
|
0 commit comments