class CVAEGAN(Image2ImageGenerativeModel):
def __init__(self,
x_dim,
y_dim,
z_dim,
enc_architecture,
gen_architecture,
adversarial_architecture,
folder="./CVAEGAN",
is_patchgan=False,
is_wasserstein=False):
super(CVAEGAN, self).__init__(
x_dim, y_dim,
[enc_architecture, gen_architecture, adversarial_architecture],
folder)
self._z_dim = z_dim
with tf.name_scope("Inputs"):
self._Z_input = tf.placeholder(tf.float32,
shape=[None, z_dim],
name="z")
self._enc_architecture = self._architectures[0]
self._gen_architecture = self._architectures[1]
self._adv_architecture = self._architectures[2]
self._is_patchgan = is_patchgan
self._is_wasserstein = is_wasserstein
self._is_feature_matching = False
################# Define architecture
if self._is_patchgan:
f_xy = self._adv_architecture[-1][-1]["filters"]
assert f_xy == 1, "If is PatchGAN, last layer of adversarial_XY needs 1 filter. Given: {}.".format(
f_xy)
a_xy = self._adv_architecture[-1][-1]["activation"]
if self._is_wasserstein:
assert a_xy == tf.identity, "If is PatchGAN, last layer of adversarial needs tf.identity. Given: {}.".format(
a_xy)
else:
assert a_xy == tf.nn.sigmoid, "If is PatchGAN, last layer of adversarial needs tf.nn.sigmoid. Given: {}.".format(
a_xy)
else:
self._adv_architecture.append(
[tf.layers.flatten, {
"name": "Flatten"
}])
if self._is_wasserstein:
self._adv_architecture.append([
logged_dense, {
"units": 1,
"activation": tf.identity,
"name": "Output"
}
])
else:
self._adv_architecture.append([
logged_dense, {
"units": 1,
"activation": tf.nn.sigmoid,
"name": "Output"
}
])
last_layers_mean = [[tf.layers.flatten, {
"name": "flatten"
}],
[
logged_dense, {
"units": z_dim,
"activation": tf.identity,
"name": "Mean"
}
]]
self._encoder_mean = Encoder(self._enc_architecture + last_layers_mean,
name="Encoder")
last_layers_std = [[tf.layers.flatten, {
"name": "flatten"
}],
[
logged_dense, {
"units": z_dim,
"activation": tf.identity,
"name": "Std"
}
]]
self._encoder_std = Encoder(self._enc_architecture + last_layers_std,
name="Encoder")
self._gen_architecture[-1][1]["name"] = "Output"
self._generator = Generator(self._gen_architecture, name="Generator")
self._adversarial = Discriminator(self._adv_architecture,
name="Adversarial")
self._nets = [self._encoder_mean, self._generator, self._adversarial]
################# Connect inputs and networks
self._mean_layer = self._encoder_mean.generate_net(self._Y_input)
self._std_layer = self._encoder_std.generate_net(self._Y_input)
self._output_enc_with_noise = self._mean_layer + tf.exp(
0.5 * self._std_layer) * self._Z_input
with tf.name_scope("Inputs"):
self._gen_input = image_condition_concat(
inputs=self._X_input,
condition=self._output_enc_with_noise,
name="mod_z_real")
self._gen_input_from_encoding = image_condition_concat(
inputs=self._X_input, condition=self._Z_input, name="mod_z")
self._output_gen = self._generator.generate_net(self._gen_input)
self._output_gen_from_encoding = self._generator.generate_net(
self._gen_input_from_encoding)
self._generator._input_dim = z_dim
assert self._output_gen.get_shape()[1:] == y_dim, (
"Generator output must have shape of y_dim. Given: {}. Expected: {}."
.format(self._output_gen.get_shape(), x_dim))
with tf.name_scope("InputsAdversarial"):
self._input_real = tf.concat(values=[self._Y_input, self._X_input],
axis=3)
self._input_fake_from_real = tf.concat(
values=[self._output_gen, self._X_input], axis=3)
self._input_fake_from_latent = tf.concat(
values=[self._output_gen_from_encoding, self._X_input], axis=3)
self._output_adv_real = self._adversarial.generate_net(
self._input_real)
self._output_adv_fake_from_real = self._adversarial.generate_net(
self._input_fake_from_real)
self._output_adv_fake_from_latent = self._adversarial.generate_net(
self._input_fake_from_latent)
################# Finalize
self._init_folders()
self._verify_init()
self._output_label_real = tf.placeholder(
tf.float32, shape=self._output_adv_real.shape, name="label_real")
self._output_label_fake = tf.placeholder(
tf.float32,
shape=self._output_adv_fake_from_real.shape,
name="label_fake")
if self._is_patchgan:
print("PATCHGAN chosen with output: {}.".format(
self._output_adv_real.shape))
def compile(self,
loss,
optimizer,
learning_rate=None,
learning_rate_enc=None,
learning_rate_gen=None,
learning_rate_adv=None,
label_smoothing=1,
lmbda_kl=0.1,
lmbda_y=1,
feature_matching=False,
random_labeling=0):
if self._is_wasserstein and loss != "wasserstein":
raise ValueError(
"If is_wasserstein is true in Constructor, loss needs to be wasserstein."
