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Commit 76756103 authored by JGlombitza's avatar JGlombitza
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remove unused functiony from utils 

add build_critic_training
add build_generator_training
parent 12d43376
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AirShower_WGAN/ShowerGAN.py
+ 9
− 18
View file @ 76756103
...@@ -16,7 +16,7 @@ import tensorflow as tf ...@@ -16,7 +16,7 @@ import tensorflow as tf
KTF.set_session(utils.get_session()) # Allows 2 jobs per GPU, Please do not change this during the tutorial KTF.set_session(utils.get_session()) # Allows 2 jobs per GPU, Please do not change this during the tutorial
log_dir="." log_dir="."
EPOCHS = 10 EPOCHS = 3
GRADIENT_PENALTY_WEIGHT = 10 GRADIENT_PENALTY_WEIGHT = 10
BATCH_SIZE = 256 BATCH_SIZE = 256
NCR = 5 NCR = 5
...@@ -62,7 +62,7 @@ def build_generator(latent_size): ...@@ -62,7 +62,7 @@ def build_generator(latent_size):
outputs = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal', activation='relu')(x) outputs = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal', activation='relu')(x)
return Model(inputs=inputs, outputs=outputs, name='generator') return Model(inputs=inputs, outputs=outputs, name='generator')
# build critic
def build_critic(): def build_critic():
critic = Sequential(name='critic') critic = Sequential(name='critic')
critic.add(Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', input_shape=(9,9,1))) critic.add(Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', input_shape=(9,9,1)))
...@@ -79,6 +79,7 @@ def build_critic(): ...@@ -79,6 +79,7 @@ def build_critic():
critic.add(Dense(1)) critic.add(Dense(1))
return critic return critic
generator = build_generator(latent_size) generator = build_generator(latent_size)
#plot_model(generator, to_file=log_dir + '/generator.png', show_shapes=True) #plot_model(generator, to_file=log_dir + '/generator.png', show_shapes=True)
critic = build_critic() critic = build_critic()
...@@ -88,10 +89,7 @@ critic = build_critic() ...@@ -88,10 +89,7 @@ critic = build_critic()
utils.make_trainable(critic, False) utils.make_trainable(critic, False)
utils.make_trainable(generator, True) utils.make_trainable(generator, True)
generator_in = Input(shape=(latent_size,)) generator_training = utils.build_generator_graph(generator, critic, latent_size)
generator_out = generator(generator_in)
critic_out = critic(generator_out)
generator_training = Model(inputs=generator_in, outputs=critic_out)
generator_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0), loss=[utils.wasserstein_loss]) generator_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0), loss=[utils.wasserstein_loss])
#plot_model(generator_training, to_file=log_dir + '/generator_training.png', show_shapes=True) #plot_model(generator_training, to_file=log_dir + '/generator_training.png', show_shapes=True)
...@@ -99,14 +97,8 @@ generator_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay= ...@@ -99,14 +97,8 @@ generator_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=
utils.make_trainable(critic, True) utils.make_trainable(critic, True)
utils.make_trainable(generator, False) utils.make_trainable(generator, False)
generator_in_critic_training = Input(shape=(latent_size,), name="noise")
shower_in_critic_training = Input(shape=(9,9,1), name='shower_maps') critic_training, averaged_batch = utils.build_critic_graph(generator, critic, latent_size, batch_size=BATCH_SIZE)
generator_out_critic_training = generator(generator_in_critic_training)
out_critic_training_gen = critic(generator_out_critic_training)
out_critic_training_shower = critic(shower_in_critic_training)
averaged_batch = RandomWeightedAverage(name='Average')([generator_out_critic_training, shower_in_critic_training])
averaged_batch_out = critic(averaged_batch)
critic_training = Model(inputs=[generator_in_critic_training, shower_in_critic_training], outputs=[out_critic_training_gen, out_critic_training_shower, averaged_batch_out])
gradient_penalty = partial(utils.gradient_penalty_loss, averaged_batch=averaged_batch, penalty_weight=GRADIENT_PENALTY_WEIGHT) gradient_penalty = partial(utils.gradient_penalty_loss, averaged_batch=averaged_batch, penalty_weight=GRADIENT_PENALTY_WEIGHT)
gradient_penalty.__name__ = 'gradient_penalty' gradient_penalty.__name__ = 'gradient_penalty'
