NoEigenComp = 20
# ----------------------------- i/o ------------------------------------------
DataDir = '../Cl_data/Data/'
PlotsDir = '../Cl_data/Plots/'
ModelDir = '../Cl_data/Model/'
train_path = DataDir + 'LatinCl_' + str(totalFiles) + '.npy'
train_target_path = DataDir + 'LatinPara5_' + str(totalFiles) + '.npy'
test_path = DataDir + 'LatinCl_' + str(TestFiles) + '.npy'
test_target_path = DataDir + 'LatinPara5_' + str(TestFiles) + '.npy'
camb_in = Cl_load.cmb_profile(train_path=train_path,
train_target_path=train_target_path,
test_path=test_path,
test_target_path=test_target_path,
num_para=5)
(x_train, y_train), (x_test, y_test) = camb_in.load_data()
x_train = x_train[:, 2:]
x_test = x_test[:, 2:]
# x_train = x_train[:,2::2]
# x_test = x_test[:,2::2]
print(x_train.shape, 'train sequences')
print(x_test.shape, 'test sequences')
print(y_train.shape, 'train sequences')
print(y_test.shape, 'test sequences')
def train(self, epochs, batch_size=128, save_interval=50):
camb_in = Cl_load.cmb_profile(train_path=train_path,
train_target_path=train_target_path,
test_path=test_path,
test_target_path=test_target_path,
num_para=5)
(x_train, y_train), (x_test, y_test) = camb_in.load_data()
x_train = x_train[:, 2:]
x_test = x_test[:, 2:]
print(x_train.shape, 'train sequences')
print(x_test.shape, 'test sequences')
print(y_train.shape, 'train sequences')
print(y_test.shape, 'test sequences')
normFactor = np.max([np.max(x_train), np.max(x_test)])
print('-------normalization factor:', normFactor)
x_train = x_train.astype('float32') / normFactor #/ 255.
x_test = x_test.astype('float32') / normFactor #/ 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
# Load the dataset
#(X_train, _), (_, _) = mnist.load_data()
# Rescale -1 to 1
#X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(x_train, axis=3)
half_batch = int(batch_size / 2)
for epoch in range(epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random half batch of images
idx = np.random.randint(0, X_train.shape[0], half_batch)
imgs = X_train[idx]
# Generate a half batch of embedded images
latent_fake = self.encoder.predict(imgs)
latent_real = np.random.normal(size=(half_batch, self.encoded_dim))
valid = np.ones((half_batch, 1))
fake = np.zeros((half_batch, 1))
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(latent_real, valid)
d_loss_fake = self.discriminator.train_on_batch(latent_fake, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
# Select a random half batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
# Generator wants the discriminator to label the generated representations as valid
valid_y = np.ones((batch_size, 1))
# Train the generator
g_loss = self.adversarial_autoencoder.train_on_batch(
imgs, [imgs, valid_y])
# Plot the progress
print("%d [D loss: %f, acc: %.2f%%] [G loss: %f, mse: %f]" %
(epoch, d_loss[0], 100 * d_loss[1], g_loss[0], g_loss[1]))
plt.figure(1032)
plt.plot(epoch, d_loss[0], 'ko')
plt.plot(epoch, g_loss[0], 'ro')
plt.plot(epoch, g_loss[0], 'bo')
plt.figure(1031)
plt.plot(epoch, 100 * d_loss[1], 'go')
# If at save interval => save generated image samples
if epoch % save_interval == 0:
# Select a random half batch of images
idx = np.random.randint(0, X_train.shape[0], 25)
imgs = X_train[idx]
self.save_imgs(epoch, imgs)