)
if not self._is_wasserstein and loss == "wasserstein":
raise ValueError(
"If loss is wasserstein, is_wasserstein needs to be true in constructor."
)
if np.all([
lr is None for lr in [
learning_rate, learning_rate_enc, learning_rate_gen,
learning_rate_adv
]
]):
raise ValueError("Need learning_rate.")
if learning_rate is not None and learning_rate_enc is None:
learning_rate_enc = learning_rate
if learning_rate is not None and learning_rate_gen is None:
learning_rate_gen = learning_rate
if learning_rate is not None and learning_rate_adv is None:
learning_rate_adv = learning_rate
self._define_loss(loss=loss,
label_smoothing=label_smoothing,
lmbda_kl=lmbda_kl,
lmbda_y=lmbda_y,
feature_matching=feature_matching,
random_labeling=random_labeling)
with tf.name_scope("Optimizer"):
self._enc_optimizer = optimizer(learning_rate=learning_rate_enc)
self._enc_optimizer_op = self._enc_optimizer.minimize(
self._enc_loss,
var_list=self._get_vars("Encoder"),
name="Encoder")
self._gen_optimizer = optimizer(learning_rate=learning_rate_gen)
self._gen_oprimizer_op = self._gen_optimizer.minimize(
self._gen_loss,
var_list=self._get_vars("Generator"),
name="Generator")
self._adv_optimizer = optimizer(learning_rate=learning_rate_adv)
self._adv_optimizer_op = self._adv_optimizer.minimize(
self._adv_loss,
var_list=self._get_vars("Adversarial"),
name="Adversarial")
self._summarise()
def _define_loss(self, loss, label_smoothing, lmbda_kl, lmbda_y,
feature_matching, random_labeling):
possible_losses = ["cross-entropy", "L2", "wasserstein", "KL"]
def get_labels_one():
return tf.math.multiply(self._output_label_real, label_smoothing)
def get_labels_zero():
return self._output_label_fake
eps = 1e-6
self._label_smoothing = label_smoothing
self._random_labeling = random_labeling
## Kullback-Leibler divergence
self._KLdiv = 0.5 * (tf.square(self._mean_layer) +
tf.exp(self._std_layer) - self._std_layer - 1)
self._KLdiv = lmbda_kl * tf.reduce_mean(self._KLdiv)
## L1 loss
self._recon_loss = lmbda_y * tf.reduce_mean(
tf.abs(self._Y_input - self._output_gen))
## Adversarial loss
if loss == "cross-entropy":
self._logits_real = tf.math.log(self._output_adv_real /
(1 + eps - self._output_adv_real) +
eps)
self._logits_fake_from_real = tf.math.log(
self._output_adv_fake_from_real /
(1 + eps - self._output_adv_fake_from_real) + eps)
self._logits_fake_from_latent = tf.math.log(
self._output_adv_fake_from_latent /
(1 + eps - self._output_adv_fake_from_latent) + eps)
self._generator_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(self._logits_fake_from_real),
logits=self._logits_fake_from_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(self._logits_fake_from_latent),
logits=self._logits_fake_from_latent))
self._adversarial_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_one(), logits=self._logits_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_zero(),
logits=self._logits_fake_from_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_zero(),
logits=self._logits_fake_from_latent))
elif loss == "L2":
self._generator_loss = tf.reduce_mean(
tf.square(self._output_adv_fake_from_real -
tf.ones_like(self._output_adv_fake_from_real)) +
tf.square(self._output_adv_fake_from_latent -
tf.ones_like(self._output_adv_fake_from_latent))) / 2
self._adversarial_loss = (tf.reduce_mean(
tf.square(self._output_adv_real - get_labels_one()) +
tf.square(self._output_adv_fake_from_real -
get_labels_zero()) +
tf.square(self._output_adv_fake_from_latent -
get_labels_zero()))) / 3.0
elif loss == "wasserstein":
self._generator_loss = -tf.reduce_mean(
self._output_adv_fake_from_real) - tf.reduce_mean(
self._output_adv_fake_from_latent)
self._adversarial_loss = (
-(tf.reduce_mean(self._output_adv_real) -
tf.reduce_mean(self._output_adv_fake_from_real) -
tf.reduce_mean(self._output_adv_fake_from_latent)) +
10 * self._define_gradient_penalty())
elif loss == "KL":
self._logits_real = tf.math.log(self._output_adv_real /
(1 + eps - self._output_adv_real) +
eps)
self._logits_fake_from_real = tf.math.log(
self._output_adv_fake_from_real /
(1 + eps - self._output_adv_fake_from_real) + eps)