critic_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0), loss=[utils.wasserstein_loss, utils.wasserstein_loss, gradient_penalty]) critic_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0), loss=[utils.wasserstein_loss, utils.wasserstein_loss, gradient_penalty])
...@@ -115,7 +107,7 @@ critic_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0 ...@@ -115,7 +107,7 @@ critic_training.compile(optimizer=Adam(0.0001, beta_1=0.5, beta_2=0.9, decay=0.0
# For Wassersteinloss # For Wassersteinloss
positive_y = np.ones(BATCH_SIZE) positive_y = np.ones(BATCH_SIZE)
negative_y = -positive_y negative_y = -positive_y
dummy = np.zeros(BATCH_SIZE) # keras throws an error when calculating a loss withuot having a label -> needed for using the gradient penalty loss dummy = np.zeros(BATCH_SIZE) # keras throws an error when calculating a loss without having a label -> needed for using the gradient penalty loss
generator_loss = [] generator_loss = []
critic_loss = [] critic_loss = []
...@@ -136,9 +128,8 @@ for epoch in range(EPOCHS): ...@@ -136,9 +128,8 @@ for epoch in range(EPOCHS):
generator_loss.append(generator_training.train_on_batch([noise_batch], [positive_y])) generator_loss.append(generator_training.train_on_batch([noise_batch], [positive_y]))
print "generator loss:", generator_loss[-1] print "generator loss:", generator_loss[-1]
utils.plot_loss(critic_loss, name="critic", iterations_per_epoch=iterations_per_epoch, NCR=NCR, log_dir=log_dir, bins_per_epoch=3) utils.plot_loss(critic_loss, name="critic", log_dir=log_dir)
utils.plot_loss(generator_loss, name="generator", iterations_per_epoch=iterations_per_epoch, log_dir=log_dir, bins_per_epoch=3) utils.plot_loss(generator_loss, name="generator",log_dir=log_dir)
# plot some generated figures # plot some generated figures
generated_map = generator.predict(np.random.randn(BATCH_SIZE, latent_size)) generated_map = generator.predict(np.random.randn(BATCH_SIZE, latent_size))
......
AirShower_WGAN/utils.py
+ 25
− 146
View file @ 76756103
...@@ -8,13 +8,15 @@ import matplotlib ...@@ -8,13 +8,15 @@ import matplotlib
matplotlib.use('Agg') matplotlib.use('Agg')
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
def get_session(gpu_fraction=0.40): def get_session(gpu_fraction=0.40):
''' Allocate only a fraction of the GPU RAM - (1080 GTX 8Gb)''' ''' Allocate only a fraction of the GPU RAM - (1080 GTX 8Gb). Please do not change the fraction during the tutorial!'''
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
def ReadInData(filenames): def ReadInData(filenames):
'''Reads in the trainings data'''
N = 100000 *len(filenames) N = 100000 *len(filenames)
a = 100000 a = 100000
shower_maps = np.zeros(N*9*9*1).reshape(N,9,9,1) shower_maps = np.zeros(N*9*9*1).reshape(N,9,9,1)
...@@ -26,36 +28,8 @@ def ReadInData(filenames): ...@@ -26,36 +28,8 @@ def ReadInData(filenames):
return shower_maps, Energy return shower_maps, Energy
def ang2vec(phi, zenith):
""" Get 3-vector from spherical angles.
Args:
phi (array): azimuth (pi, -pi), 0 points in x-direction, pi/2 in y-direction
zenith (array): zenith (0, pi), 0 points in z-direction
Returns:
array of 3-vectors
"""
x = np.sin(zenith) * np.cos(phi)
y = np.sin(zenith) * np.sin(phi)
z = np.cos(zenith)
return np.array([x, y, z]).T
def vec2ang(v):
'''Calculates phi and theta for given shower axis'''
x, y, z = np.asarray(v).T
phi = np.arctan2(y, x)
theta = np.pi/2 - np.arctan2(z, (x**2 + y**2)**.5)
return np.rad2deg(phi), np.rad2deg(theta)
def DenselyConnectedConv(z, nfilter):
''' Densely Connected SeparableConvolution2D Layer'''
c = Conv2D(nfilter, (3, 3), padding = 'same', activation='relu', kernel_initializer='he_normal')(z)
return concatenate([z, c], axis=-1)
def make_trainable(model, trainable): def make_trainable(model, trainable):
''' Freezes/unfreezes the weights in the given model ''' ''' Freezes/unfreezes the weights in the given model for each layer'''
for layer in model.layers: for layer in model.layers:
model.trainable=trainable model.trainable=trainable
model.trainable=trainable model.trainable=trainable
...@@ -69,14 +43,13 @@ def rectangular_array(n=9): ...@@ -69,14 +43,13 @@ def rectangular_array(n=9):
def wasserstein_loss(y_true, y_pred): def wasserstein_loss(y_true, y_pred):
"""Calculates the Wasserstein loss - critic maximises the distance between its output for real and generated samples. """Calculates the Wasserstein loss - critic maximises the distance between its output for real and generated samples.
