test_size = 0.2 # 20%
val_size = 0.25 # 25% of trainning size
n_train = int(N_sample * (1 - test_size) * (1 - val_size))
epochs = int(n_iter / (n_train / batch_size))
f = open("Trainning_INFO_Regression_58k_QQBAL.txt", "w+")
f.write('INFO: Epochs:{} -- Batch size:{} \n'.format(epochs, batch_size))
start = time.time()
X, y = Load_Files('truth_DR12Q.fits',
'data_dr12.fits',
N_sample, ['QSO', 'QSO_BAL'],
classification=False)
train_loader, test_loader, val_loader = Data_Loader(X, y, N_sample, batch_size,
test_size, val_size)
"""
for i in range(100):
x=np.linspace(300,1000,443)
print('Redshift:{}'.format(y[i]))
plt.plot(x,X[i,:])
plt.xlabel('Wavelength')
plt.ylabel('Renormalized Flux [Arb unix]')
plt.show()
"""
# In[13]:
n_iter = 10000
test_size = 0.2 # 20%
val_size = 0.25 # 25% of trainning size
n_train = int(N_sample * (1 - test_size) * (1 - val_size))
epochs = int(n_iter / (n_train / batch_size))
fi = open("Trainning_INFO_80k.txt", "w+")
fi.write('INFO: Epochs:{} -- Batch size:{} \n'.format(epochs, batch_size))
start = time.time()
X, y = Load_Files('truth_DR12Q.fits',
'data_dr12.fits',
N_sample,
None,
classification=True)
train_loader, test_loader, val_loader, train_s, test_s, val_s = Data_Loader(
X, y, N_sample, batch_size, test_size, val_size, classification=True)
# CNN for classification
learning_rate = 0.1
class Net_C(nn.Module):
def __init__(self):
super(Net_C, self).__init__()
self.conv1 = nn.Conv1d(1, 64, 15, stride=2)
self.conv2 = nn.Conv1d(64, 128, 15, stride=2)