Commit a82feab7 authored by Liu Qianqiao's avatar Liu Qianqiao
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parent a16e5a72
from keras.callbacks import Callback, EarlyStopping, ModelCheckpoint
class TrainingHistory(Callback):
def __init__(self, x_test, y_test, CLASSES_LIST):
super(Callback, self).__init__()
self.x_test = x_test
self.y_test = y_test
self.CLASSES_LIST = CLASSES_LIST
def on_train_begin(self, logs={}):
self.losses = []
self.epoch_losses = []
self.epoch_val_losses = []
self.val_losses = []
self.predictions = []
self.epochs = []
self.f1 = []
self.i = 0
self.save_every = 50
def on_epoch_end(self, epoch, logs={}):
y_predicted = self.model.predict(self.x_test).argmax(1)
print(y_predicted.shape)
print("Test Accuracy:", accuracy_score(self.y_test, y_predicted))
p, r, f1, s = precision_recall_fscore_support(self.y_test, y_predicted,
average='micro',
labels=[x for x in
self.CLASSES_LIST])
print('p r f1 %.1f %.1f %.1f' % (np.average(p, weights=s)*100.0,
np.average(r, weights=s)*100.0,
np.average(f1, weights=s)*100.0))
try:
print(classification_report(self.y_test, y_predicted, labels=[x for x in
self.CLASSES_LIST]))
except:
print('ZERO')
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