To achieve this generated samples have the label -1 and real samples the label 1. Multiplying the outputs by the labels results to the wasserstein loss""" To achieve this generated samples have the label -1 and real samples the label 1. Multiplying the outputs by the labels results to the wasserstein loss via the Kantorovich-Rubinstein duality"""
return K.mean(y_true * y_pred) return K.mean(y_true * y_pred)
def gradient_penalty_loss(y_true, y_pred, averaged_batch, penalty_weight): def gradient_penalty_loss(y_true, y_pred, averaged_batch, penalty_weight):
"""Calculates the gradient penalty (for details see arXiv:1704.00028v3). """Calculates the gradient penalty (for details see arXiv:1704.00028v3).
The 1-Lipschitz constraint for improved WGANs is enforced by adding a term which penalizes if the gradient norm in the critic unequal to 1. The 1-Lipschitz constraint for improved WGANs is enforced by adding a term to the loss which penalizes if the gradient norm in the critic unequal to 1"""
(With this loss we penalize gradients with respect to the input of the averaged samples.)"""
gradients = K.gradients(K.sum(y_pred), averaged_batch) gradients = K.gradients(K.sum(y_pred), averaged_batch)
gradient_l2_norm = K.sqrt(K.sum(K.square(gradients))) gradient_l2_norm = K.sqrt(K.sum(K.square(gradients)))
gradient_penalty = penalty_weight * K.square(1 - gradient_l2_norm) gradient_penalty = penalty_weight * K.square(1 - gradient_l2_norm)
...@@ -94,103 +67,43 @@ class RandomWeightedAverage(_Merge): ...@@ -94,103 +67,43 @@ class RandomWeightedAverage(_Merge):
return (weights * inputs[0]) + ((1 - weights) * inputs[1]) return (weights * inputs[0]) + ((1 - weights) * inputs[1])
def build_generator_graph(generator, critic, conditioner, latent_size, energy_in=1): def build_generator_graph(generator, critic, latent_size):
'''Builds the graph for training the generator part of the improved WGAN'''
generator_in = Input(shape=(latent_size,)) generator_in = Input(shape=(latent_size,))
generator_in_energy_label = Input(shape=(energy_in,)) generator_out = generator([generator_in])
generator_out = generator([generator_in, generator_in_energy_label])
critic_out = critic(generator_out) critic_out = critic(generator_out)
energy_rec_out = conditioner(generator_out) return Model(inputs=[generator_in], outputs=[critic_out])
return Model(inputs=[generator_in, generator_in_energy_label], outputs=[critic_out, energy_rec_out])
def build_critic_graph(generator, critic, conditioner, latent_size, energy_in, batch_size=1): def build_critic_graph(generator, critic, latent_size, batch_size=1):
'''Builds the graph for training the critic part of the improved WGAN'''
generator_in_critic_training = Input(shape=(latent_size,), name="noise") generator_in_critic_training = Input(shape=(latent_size,), name="noise")
generator_in_critic_training_energy_label = Input(shape=(energy_in,), name="energy_label")
shower_in_critic_training = Input(shape=(9,9,1), name='shower_maps') shower_in_critic_training = Input(shape=(9,9,1), name='shower_maps')
generator_out_critic_training = generator([generator_in_critic_training, generator_in_critic_training_energy_label]) generator_out_critic_training = generator([generator_in_critic_training])
out_critic_training_gen = critic(generator_out_critic_training) out_critic_training_gen = critic(generator_out_critic_training)
out_critic_training_shower = critic(shower_in_critic_training) out_critic_training_shower = critic(shower_in_critic_training)
averaged_batch = RandomWeightedAverage(batch_size, name='Average')([generator_out_critic_training, shower_in_critic_training]) averaged_batch = RandomWeightedAverage(batch_size, name='Average')([generator_out_critic_training, shower_in_critic_training])
