import numpy as np
import os
import math
import tensorflow as tf
import matplotlib.pyplot as plt
from datetime import datetime
from architectures.utils.NN_building_blocks import *
from architectures.utils.NN_gen_building_blocks import *
# from architectures.utils.arbitrary_act import *
def lrelu(x, alpha=0.001):
return tf.maximum(alpha*x,x)
def shifted_lrelu(x, max_val, alpha=0.001):
return tf.minimum(max_val+alpha*x,x)
def tf_HCAL_act(x, mask, max_val_out=6120., max_val_in=1530.):
output = tf.where(mask,
lrelu(shifted_lrelu(x, max_val=max_val_out)),
lrelu(shifted_lrelu(x, max_val=max_val_in)),
)
return output
# def tf_HCAL_act(x, mask, max_val_out=6120., max_val_in=1530.):
# output = tf.where(mask,
# tf.maximum(0.,tf.minimum(max_val_out,x)) ,
# tf.maximum(0.,tf.minimum(max_val_in,x))
# )
# return output
pretrain=None
# some dummy constants
LEARNING_RATE = None
BETA1 = None
COST_TYPE=None
BATCH_SIZE = None
EPOCHS = None
SAVE_SAMPLE_PERIOD = None
PATH = None
SEED = None
rnd_seed=1
LAMBDA=.01
EPS=1e-6
CYCL_WEIGHT=None
LATENT_WEIGHT=None
KL_WEIGHT=None
DISCR_STEPS=None
GEN_STEPS=None
VAE_STEPS=None
min_true=None
max_true=None
min_reco=None
max_reco=None
n_H_A=None
n_W_A=None
n_W_B=None
n_H_B=None
n_C=None
d_sizes=None
g_sizes_enc=None
g_sizes_dec=None
e_sizes=None
class bicycle_GAN(object):
#fix args
def __init__(
self,
n_H_A=n_H_A, n_W_A=n_W_A,
n_H_B=n_H_B, n_W_B=n_W_B, n_C=n_C,
min_true=min_true, max_true=max_true,
min_reco=min_reco, max_reco=max_reco,
d_sizes=d_sizes, g_sizes_enc=g_sizes_enc, g_sizes_dec=g_sizes_dec, e_sizes=e_sizes,
lr=LEARNING_RATE, beta1=BETA1,
cost_type=COST_TYPE, cycl_weight=CYCL_WEIGHT, latent_weight=LATENT_WEIGHT, kl_weight=KL_WEIGHT,
discr_steps=DISCR_STEPS, gen_steps=GEN_STEPS, vae_steps=VAE_STEPS,
batch_size=BATCH_SIZE, epochs=EPOCHS,
save_sample=SAVE_SAMPLE_PERIOD, path=PATH, seed=SEED,
):
"""
Positional arguments:
- width of (square) image
- number of channels of input image
- discriminator sizes
a python dict of the kind
d_sizes = { 'convblocklayer_n':[(n_c+1, kernel, stride, apply_batch_norm, weight initializer),
(,,,,),
(,,,,),
],
'convblock_shortcut_layer_n':[(,,,)],
'dense_layers':[(n_o, apply_bn, weight_init)]
}
- generator sizes
a python dictionary of the kind
g_sizes = {
'z':latent_space_dim,
'projection': int,
'bn_after_project':bool
'deconvblocklayer_n':[(n_c+1, kernel, stride, apply_batch_norm, weight initializer),
(,,,,),
(,,,,),
],
'deconvblock_shortcut_layer_n':[(,,,)],
'dense_layers':[(n_o, apply_bn, weight_init)]
'activation':function
}
Keyword arguments:
- lr = LEARNING_RATE (float32)
- beta1 = ema parameter for adam opt (float32)
- batch_size (int)
- save_sample = after how many batches iterations to save a sample (int)
- path = relative path for saving samples
"""
latent_dims=e_sizes['latent_dims']
self.min_true=min_true
self.max_true=max_true
self.min_reco=min_reco
self.max_reco=max_reco
self.seed=seed
self.n_W_A = n_W_A
self.n_H_A = n_H_A
self.n_W_B = n_W_B
self.n_H_B = n_H_B