self._logits_fake_from_latent = tf.math.log(
self._output_adv_fake_from_latent /
(1 + eps - self._output_adv_fake_from_latent) + eps)
self._generator_loss = (
-tf.reduce_mean(self._logits_fake_from_real) -
tf.reduce_mean(self._logits_fake_from_latent)) / 2
self._adversarial_loss = tf.reduce_mean(
0.5 * tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_one(), logits=self._logits_real) +
0.25 * tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_zero(),
logits=self._logits_fake_from_real) +
0.25 * tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_zero(),
logits=self._logits_fake_from_latent))
else:
raise ValueError(
"Loss not implemented. Choose from {}. Given: {}.".format(
possible_losses, loss))
if feature_matching:
self._is_feature_matching = True
otp_adv_real = self._adversarial.generate_net(
self._input_real,
tf_trainflag=self._is_training,
return_idx=-2)
otp_adv_fake = self._adversarial.generate_net(
self._input_fake_from_real,
tf_trainflag=self._is_training,
return_idx=-2)
self._generator_loss = tf.reduce_mean(
tf.square(otp_adv_real - otp_adv_fake))
with tf.name_scope("Loss") as scope:
self._enc_loss = self._KLdiv + self._recon_loss + self._generator_loss
self._gen_loss = self._recon_loss + self._generator_loss
self._adv_loss = self._adversarial_loss
tf.summary.scalar("Kullback-Leibler", self._KLdiv)
tf.summary.scalar("Reconstruction", self._recon_loss)
tf.summary.scalar("Vanilla_Generator", self._generator_loss)
tf.summary.scalar("Encoder", self._enc_loss)
tf.summary.scalar("Generator", self._gen_loss)
tf.summary.scalar("Adversarial", self._adv_loss)
def _define_gradient_penalty(self):
alpha = tf.random_uniform(shape=tf.shape(self._input_real),
minval=0.,
maxval=1.)
differences = self._input_fake_from_real - self._input_real
interpolates = self._input_real + (alpha * differences)
gradients = tf.gradients(self._adversarial.generate_net(interpolates),
[interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients)))
with tf.name_scope("Loss") as scope:
self._gradient_penalty = tf.reduce_mean((slopes - 1.)**2)
tf.summary.scalar("Gradient_penalty", self._gradient_penalty)
return self._gradient_penalty
def train(self,
x_train,
y_train,
x_test,
y_test,
epochs=100,
batch_size=64,
adv_steps=5,
gen_steps=1,
log_step=3,
gpu_options=None,
batch_log_step=None):
self._set_up_training(log_step=log_step, gpu_options=gpu_options)
self._set_up_test_train_sample(x_train=x_train,
y_train=y_train,
x_test=x_test,
y_test=y_test)
self._z_test = self._generator.sample_noise(n=len(self._x_test))
nr_batches = np.floor(len(x_train) / batch_size)
self.batch_size = batch_size
self._prepare_monitoring()
self._log_results(epoch=0, epoch_time=0)
for epoch in range(epochs):
adv_loss_epoch = 0
gen_loss_epoch = 0
enc_loss_epoch = 0
start = time.clock()
trained_examples = 0
batch_nr = 0
while trained_examples < len(x_train):
batch_train_start = time.clock()
adv_loss_batch, gen_loss_batch, enc_loss_batch = self._optimize(
self._trainset, adv_steps, gen_steps)
trained_examples += self.batch_size
adv_loss_epoch += adv_loss_batch
gen_loss_epoch += gen_loss_batch
enc_loss_epoch += enc_loss_batch
self._total_train_time += (time.clock() - batch_train_start)
if (batch_log_step is not None) and (batch_nr % batch_log_step
== 0):
self._count_batches += batch_log_step
batch_train_time = (time.clock() - start) / 60
self._log(self._count_batches, batch_train_time)
batch_nr += 1
epoch_train_time = (time.clock() - start) / 60
adv_loss_epoch = np.round(adv_loss_epoch, 2)
gen_loss_epoch = np.round(gen_loss_epoch, 2)
enc_loss_epoch = np.round(enc_loss_epoch, 2)
print("\nEpoch {}: D: {}; G: {}; E: {}.".format(
epoch, adv_loss_epoch, gen_loss_epoch, enc_loss_epoch))
if batch_log_step is None and (log_step
is not None) and (epoch % log_step
== 0):
self._log(epoch + 1, epoch_train_time)
def _optimize(self, dataset, adv_steps, gen_steps):
for i in range(adv_steps):
current_batch_x, current_batch_y = dataset.get_next_batch(
self.batch_size)
Z_noise = self._generator.sample_noise(n=len(current_batch_x))
