Exemple #1
0
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)