self.n_C = n_C
#input data
self.input_A = tf.placeholder(
tf.float32,
shape=(None,
n_H_A, n_W_A, n_C),
name='X_A',
)
self.input_B = tf.placeholder(
tf.float32,
shape=(None,
n_H_B, n_W_B, n_C),
name='X_B',
)
self.batch_sz = tf.placeholder(
tf.int32,
shape=(),
name='batch_sz'
)
self.lr = tf.placeholder(
tf.float32,
shape=(),
name='lr'
)
self.z = tf.placeholder(
tf.float32,
shape=(None,
latent_dims)
)
self.input_test_A = tf.placeholder(
tf.float32,
shape=(None,
n_H_A, n_W_A, n_C),
name='X_test_A',
)
self.mask = tf.placeholder(
tf.bool,
shape=(None,
n_H_A, n_W_A, n_C),
name='inner_outer',
)
G = bicycleGenerator(self.input_A, self.n_H_B, self.n_W_B, g_sizes_enc, g_sizes_dec, 'A_to_B')
D = Discriminator_minibatch(self.input_B, d_sizes, 'B')
#D = Discriminator(self.input_B, d_sizes, 'B')
E = convEncoder(self.input_B, e_sizes, 'B')
with tf.variable_scope('encoder_B') as scope:
z_encoded, z_encoded_mu, z_encoded_log_sigma = E.e_forward(self.input_B)
with tf.variable_scope('generator_A_to_B') as scope:
sample_A_to_B_encoded = tf_HCAL_act(G.g_forward(self.input_A, z_encoded), self.mask)
with tf.variable_scope('generator_A_to_B') as scope:
scope.reuse_variables()
sample_A_to_B = self.sample_A_to_B = tf_HCAL_act(G.g_forward(self.input_A, self.z, reuse=True), self.mask)
with tf.variable_scope('encoder_B') as scope:
scope.reuse_variables()
z_recon, z_recon_mu, z_recon_log_sigma = E.e_forward(sample_A_to_B, reuse=True)
with tf.variable_scope('discriminator_B') as scope:
logits_real, feature_output_real = D.d_forward(self.input_B)
with tf.variable_scope('discriminator_B') as scope:
scope.reuse_variables()
logits_fake, feature_output_fake = D.d_forward(sample_A_to_B, reuse=True)
logits_fake_encoded, feature_output_fake_encoded = D.d_forward(sample_A_to_B_encoded, reuse=True)
with tf.variable_scope('generator_A_to_B') as scope:
scope.reuse_variables()
self.test_images_A_to_B = tf_HCAL_act(G.g_forward(
self.input_test_A, self.z, reuse=True, is_training=False
),self.mask)
#parameters lists
self.d_params =[t for t in tf.trainable_variables() if 'discriminator' in t.name]
self.e_params =[t for t in tf.trainable_variables() if 'encoder' in t.name]
self.g_params =[t for t in tf.trainable_variables() if 'generator' in t.name]
predicted_real= tf.nn.sigmoid(logits_real)
#predicted_real=tf.maximum(tf.minimum(predicted_real, 0.99), 0.00)
predicted_fake=tf.nn.sigmoid(logits_fake)
#predicted_fake=tf.maximum(tf.minimum(predicted_fake, 0.99), 0.00)
predicted_fake_encoded = tf.nn.sigmoid(logits_fake_encoded)
#predicted_fake_encoded =tf.maximum(tf.minimum(predicted_fake_encoded, 0.99), 0.00)
epsilon=1e-2
#GAN LOSS
if cost_type=='GAN':
#DISCRIMINATOR LOSSES
self.d_cost_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_real,
labels=(1-epsilon)*tf.ones_like(logits_real)
)
)
self.d_cost_fake_lr_GAN = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake,
labels=epsilon+tf.zeros_like(logits_fake)
)
)
self.d_cost_fake_vae_GAN = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake_encoded,