averaged_batch_out = critic(averaged_batch) averaged_batch_out = critic(averaged_batch)
rec_out = conditioner(shower_in_critic_training) return Model(inputs=[generator_in_critic_training, shower_in_critic_training], outputs=[out_critic_training_gen, out_critic_training_shower, averaged_batch_out]), averaged_batch
return Model(inputs=[generator_in_critic_training, shower_in_critic_training, generator_in_critic_training_energy_label], outputs=[out_critic_training_gen, out_critic_training_shower, averaged_batch_out, rec_out]), averaged_batch
def plot_loss_wasserstein(loss, log_dir = ".", name="", iterations_per_epoch=10, NCR=1, Rec_scaling=1):
fig, ax1 = plt.subplots(1, figsize=(10,5))
epoch = np.arange(len(loss))
loss = np.array(loss)
plt.plot(epoch, loss[:,1] + loss[:,2] + loss[:,3], color='red', markersize=12, label=r'$Wasserstein$')
plt.legend(loc='upper right',prop={'size':10})
ax1.set_xlabel(r'$Iterations$')
ax1.set_ylabel(r'$Loss$')
ax1.set_ylim(-2, 2)
fig.savefig(log_dir + '/%s_Loss_wasserstein.png' %name, dpi=480)
plt.plot(epoch, loss[:,4]*Rec_scaling, color='orange', markersize=12, label=r'$Reconstruction$')
plt.close('all')
def plot_loss_reco(loss, log_dir = ".", name="", iterations_per_epoch=10, NCR=1, Rec_scaling=1):
fig, ax1 = plt.subplots(1, figsize=(10,4))
epoch = np.arange(len(loss))
loss = np.array(loss)
plt.plot(epoch, loss[:,2], color='orange', markersize=12, label=r'$Reconstruction$')
plt.legend(loc='upper right',prop={'size':10})
ax1.set_xlabel(r'$Iterations$')
ax1.set_ylabel(r'$Loss$')
ax1.set_yscale("log", nonposy='clip')
fig.savefig(log_dir + '/%s_Loss_reco.png' %name, dpi=480)
plt.close('all')
def plot_loss(loss, log_dir = ".", name="", iterations_per_epoch=10, NCR=1, Rec_scaling=1): def plot_loss(loss, log_dir = ".", name=""):
"""Plot the traings curve"""
fig, ax1 = plt.subplots(1, figsize=(10,4)) fig, ax1 = plt.subplots(1, figsize=(10,4))
epoch = np.arange(len(loss)) epoch = np.arange(len(loss))
loss = np.array(loss) loss = np.array(loss)
try: try:
plt.plot(epoch, loss[:,0], color='red', markersize=12, label=r'$Total$') plt.plot(epoch, loss[:,0], color='red', markersize=12, label=r'Total')
plt.plot(epoch, loss[:,1] + loss[:,2], color='green', label=r'$Wasserstein$', linestyle='dashed') plt.plot(epoch, loss[:,1] + loss[:,2], color='green', label=r'Wasserstein', linestyle='dashed')
plt.plot(epoch, loss[:,3], color='royalblue', markersize=12, label=r'$Gradient$', linestyle='dashed') plt.plot(epoch, loss[:,3], color='royalblue', markersize=12, label=r'GradientPenalty', linestyle='dashed')
plt.plot(epoch, Rec_scaling*loss[:,4], color='orange', markersize=12, label=r'$Reconstrucion$', linestyle='dashed')
except: except:
plt.plot(epoch, loss[:,0], color='red', markersize=12, label=r'$Total$') plt.plot(epoch, loss[:], color='red', markersize=12, label=r'Total')
plt.legend(loc='upper right',prop={'size':10}) plt.legend(loc='upper right',prop={'size':10})
ax1.set_xlabel(r'$Iterations$') ax1.set_xlabel(r'Iterations')
ax1.set_ylabel(r'$Cost \Theta (MSE)$') ax1.set_ylabel(r'Loss')
ax1.set_ylim(-2, 2) ax1.set_ylim(-1, 3)
fig.savefig(log_dir + '/%s_Loss.png' %name, dpi=480) fig.savefig(log_dir + '/%s_Loss.png' %name, dpi=120)
plt.close('all')
def plot_array_scatter(values, label, axis, vmin=0):
"""Plot a map *values* for an detector array specified by *v_stations*. """
xd, yd = rectangular_array()
filter = values!=0
filter[5,5] = True
axis.scatter(xd[~filter], yd[~filter], c = 'grey', s = 220, alpha=0.1, label="silent")
circles = axis.scatter(xd[filter], yd[filter], c = values[filter], s = 200, alpha=1, label="loud", vmin=vmin)
cbar = plt.colorbar(circles, ax=axis)
cbar.set_label(label + ' [a.u.]')