_, adv_loss_batch = self._sess.run(
[self._adv_optimizer_op, self._adv_loss],
feed_dict={
self._X_input: current_batch_x,
self._Y_input: current_batch_y,
self._Z_input: Z_noise,
self._is_training: True,
self._output_label_real:
self.get_random_label(is_real=True),
self._output_label_fake:
self.get_random_label(is_real=False)
})
for i in range(gen_steps):
Z_noise = self._generator.sample_noise(n=len(current_batch_x))
_, gen_loss_batch = self._sess.run(
[self._gen_oprimizer_op, self._gen_loss],
feed_dict={
self._X_input: current_batch_x,
self._Y_input: current_batch_y,
self._Z_input: Z_noise,
self._is_training: True
})
_, enc_loss_batch = self._sess.run(
[self._enc_optimizer_op, self._enc_loss],
feed_dict={
self._X_input: current_batch_x,
self._Y_input: current_batch_y,
self._Z_input: Z_noise,
self._is_training: True
})
return adv_loss_batch, gen_loss_batch, enc_loss_batch
def _log_results(self, epoch, epoch_time):
summary = self._sess.run(self._merged_summaries,
feed_dict={
self._X_input:
self._x_test,
self._Y_input:
self._y_test,
self._Z_input:
self._z_test,
self._epoch_time:
epoch_time,
self._is_training:
False,
self._epoch_nr:
epoch,
self._output_label_real:
self.get_random_label(is_real=True,
size=self._nr_test),
self._output_label_fake:
self.get_random_label(is_real=False,
size=self._nr_test)
})
self._writer1.add_summary(summary, epoch)
nr_test = len(self._x_test)
summary = self._sess.run(self._merged_summaries,
feed_dict={
self._X_input:
self._trainset.get_xdata()[:nr_test],
self._Z_input:
self._z_test,
self._Y_input:
self._trainset.get_ydata()[:nr_test],
self._epoch_time:
epoch_time,
self._is_training:
False,
self._epoch_nr:
epoch,
self._output_label_real:
self.get_random_label(is_real=True,
size=self._nr_test),
self._output_label_fake:
self.get_random_label(is_real=False,
size=self._nr_test)
})
self._writer2.add_summary(summary, epoch)
if self._image_shape is not None:
self.plot_samples(inpt_x=self._x_test[:10],
inpt_y=self._y_test[:10],
sess=self._sess,
image_shape=self._image_shape,
epoch=epoch,
path="{}/GeneratedSamples/result_{}.png".format(
self._folder, epoch))
self.save_model(epoch)
additional_log = getattr(self, "evaluate", None)
if callable(additional_log):
self.evaluate(true=self._x_test,
condition=self._y_test,
epoch=epoch)
print("Logged.")
def plot_samples(self, inpt_x, inpt_y, sess, image_shape, epoch, path):
outpt_xy = sess.run(self._output_gen_from_encoding,
feed_dict={
self._X_input: inpt_x,
self._Z_input: self._z_test[:len(inpt_x)],
self._is_training: False
})
image_matrix = np.array([[
x.reshape(self._x_dim[0], self._x_dim[1]),
y.reshape(self._y_dim[0], self._y_dim[1]),
np.zeros(shape=(self._x_dim[0], self._x_dim[1])),
xy.reshape(self._y_dim[0], self._y_dim[1])
] for x, y, xy in zip(inpt_x, inpt_y, outpt_xy)])
self._generator.build_generated_samples(
image_matrix,
column_titles=["True X", "True Y", "", "Gen_XY"],
epoch=epoch,
path=path)
def _prepare_monitoring(self):
self._total_train_time = 0
self._total_log_time = 0
self._count_batches = 0
self._batches = []
self._max_allowed_failed_checks = 20
self._enc_grads_and_vars = self._enc_optimizer.compute_gradients(
self._enc_loss, var_list=self._get_vars("Encoder"))
self._gen_grads_and_vars = self._gen_optimizer.compute_gradients(
self._gen_loss, var_list=self._get_vars("Generator"))
self._adv_grads_and_vars = self._adv_optimizer.compute_gradients(
self._adv_loss, var_list=self._get_vars("Adversarial"))
self._monitor_dict = {
"Gradients": [[
self._enc_grads_and_vars, self._gen_grads_and_vars,
self._adv_grads_and_vars
], ["Encoder", "Generator", "Adversarial"],
[[] for i in range(9)]],
"Losses":
[[
self._enc_loss, self._gen_loss, self._adversarial_loss,
self._generator_loss, self._recon_loss, self._KLdiv
],
[
"Encoder (V+R+K)", "Generator (V+R)", "Adversarial",
"Vanilla_Generator", "Reconstruction", "Kullback-Leibler"
], [[] for i in range(6)]],
"Output Adversarial": [[
self._output_adv_fake_from_real,
self._output_adv_fake_from_latent, self._output_adv_real
], ["Fake_from_real", "Fake_from_latent", "Real"],
[[] for i in range(3)], [np.mean]]
}