labels=epsilon+tf.zeros_like(logits_fake_encoded)
)
)
#GENERATOR LOSSES
self.g_cost_lr_GAN = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake,
labels=(1-epsilon)*tf.ones_like(logits_fake)
)
)
self.g_cost_vae_GAN = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake_encoded,
labels=(1-epsilon)*tf.ones_like(logits_fake_encoded)
)
)
if cost_type=='WGAN':
#DISCRIMINATOR
self.d_cost_real= -tf.reduce_mean(logits_real)
self.d_cost_fake_vae_GAN = tf.reduce_mean(logits_fake_encoded)
self.d_cost_fake_lr_GAN = tf.reduce_mean(logits_fake)
#GP
epsilon= tf.random_uniform(
[self.batch_sz, 1, 1, 1],
minval=0.,
maxval=1.,
)
interpolated = epsilon*self.input_A + (1-epsilon/2)*sample_A_to_B + (1-epsilon/2)*sample_A_to_B_encoded
with tf.variable_scope('discriminator_B') as scope:
scope.reuse_variables()
logits_interpolated= D.d_forward(self.input_A, interpolated, reuse=True)
gradients = tf.gradients(logits_interpolated, [interpolated], name='D_logits_intp')[0]
grad_l2= tf.sqrt(tf.reduce_sum(tf.square(gradients), axis=[1,2,3]))
self.grad_penalty=tf.reduce_mean(tf.square(grad_l2-1.0))
#GENERATOR
self.g_cost_vae_GAN= - tf.reduce_mean(logits_fake_encoded)
self.g_cost_lr_GAN= - tf.reduce_mean(logits_fake)
if cost_type=='FEATURE':
#DISCRIMINATOR LOSSES
self.d_cost_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_real,
labels=(1-epsilon)*tf.ones_like(logits_real)
)
)
self.d_cost_fake_lr_GAN = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake,
labels=epsilon+tf.zeros_like(logits_fake)
)
)
self.d_cost_fake_vae_GAN = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake_encoded,
labels=epsilon+tf.zeros_like(logits_fake_encoded)
)
)
#GENERATOR LOSSES
g_cost_lr_GAN = tf.reduce_sum(tf.squared_difference(feature_output_real,feature_output_fake), axis=1)
self.g_cost_lr_GAN = tf.reduce_mean(g_cost_lr_GAN)
g_cost_vae_GAN = tf.reduce_sum(tf.squared_difference(feature_output_real,feature_output_fake_encoded), axis=1)
self.g_cost_vae_GAN=tf.reduce_mean(g_cost_vae_GAN)
#CYCLIC WEIGHT
g_cost_cycl = tf.reduce_mean(tf.abs(self.input_B - sample_A_to_B_encoded), axis=[1,2,3])
self.g_cost_cycl = tf.reduce_mean(g_cost_cycl)
# self.g_4_cells_cycl_encoded=tf.reduce_mean(
# tf.abs(
# tf.cast(
# tf.convert_to_tensor(
# [
# tf.nn.top_k(
# tf.reshape(
# self.input_B[i],
# [-1]),
# k=4)[0] -
# tf.nn.top_k(
# tf.reshape(
# sample_A_to_B_encoded[i],
# [-1]),
# k=4)[0]
# for i in range(16)]
# )
# ,dtype=tf.float32)
# )
# )
# self.g_4_cells_pos_encoded=tf.reduce_mean(tf.abs(tf.cast(tf.convert_to_tensor(
# [
# tf.nn.top_k(
# tf.reshape(
# self.input_B[i],
# [-1]),
# k=4)[1] -
# tf.nn.top_k(
# tf.reshape(
# sample_A_to_B_encoded[i],
# [-1]),
# k=4)[1]
# for i in range(16)]
# ), dtype=tf.float32)
# )
# )
#ENCODER COSTS
e_cost_latent_cycle = tf.reduce_mean(tf.abs(self.z - z_recon), axis=1)
self.e_cost_latent_cycle=tf.reduce_mean(e_cost_latent_cycle)
self.e_cost_kl = 0.5 * tf.reduce_mean(-1 + z_encoded_mu ** 2 + z_encoded_log_sigma**2 - 2*tf.log(z_encoded_log_sigma))