axis.grid(True)
axis.set_title(label + ' footprint', fontsize=10)
axis.set_aspect('equal')
axis.set_xlim(-5, 5)
axis.set_ylim(-5, 5)
axis.set_xlabel('x [km]')
axis.set_ylabel('y [km]')
def plot_showermaps(footprint, log_dir='.', title='', epoch=''):
""" Plots the time and signal footprint in one figure """
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(10,4))
fig.subplots_adjust(left=0, bottom=0.13)
plot_array_scatter(footprint[:,:,0], label = 'time', axis=ax1, vmin=1)
plot_array_scatter(footprint[:,:,1], label = 'signal', axis=ax2)
plt.suptitle(title + ' ' + str(epoch), fontsize=16)
plt.tight_layout()
plt.legend(bbox_to_anchor=(0.59, 0.12, 0, 0.0), bbox_transform=plt.gcf().transFigure, ncol=2)
fig.savefig(log_dir + '/%s_shower_map.png' %epoch, dpi=140)
plt.close('all') plt.close('all')
...@@ -229,39 +142,5 @@ def plot_multiple_signalmaps(footprint, log_dir='.', title='', epoch='', nrows=2 ...@@ -229,39 +142,5 @@ def plot_multiple_signalmaps(footprint, log_dir='.', title='', epoch='', nrows=2
plt.tight_layout() plt.tight_layout()
fig.subplots_adjust(left=0.02, top=0.95) fig.subplots_adjust(left=0.02, top=0.95)
plt.suptitle(title + ' ' + str(epoch), fontsize=12) plt.suptitle(title + ' ' + str(epoch), fontsize=12)
fig.savefig(log_dir + '/%s_signal_maps.png' %epoch, dpi=140) fig.savefig(log_dir + '/%s_signal_maps.png' %epoch, dpi=120)
plt.close('all') plt.close('all')
def plot_energy_histos(y_true, y_pred, log_dir, name):
''' Plottingfunction to evaluate the reconstruction of the cosmic ray energy
plots: predicted Energy against MC Energy and the energy dependence of the relative energy resolution (embedded)'''
y_true = y_true.reshape(y_true.shape[0],1)
reco = y_true-y_pred
# Embedding plot
fig, ax1 = plt.subplots(1)
ax1.scatter(y_true, y_pred, s = 2, color='royalblue', alpha=.90)
ax1.set_xlabel(r'$Energy_{Input}\;[EeV]$')
ax1.set_ylabel(r'$Energy_{DNN}\;[EeV]$')
left, bottom, width, height = [0.23, 0.6, 0.3, 0.26] # Links Oben
ax1.text(0.95,0.2, r"$\mu = $ %.3f" % np.mean(reco) + " [EeV]" + "\n" + "$\sigma = $ %.3f" % np.std(reco) + " [EeV]", verticalalignment = 'top', horizontalalignment = 'right', transform=ax1.transAxes, backgroundcolor='w')
ax1.plot([np.min(y_true), np.max(y_true)], [np.min(y_true), np.max(y_true)], color='red')
fig.savefig(log_dir + '/%s_Energy_reconstruction.png' %name, dpi=140)
return reco
def plot_energy_histos_real(y_true, y_pred, log_dir, name):
''' Plottingfunction to evaluate the reconstruction of the cosmic ray energy
plots: predicted Energy against MC Energy and the energy dependence of the relative energy resolution (embedded)'''
y_true = y_true.reshape(y_true.shape[0],1)
reco = y_true-y_pred
# Embedding plot
fig, ax1 = plt.subplots(1)
ax1.scatter(y_true, y_pred, s = 2, color='royalblue', alpha=.90)
ax1.set_xlabel(r'$Energy_{MC}\;[EeV]$')
ax1.set_ylabel(r'$Energy_{DNN}\;[EeV]$')
left, bottom, width, height = [0.23, 0.6, 0.3, 0.26] # Links Oben
ax1.text(0.95,0.2, r"$\mu = $ %.3f" % np.mean(reco) + " [EeV]" + "\n" + "$\sigma = $ %.3f" % np.std(reco) + " [EeV]", verticalalignment = 'top', horizontalalignment = 'right', transform=ax1.transAxes, backgroundcolor='w')
ax1.plot([np.min(y_true), np.max(y_true)], [np.min(y_true), np.max(y_true)], color='red')
fig.savefig(log_dir + '/%s_Energy_reconstruction.png' %name, dpi=140)
return reco
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