self._check_dict = {
"Dominating Discriminator": {
"Tensors":
[self._output_adv_real, self._output_adv_fake_from_real],
"OPonTensors": [np.mean, np.mean],
"Relation": [">", "<"],
"Threshold": [
self._label_smoothing * 0.95,
(1 - self._label_smoothing) * 1.05
],
"TensorRelation":
np.logical_and
},
"Generator outputs zeros": {
"Tensors": [
self._output_gen_from_encoding,
self._output_gen_from_encoding
],
"OPonTensors": [np.max, np.min],
"Relation": ["<", ">"],
"Threshold": [0.05, 0.95],
"TensorRelation":
np.logical_or
}
}
self._check_count = [0 for key in self._check_dict]
if not os.path.exists(self._folder + "/Evaluation"):
pos.mkdir(self._folder + "/Evaluation")
os.mkdir(self._folder + "/Evaluation/Cells")
os.mkdir(self._folder + "/Evaluation/CenterOfMassX")
os.mkdir(self._folder + "/Evaluation/CenterOfMassY")
os.mkdir(self._folder + "/Evaluation/Energy")
os.mkdir(self._folder + "/Evaluation/MaxEnergy")
os.mkdir(self._folder + "/Evaluation/StdEnergy")
def evaluate(self, true, condition, epoch):
print("Batch ", epoch)
log_start = time.clock()
self._batches.append(epoch)
fake = self._sess.run(self._output_gen_from_encoding,
feed_dict={
self._X_input: self._x_test,
self._Z_input: self._z_test,
self._is_training: False
})
true = self._y_test.reshape(
[-1, self._image_shape[0], self._image_shape[1]])
fake = fake.reshape([-1, self._image_shape[0], self._image_shape[1]])
build_histogram(true=true,
fake=fake,
function=get_energies,
name="Energy",
epoch=epoch,
folder=self._folder)
build_histogram(true=true,
fake=fake,
function=get_number_of_activated_cells,
name="Cells",
epoch=epoch,
folder=self._folder,
threshold=6 / 6120)
build_histogram(true=true,
fake=fake,
function=get_max_energy,
name="MaxEnergy",
epoch=epoch,
folder=self._folder)
build_histogram(true=true,
fake=fake,
function=get_center_of_mass_x,
name="CenterOfMassX",
epoch=epoch,
folder=self._folder,
image_shape=self._image_shape)
build_histogram(true=true,
fake=fake,
function=get_center_of_mass_y,
name="CenterOfMassY",
epoch=epoch,
folder=self._folder,
image_shape=self._image_shape)
build_histogram(true=true,
fake=fake,
function=get_std_energy,
name="StdEnergy",
epoch=epoch,
folder=self._folder)
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(18, 10))
axs = np.ravel(axs)
if "Gradients" in self._monitor_dict:
colors = ["green", "blue", "red"]
axy_min = np.inf
axy_max = -np.inf
for go, gradient_ops in enumerate(
self._monitor_dict["Gradients"][0]):
grads = [
self._sess.run(
gv[0],
feed_dict={
self._X_input:
self._x_test,
self._Y_input:
self._y_test,
self._Z_input:
self._z_test,
self._is_training:
False,
self._output_label_real:
self.get_random_label(is_real=True,
size=self._nr_test),
self._output_label_fake:
self.get_random_label(is_real=False,
size=self._nr_test)
}) for gv in gradient_ops
]
for op_idx, op in enumerate([np.mean, np.max, np.min]):
self._monitor_dict["Gradients"][2][go * 3 + op_idx].append(
op([op(grad) for grad in grads]))
vals = self._monitor_dict["Gradients"][2][go * 3 + op_idx]
if op_idx == 0:
axs[0].plot(
self._batches,
vals,
label=self._monitor_dict["Gradients"][1][go],
color=colors[go])
else:
axs[0].plot(self._batches,
vals,
linewidth=0.5,
linestyle="--",
color=colors[go])
upper = np.mean(vals)
lower = np.mean(vals)
if upper > axy_max:
axy_max = upper
if lower < axy_min:
axy_min = lower
axs[0].set_title("Gradients")
axs[0].legend()
axs[0].set_ylim([axy_min, axy_max])
current_batch_x, current_batch_y = self._trainset.get_next_batch(
self.batch_size)
Z_noise = self._generator.sample_noise(n=len(current_batch_x))
colors = [
"green", "blue", "red", "orange", "purple", "brown", "gray",
"pink", "cyan", "olive"
]
for k, key in enumerate(self._monitor_dict):
if key == "Gradients":
continue
key_results = self._sess.run(
self._monitor_dict[key][0],
feed_dict={
self._X_input: current_batch_x,
self._Y_input: current_batch_y,
self._Z_input: Z_noise,
self._is_training: True,
self._output_label_real:
self.get_random_label(is_real=True),
self._output_label_fake:
self.get_random_label(is_real=False)
})
for kr, key_result in enumerate(key_results):