#TOTAL COSTS
self.d_cost = self.d_cost_fake_vae_GAN + self.d_cost_fake_lr_GAN + 2*self.d_cost_real
self.g_cost = self.g_cost_vae_GAN + self.g_cost_lr_GAN + cycl_weight*self.g_cost_cycl + latent_weight*self.e_cost_latent_cycle
self.e_cost = kl_weight*self.e_cost_kl + self.g_cost_vae_GAN + cycl_weight*self.g_cost_cycl + latent_weight*self.e_cost_latent_cycle
self.d_train_op = tf.train.AdamOptimizer(
learning_rate=lr,
beta1=beta1,
).minimize(
self.d_cost,
var_list=self.d_params
)
self.g_train_op = tf.train.AdamOptimizer(
learning_rate=lr,
beta1=beta1,
).minimize(
self.g_cost,
var_list=self.g_params
)
self.e_train_op = tf.train.AdamOptimizer(
learning_rate=lr,
beta1=beta1,
).minimize(
self.e_cost,
var_list=self.e_params
)
real_predictions = tf.cast(logits_real>0.5,tf.float32)
fake_predictions = tf.cast(logits_fake<0.5,tf.float32)
fake_enc_predictions = tf.cast(logits_fake_encoded<0.5,tf.float32)
num_predictions=2.0*batch_size
num_correct = tf.reduce_sum(real_predictions)+tf.reduce_sum(fake_predictions)
num_correct_enc = tf.reduce_sum(real_predictions)+tf.reduce_sum(fake_enc_predictions)
self.d_accuracy= num_correct/num_predictions
self.d_accuracy_enc= num_correct_enc/num_predictions
self.D=D
self.G=G
self.E=E
self.latent_weight=latent_weight
self.cycl_weight=cycl_weight
self.kl_weight=kl_weight
self.latent_dims=latent_dims
self.batch_size=batch_size
self.epochs=epochs
self.save_sample=save_sample
self.path=path
self.lr = lr
self.cost_type=cost_type
self.gen_steps=gen_steps
self.vae_steps=vae_steps
self.discr_steps=discr_steps
def set_session(self, session):
self.session = session
for layer in self.D.d_conv_layers:
layer.set_session(session)
for layer in self.G.g_enc_conv_layers:
layer.set_session(session)
for layer in self.G.g_dec_conv_layers:
layer.set_session(session)
for layer in self.E.e_conv_layers:
layer.set_session(session)
for layer in self.E.e_dense_layers:
layer.set_session(session)
def fit(self, X_A, X_B, validating_size):
all_A = X_A
all_B = X_B
gen_steps=self.gen_steps
discr_steps=self.discr_steps
vae_steps = self.vae_steps
m = X_A.shape[0]
train_A = all_A[0:m-validating_size]
train_B = all_B[0:m-validating_size]
validating_A = all_A[m-validating_size:m]
validating_B = all_B[m-validating_size:m]
seed=self.seed
d_costs=[]
d_costs_vae_GAN=[]
d_costs_lr_GAN=[]
d_costs_GAN=[ ]
g_costs=[]
g_costs_lr_GAN=[]
g_costs_vae_GAN=[]
g_costs_cycl=[]
e_costs=[]
e_costs_kl=[]
e_costs_latent_cycle=[]
N=len(train_A)
n_batches = N // self.batch_size
total_iters=0
print('\n ****** \n')
print('Training bicycleGAN with a total of ' +str(N)+' samples distributed in '+ str((N)//self.batch_size) +' batches of size '+str(self.batch_size)+'\n')
print('The validation set consists of {0} images'.format(validating_A.shape[0]))
print('The learning rate is '+str(self.lr)+', and every ' +str(self.save_sample)+ ' batches a generated sample will be saved to '+ self.path)
print('\n ****** \n')
for epoch in range(self.epochs):
seed+=1
print('Epoch:', epoch)
