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aggregated.py
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aggregated.py
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import os
import random
import argparse
import tensorflow as tf
import numpy as np
import pandas as pd
from utils import load_data, load_model
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='model evaluation')
parser.add_argument('-s', '--seed', default=0, type=int, help='random seed')
parser.add_argument('-m', '--mpath', default='test', help='path of model')
args = parser.parse_args()
seed = args.seed
model_path = args.mpath
if not os.path.exists(os.path.join(os.path.join(os.getcwd(), 'model'), 'aggregated.txt')):
df = pd.DataFrame(columns=['Model', 'logdetcov', 'mean_square'])
df.to_csv(os.path.join(os.path.join(os.getcwd(), 'model'), 'aggregated.txt'), index=False, float_format='%.6f')
df = pd.read_csv(os.path.join(os.path.join(os.getcwd(), 'model'), 'aggregated.txt'))
model, batch_size, datapath = load_model(model_path)
if os.path.basename(model_path) in list(df['Model']):
print('Results already exists.')
exit()
else:
dic = {'Model': os.path.basename(model_path)}
_, _, test_dataset = load_data(batch_size, datapath, is_train=False)
tf.random.set_seed(seed)
np.random.seed(seed)
random.seed(seed)
logdetcov = []
mean_square = []
for _ in range(5):
initial = True
for x_batch_test in test_dataset:
if initial:
sample_z = model.encoding(x_batch_test)[0]
initial = False
else:
z = model.encoding(x_batch_test)[0]
sample_z = tf.keras.backend.concatenate([sample_z, z], axis=0)
logdetcov.append(np.log(np.linalg.det(np.cov(sample_z.numpy(), rowvar=False))))
mean_square.append(np.sum(np.square(np.mean(sample_z.numpy(), axis=0))))
dic['logdetcov'] = np.mean(logdetcov)
dic['mean_square'] = np.mean(mean_square)
print(dic)
df = pd.concat([df, pd.DataFrame(dic, index=[0])], ignore_index=True)
df.to_csv(os.path.join(os.path.join(os.getcwd(), 'model'), 'aggregated.txt'), index=False, float_format='%.6f')