try:
self._monitor_dict[key][2][kr].append(
self._monitor_dict[key][3][0](key_result))
except IndexError:
self._monitor_dict[key][2][kr].append(key_result)
axs[k].plot(self._batches,
self._monitor_dict[key][2][kr],
label=self._monitor_dict[key][1][kr],
color=colors[kr])
axs[k].legend()
axs[k].set_title(key)
print("; ".join([
"{}: {}".format(name, round(float(val[-1]), 5)) for name, val
in zip(self._monitor_dict[key][1], self._monitor_dict[key][2])
]))
gen_samples = self._sess.run(
[self._output_gen_from_encoding],
feed_dict={
self._X_input: current_batch_x,
self._Z_input: Z_noise,
self._is_training: False
})
axs[-1].hist([np.ravel(gen_samples),
np.ravel(current_batch_y)],
label=["Generated", "True"])
axs[-1].set_title("Pixel distribution")
axs[-1].legend()
for check_idx, check_key in enumerate(self._check_dict):
result_bools_of_check = []
check = self._check_dict[check_key]
for tensor_idx in range(len(check["Tensors"])):
tensor_ = self._sess.run(check["Tensors"][tensor_idx],
feed_dict={
self._X_input: self._x_test,
self._Y_input: self._y_test,
self._Z_input: self._z_test,
self._is_training: False
})
tensor_op = check["OPonTensors"][tensor_idx](tensor_)
if eval(
str(tensor_op) + check["Relation"][tensor_idx] +
str(check["Threshold"][tensor_idx])):
result_bools_of_check.append(True)
else:
result_bools_of_check.append(False)
if (tensor_idx > 0 and check["TensorRelation"](
*result_bools_of_check)) or (result_bools_of_check[0]):
self._check_count[check_idx] += 1
if self._check_count[
check_idx] == self._max_allowed_failed_checks:
raise GeneratorExit(check_key)
else:
self._check_count[check_idx] = 0
self._total_log_time += (time.clock() - log_start)
fig.suptitle("Train {} / Log {} / Fails {}".format(
np.round(self._total_train_time, 2),
np.round(self._total_log_time, 2), self._check_count))
plt.savefig(self._folder + "/TrainStatistics.png")
plt.close("all")
def get_random_label(self, is_real, size=None):
if size is None:
size = self.batch_size
labels_shape = [size, *self._output_adv_real.shape.as_list()[1:]]
labels = np.ones(shape=labels_shape)
if self._random_labeling > 0:
relabel_mask = np.random.binomial(n=1,
p=self._random_labeling,
size=labels_shape) == 1
labels[relabel_mask] = 0
if not is_real:
labels = 1 - labels
return labels
class VAEGAN(GenerativeModel):
def __init__(self,
x_dim,
z_dim,
enc_architecture,
gen_architecture,
disc_architecture,
folder="./VAEGAN"):
super(VAEGAN, self).__init__(
x_dim, z_dim,
[enc_architecture, gen_architecture, disc_architecture], folder)
self._enc_architecture = self._architectures[0]
self._gen_architecture = self._architectures[1]
self._disc_architecture = self._architectures[2]
################# Define architecture
last_layer_mean = [
logged_dense, {
"units": z_dim,
"activation": tf.identity,
"name": "Mean"
}
]
self._encoder_mean = Encoder(self._enc_architecture +
[last_layer_mean],
name="Encoder")
last_layer_std = [
logged_dense, {
"units": z_dim,
"activation": tf.identity,
"name": "Std"
}
]
self._encoder_std = Encoder(self._enc_architecture + [last_layer_std],
name="Encoder")
self._gen_architecture[-1][1]["name"] = "Output"
self._generator = Generator(self._gen_architecture, name="Generator")
self._disc_architecture.append(
[tf.layers.flatten, {
"name": "Flatten"
}])
self._disc_architecture.append([
logged_dense, {
"units": 1,
"activation": tf.nn.sigmoid,
"name": "Output"
}
])
self._discriminator = Discriminator(self._disc_architecture,
name="Discriminator")
self._nets = [self._encoder_mean, self._generator, self._discriminator]
################# Connect inputs and networks
self._mean_layer = self._encoder_mean.generate_net(self._X_input)
self._std_layer = self._encoder_std.generate_net(self._X_input)
self._output_enc_with_noise = self._mean_layer + tf.exp(
0.5 * self._std_layer) * self._Z_input
self._output_gen = self._generator.generate_net(
self._output_enc_with_noise)
self._output_gen_from_encoding = self._generator.generate_net(
self._Z_input)
assert self._output_gen.get_shape()[1:] == x_dim, (
"Generator output must have shape of x_dim. Given: {}. Expected: {}."