batches_A = unsupervised_random_mini_batches(train_A, self.batch_size, seed)
batches_B = unsupervised_random_mini_batches(train_B, self.batch_size, seed)
for X_batch_A, X_batch_B in zip(batches_A[:-1], batches_B[:-1]):
bs=X_batch_A.shape[0]
in_out_mask_zeros = np.zeros([bs, self.n_H_A, self.n_W_A, self.n_C])
in_out_mask_zeros[:, 12:40, 16:48, :]=1
in_out_mask = in_out_mask_zeros.astype(dtype=bool)
t0 = datetime.now()
e_cost=0
e_cost_latent_cycle=0
e_cost_kl=0
g_cost=0
g_cost_cycl=0
g_cost_lr_GAN=0
g_cost_vae_GAN=0
#cluster_diff=0
d_cost=0
d_cost_vae_GAN=0
d_cost_lr_GAN=0
sample_z = np.random.normal(size=(bs, self.latent_dims))
for i in range(discr_steps):
#sample_z = np.random.normal(size=(bs, self.latent_dims))
_, d_acc, d_acc_enc, d_cost, d_cost_vae_GAN, d_cost_lr_GAN = self.session.run(
(self.d_train_op, self.d_accuracy, self.d_accuracy_enc, self.d_cost, self.d_cost_fake_vae_GAN, self.d_cost_fake_lr_GAN),
feed_dict={self.input_A:X_batch_A, self.input_B:X_batch_B,
self.z:sample_z, self.mask:in_out_mask, self.batch_sz:bs
},
)
d_cost+=d_cost
d_cost_vae_GAN+=d_cost_vae_GAN
d_cost_lr_GAN+=d_cost_lr_GAN
d_costs.append(d_cost/discr_steps)
d_costs_vae_GAN.append(d_cost_vae_GAN/discr_steps)
d_costs_lr_GAN.append(d_cost_lr_GAN/discr_steps)
for i in range(gen_steps):
#sample_z = np.random.normal(size=(bs, self.latent_dims))
_, g_cost, g_cost_cycl, g_cost_lr_GAN, g_cost_vae_GAN = self.session.run(
(self.g_train_op, self.g_cost,
self.g_cost_cycl, self.g_cost_lr_GAN, self.g_cost_vae_GAN,
),
feed_dict={self.input_A:X_batch_A, self.input_B:X_batch_B,
self.z:sample_z, self.mask:in_out_mask, self.batch_sz:bs
},
)
g_cost+=g_cost
g_cost_cycl+=g_cost_cycl
g_cost_lr_GAN+=g_cost_lr_GAN
g_cost_vae_GAN+=g_cost_vae_GAN
#cluster_diff+=cluster_diff
g_costs.append(g_cost/gen_steps)
g_costs_vae_GAN.append(g_cost_vae_GAN/gen_steps)
g_costs_lr_GAN.append(g_cost_lr_GAN/gen_steps)
g_costs_cycl.append(self.cycl_weight*g_cost_cycl/gen_steps)
#cluster_diffs.append(self.cycl_weight*cluster_diff/gen_steps)
for i in range(vae_steps):
#sample_z = np.random.normal(size=(bs, self.latent_dims))
_, e_cost, e_cost_latent_cycle, e_cost_kl = self.session.run(
(self.e_train_op, self.e_cost,
self.e_cost_latent_cycle, self.e_cost_kl
),
feed_dict={self.input_A:X_batch_A, self.input_B:X_batch_B,
self.z:sample_z, self.mask:in_out_mask, self.batch_sz:bs
},
)
e_cost+=e_cost
e_cost_latent_cycle+=e_cost_latent_cycle
e_cost_kl+=e_cost_kl
#cluster_diff+=cluster_diff
e_costs.append(e_cost/vae_steps)
e_costs_latent_cycle.append(self.latent_weight*e_cost_latent_cycle/vae_steps)
e_costs_kl.append(self.kl_weight*e_cost_kl/vae_steps)
total_iters+=1
if total_iters % self.save_sample==0:
plt.clf()
print("At iter: %d - dt: %s - d_acc: %.2f, - d_acc_enc: %.2f" % (total_iters, datetime.now() - t0, d_acc, d_acc_enc))
print("Discriminator cost {0:.4g}, Generator cost {1:.4g}, VAE Cost {2:.4g}, KL divergence cost {3:.4g}".format(d_cost, g_cost, e_cost, e_cost_kl))
print('Saving a sample...')
