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validate.py
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validate.py
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import numpy as np
from keras.models import load_model
import pickle
import keras.backend as K
import tensorflow as tf
from plain_data_make import load_kfold_names, PlainPreprocessor
from plain_model import hyper_params, gen_pp_data_dir_name
import os
from model_tool import to_multilabel
import matplotlib.pyplot as plt
preprocessor = PlainPreprocessor(hyper_params)
if __name__ == '__main__':
## Add your codes to classify normal and illness.
## Classify the samples of the test set and write the results into answers.txt,
## and each row representing a prediction of one sample.
## Here we use random numbers as prediction labels as an example and
## you should replace it with your own results.
# preprocess the test data
'''load kfold data'''
data_dir = gen_pp_data_dir_name()
kfold_idx, train_names = load_kfold_names()
f1_score = []
model = load_model('models/rematch_ckpt_plain45165_0_092_0.0994_0.1069_0.9600.h5')
model.summary()
for _, val_idx in kfold_idx:
names = [train_names[idx] for idx in val_idx]
x = []
y = []
for name in names:
train_sig, pre_train_sig, pre_train_label = pickle.load(
open(os.path.join(data_dir, name + '.dat'), 'rb'))
x.append(np.transpose(pre_train_sig))
y.append(pre_train_label)
x = np.array(x)
predicted = model.predict(x)
predicted = np.argmax(predicted, axis=2)
expected = y
# for idx in range(len(predicted)):
# plt.plot(x[idx])
# plt.plot(predicted[idx])
# plt.plot(expected[idx])
# plt.show()