.format(self._output_gen.get_shape(), x_dim))
self._output_disc_real = self._discriminator.generate_net(
self._X_input)
self._output_disc_fake_from_real = self._discriminator.generate_net(
self._output_gen)
self._output_disc_fake_from_latent = self._discriminator.generate_net(
self._output_gen_from_encoding)
################# Finalize
self._init_folders()
self._verify_init()
def compile(self,
learning_rate=0.0001,
optimizer=tf.train.AdamOptimizer,
label_smoothing=1,
gamma=1):
self._define_loss(label_smoothing=label_smoothing, gamma=gamma)
with tf.name_scope("Optimizer"):
enc_optimizer = optimizer(learning_rate=learning_rate)
self._enc_optimizer = enc_optimizer.minimize(
self._enc_loss,
var_list=self._get_vars("Encoder"),
name="Encoder")
gen_optimizer = optimizer(learning_rate=learning_rate)
self._gen_optimizer = gen_optimizer.minimize(
self._gen_loss,
var_list=self._get_vars("Generator"),
name="Generator")
disc_optimizer = optimizer(learning_rate=learning_rate)
self._disc_optimizer = disc_optimizer.minimize(
self._disc_loss,
var_list=self._get_vars("Discriminator"),
name="Discriminator")
self._summarise()
def _define_loss(self, label_smoothing, gamma):
def get_labels_one(tensor):
return tf.ones_like(tensor) * label_smoothing
eps = 1e-7
## Kullback-Leibler divergence
self._KLdiv = 0.5 * (tf.square(self._mean_layer) +
tf.exp(self._std_layer) - self._std_layer - 1)
self._KLdiv = tf.reduce_mean(self._KLdiv)
## Feature matching loss
otp_disc_real = self._discriminator.generate_net(
self._X_input, tf_trainflag=self._is_training, return_idx=-2)
otp_disc_fake = self._discriminator.generate_net(
self._output_gen, tf_trainflag=self._is_training, return_idx=-2)
self._feature_loss = tf.reduce_mean(
tf.square(otp_disc_real - otp_disc_fake))
## Discriminator loss
self._logits_real = tf.math.log(self._output_disc_real /
(1 + eps - self._output_disc_real) +
eps)
self._logits_fake_from_real = tf.math.log(
self._output_disc_fake_from_real /
(1 + eps - self._output_disc_fake_from_real) + eps)
self._logits_fake_from_latent = tf.math.log(
self._output_disc_fake_from_latent /
(1 + eps - self._output_disc_fake_from_latent) + eps)
self._generator_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_one(self._logits_fake_from_real),
logits=self._logits_fake_from_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_one(self._logits_fake_from_latent),
logits=self._logits_fake_from_latent))
self._discriminator_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=get_labels_one(
self._logits_real),
logits=self._logits_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(self._logits_fake_from_real),
logits=self._logits_fake_from_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(self._logits_fake_from_latent),
logits=self._logits_fake_from_latent))
with tf.name_scope("Loss") as scope:
self._enc_loss = self._KLdiv + self._feature_loss
self._gen_loss = self._feature_loss + self._generator_loss
self._disc_loss = self._discriminator_loss
tf.summary.scalar("Encoder", self._enc_loss)
tf.summary.scalar("Generator", self._gen_loss)
tf.summary.scalar("Discriminator", self._disc_loss)
def train(self,
x_train,
x_test,
epochs=100,
batch_size=64,
disc_steps=5,
gen_steps=1,
log_step=3):
self._set_up_training(log_step=log_step)
self._set_up_test_train_sample(x_train, x_test)
for epoch in range(epochs):
batch_nr = 0
disc_loss_epoch = 0
gen_loss_epoch = 0
enc_loss_epoch = 0
start = time.clock()
trained_examples = 0
while trained_examples < len(x_train):
disc_loss_batch, gen_loss_batch, enc_loss_batch = self._optimize(
self._trainset, batch_size, disc_steps, gen_steps)
trained_examples += batch_size
disc_loss_epoch += disc_loss_batch
gen_loss_epoch += gen_loss_batch
enc_loss_epoch += enc_loss_batch
epoch_train_time = (time.clock() - start) / 60
disc_loss_epoch = np.round(disc_loss_epoch, 2)
gen_loss_epoch = np.round(gen_loss_epoch, 2)
enc_loss_epoch = np.round(enc_loss_epoch, 2)
print("Epoch {}: D: {}; G: {}; E: {}.".format(
epoch, disc_loss_epoch, gen_loss_epoch, enc_loss_epoch))
if log_step is not None:
self._log(epoch, epoch_train_time)
def _optimize(self, dataset, batch_size, disc_steps, gen_steps):
for i in range(disc_steps):
current_batch_x = dataset.get_next_batch(batch_size)
Z_noise = self._generator.sample_noise(n=len(current_batch_x))
_, disc_loss_batch = self._sess.run(
[self._disc_optimizer, self._disc_loss],
feed_dict={
self._X_input: current_batch_x,
self._Z_input: Z_noise
})
for i in range(gen_steps):
Z_noise = self._generator.sample_noise(n=len(current_batch_x))
_, gen_loss_batch = self._sess.run(
[self._gen_optimizer, self._gen_loss],