draw_nn_sample(validating_A, validating_B, 1,
f=self.get_sample_A_to_B, is_training=True,
total_iters=total_iters, PATH=self.path)
plt.clf()
plt.subplot(2,4,1)
plt.plot(d_costs, label='Discriminator total cost')
plt.plot(d_costs_lr_GAN, label='Discriminator of image with encoded noise cost')
plt.plot(d_costs_vae_GAN, label='Discriminator of image with input noise cost')
plt.xlabel('Batch')
plt.ylabel('Cost')
plt.legend()
plt.subplot(2,4,2)
plt.plot(g_costs, label='Generator total cost')
plt.plot(g_costs_cycl, label='Generator cyclic cost')
#plt.plot(g_costs_GAN, label='GAN cost (encoded noise image)')
#plt.plot(g_costs_vae_GAN, label='GAN cost (input noise image)')
plt.xlabel('Batch')
plt.ylabel('Cost')
plt.legend()
plt.subplot(2,4,3)
plt.plot(e_costs, label='VAE cost')
#plt.plot(e_costs_kl, label='KL cost')
#plt.plot(e_costs_latent_cycle, label='Latent space cyclic cost')
plt.xlabel('Batch')
plt.ylabel('Cost')
plt.legend()
plt.subplot(2,4,6)
#plt.plot(g_costs, label='Generator cost')
#plt.plot(g_costs_cycl, label='Generator cyclic cost')
plt.plot(g_costs_lr_GAN, label='GAN cost (encoded noise image)')
plt.plot(g_costs_vae_GAN, label='GAN cost (input noise image)')
plt.xlabel('Batch')
plt.ylabel('Cost')
plt.legend()
plt.subplot(2,4,7)
plt.plot(e_costs_latent_cycle, label='Latent space cyclic cost')
plt.xlabel('Batch')
plt.ylabel('Cost')
plt.legend()
plt.subplot(2,4,8)
plt.plot(e_costs_kl, label='KL cost')
#plt.plot(e_costs_latent_cycle, label='Latent space cyclic cost')
plt.xlabel('Batch')
plt.ylabel('Cost')
plt.legend()
fig = plt.gcf()
fig.set_size_inches(15,7)
plt.savefig(self.path+'/cost_iteration.png',dpi=80)
print('Printing validation set histograms at epoch {0}'.format(epoch))
if not os.path.exists(self.path+'/epoch{0}/'.format(epoch)):
os.mkdir(self.path+'/epoch{0}/'.format(epoch))
validating_NN=np.zeros_like(validating_B)
for i in range(validating_size):
validating_NN[i]=self.get_sample_A_to_B(validating_A[i].reshape(1,self.n_H_A,self.n_W_A,self.n_C))
print('ET Distribution plots are being printed...')
