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