validation_MC_hist= validating_B.reshape(validating_size, self.n_H_B*self.n_W_B)
validation_MC_hist = np.sum(validation_MC_hist,axis=1)
max_MC_hist = np.max(validation_MC_hist)
validation_NN_hist= validating_NN.reshape(validating_size, self.n_H_B*self.n_W_B)
validation_NN_hist = np.sum(validation_NN_hist,axis=1)
max_NN_hist = np.max(validation_NN_hist)
validation_true_hist= validating_A.reshape(validating_size, self.n_H_A*self.n_W_A)
validation_true_hist = np.sum(validation_true_hist,axis=1)
max_true_hist = np.max(validation_true_hist)
validation_NN_hist_rescaled=(validation_NN_hist/max_NN_hist)*max_MC_hist
plt.clf()
plt.subplot(1,3,1)
h_reco = plt.hist(validation_true_hist,bins=30, edgecolor='black');
plt.xlabel('E (MeV)')
plt.ylabel('dN/dE')
plt.title('True pion E_T distribution,\n max true hist: {0} '.format(max_true_hist))
plt.subplot(1,3,2)
h_reco = plt.hist(validation_MC_hist,bins=30, edgecolor='black');
plt.xlabel('E (MeV)')
plt.ylabel('dN/dE')
plt.title('Reco pion E_T distribution,\n max MC hist: {0} '.format(max_MC_hist))
plt.subplot(1,3,3)
h_nn = plt.hist(validation_NN_hist_rescaled,bins=30, edgecolor='black');
plt.xlabel('E (MeV)')
plt.ylabel('dN/dE')
plt.title('Reco pion E_T distribution from bicycleGAN, \n max NN hist: {0} '.format(max_NN_hist))
fig = plt.gcf()
fig.set_size_inches(16,4)
plt.savefig(self.path+'/epoch{0}/distribution_at_epoch_{1}.png'.format(epoch, epoch), dpi=80)
plt.clf()
diff=plt.bar(np.arange(0, max_MC_hist, step=max_MC_hist/30),
height=(h_nn[0]-h_reco[0]), edgecolor='black',
linewidth=1, color='lightblue',width = 1, align = 'edge')
plt.xlabel('E (GeV)')
plt.ylabel('dN/dE')
plt.title("ET distribution difference NN output - MC output")
fig = plt.gcf()
fig.set_size_inches(12,4)
plt.savefig(self.path+'/epoch{0}/difference_at_epoch_{1}.png'.format(epoch, epoch), dpi=80)
print('Done')
print('Resolution plots are being printed...')
diffNN = validation_NN_hist_rescaled-validation_true_hist
diffMC = validation_MC_hist-validation_true_hist
plt.clf()
plt.subplot(1,2,1)
h_reco = plt.hist(diffMC/1000,bins=30, range=(-80, 40), edgecolor='black');
plt.xlabel('ET recoMC - ET true')
plt.ylabel('dN/dETdiff')
plt.title('Resolution as simulated by MC')
plt.subplot(1,2,2)
h_nn = plt.hist(diffNN/1000,bins=30, range=(-80, 40), edgecolor='black');
plt.xlabel('ET recoNN - ET true')
plt.ylabel('dN/dETdiff')
plt.title('Resolution as simulated by NN')
fig = plt.gcf()
fig.set_size_inches(12,4)
plt.savefig(self.path+'/epoch{0}/resolution_at_epoch_{1}.png'.format(epoch, epoch), dpi=80)
print('Done')
def get_sample_A_to_B(self, X):
z = np.random.normal(size=(1, self.latent_dims))
in_out_mask_zeros = np.zeros([1, self.n_H_A, self.n_W_A, self.n_C])
in_out_mask_zeros[:, 12:40, 16:48, :]=1
in_out_mask = in_out_mask_zeros.astype(dtype=bool)
one_sample = self.session.run(
self.test_images_A_to_B,
feed_dict={self.input_test_A:X, self.z:z, self.mask:in_out_mask, self.batch_sz: 1})
return one_sample
def get_samples_A_to_B(self, X):
bs=X.shape[0]
z = np.random.normal(size=(bs, self.latent_dims))
in_out_mask_zeros = np.zeros([bs, self.n_H_A, self.n_W_A, self.n_C])
in_out_mask_zeros[:, 12:40, 16:48, :]=1
in_out_mask = in_out_mask_zeros.astype(dtype=bool)
many_samples = self.session.run(
self.test_images_A_to_B,
feed_dict={self.input_test_A:X, self.z:z, self.mask:in_out_mask, self.batch_sz: X.shape[0]})
return many_samples
Davide Lancierini