feed_dict={
self._X_input: current_batch_x,
self._Z_input: Z_noise
})
_, enc_loss_batch = self._sess.run(
[self._enc_optimizer, self._enc_loss],
feed_dict={
self._X_input: current_batch_x,
self._Z_input: Z_noise
})
return disc_loss_batch, gen_loss_batch, enc_loss_batch
class Pix2PixGAN(Image2ImageGenerativeModel):
def __init__(self,
x_dim,
y_dim,
gen_architecture,
disc_architecture,
folder="./Pix2Pix",
is_patchgan=False,
is_wasserstein=False):
super(Pix2PixGAN,
self).__init__(x_dim, y_dim,
[gen_architecture, disc_architecture], folder)
self._gen_architecture = self._architectures[0]
self._disc_architecture = self._architectures[1]
self._is_patchgan = is_patchgan
self._is_wasserstein = is_wasserstein
################# Define architecture
if self._is_patchgan:
f_xy = self._disc_architecture[-1][-1]["filters"]
assert f_xy == 1, "If is_patchgan, last layer of Discriminator_XY needs 1 filter. Given: {}.".format(
f_xy)
a_xy = self._disc_architecture[-1][-1]["activation"]
if self._is_wasserstein:
assert a_xy == tf.identity, "If is_patchgan, last layer of Discriminator_XY needs tf.nn.identity. Given: {}.".format(
a_xy)
else:
assert a_xy == tf.nn.sigmoid, "If is_patchgan, last layer of Discriminator_XY needs tf.nn.sigmoid. Given: {}.".format(
a_xy)
else:
self._disc_architecture.append(
[tf.layers.flatten, {
"name": "Flatten"
}])
if self._is_wasserstein:
self._disc_architecture.append([
logged_dense, {
"units": 1,
"activation": tf.identity,
"name": "Output"
}
])
else:
self._disc_architecture.append([
logged_dense, {
"units": 1,
"activation": tf.nn.sigmoid,
"name": "Output"
}
])
self._gen_architecture[-1][1]["name"] = "Output"
self._generator = Generator(self._gen_architecture, name="Generator")
self._discriminator = Discriminator(self._disc_architecture,
name="Discriminator")
self._nets = [self._generator, self._discriminator]
################# Connect inputs and networks
self._output_gen = self._generator.generate_net(
self._X_input, tf_trainflag=self._is_training)
with tf.name_scope("InputsMod"):
gen_out_shape = self._output_gen.get_shape()
x_shape = self._X_input.get_shape()
y_shape = self._Y_input.get_shape()
assert gen_out_shape[1] == x_shape[1] and gen_out_shape[
2] == x_shape[2], (
"Wrong shapes: Generator output has {}, while X input has {}."
.format(gen_out_shape, x_shape))
assert y_shape[1] == x_shape[1] and y_shape[2] == x_shape[2], (
"Wrong shapes: Y input has {}, while X input has {}.".format(
y_shape, x_shape))
self._output_gen_mod = tf.concat(
values=[self._output_gen, self._X_input],
axis=3,
name="mod_gen")
self._Y_input_mod = tf.concat(
values=[self._Y_input, self._X_input], axis=3, name="mod_y")
self._output_disc_real = self._discriminator.generate_net(
self._Y_input_mod, tf_trainflag=self._is_training)
self._output_disc_fake = self._discriminator.generate_net(
self._output_gen_mod, tf_trainflag=self._is_training)
if self._is_patchgan:
print("PATCHGAN chosen with output: {}.".format(
self._output_disc_real.shape))
################# Finalize
self._init_folders()
self._verify_init()
def compile(self,
learning_rate=0.0005,
learning_rate_gen=None,
learning_rate_disc=None,
optimizer=tf.train.AdamOptimizer,
lmbda=10,
loss="cross-entropy",
label_smoothing=1):
if self._is_wasserstein and loss != "wasserstein":
raise ValueError(
"If is_wasserstein is true in Constructor, loss needs to be wasserstein."
)
if not self._is_wasserstein and loss == "wasserstein":
raise ValueError(
"If loss is wasserstein, is_wasserstein needs to be true in constructor."
)
if learning_rate_gen is None:
learning_rate_gen = learning_rate
if learning_rate_disc is None:
learning_rate_disc = learning_rate
self._define_loss(lmbda=lmbda,
loss=loss,
label_smoothing=label_smoothing)
with tf.name_scope("Optimizer"):
self._gen_optimizer = optimizer(learning_rate=learning_rate_gen)
self._gen_optimizer_op = self._gen_optimizer.minimize(
self._gen_loss,
var_list=self._get_vars(scope="Generator"),
name="Generator")
self._disc_optimizer = optimizer(learning_rate=learning_rate_disc)
self._disc_optimizer_op = self._disc_optimizer.minimize(
self._disc_loss,
var_list=self._get_vars(scope="Discriminator"),
name="Discriminator")
self._summarise()
def _define_loss(self, lmbda, loss, label_smoothing):
possible_losses = ["cross-entropy", "L2", "wasserstein"]
def get_labels_one(tensor):
return tf.ones_like(tensor) * label_smoothing
def get_labels_zero(tensor):
return tf.zeros_like(tensor) + 1 - label_smoothing
eps = 1e-7
self._label_smoothing = label_smoothing
if loss == "wasserstein":
self._generator_loss = -tf.reduce_mean(self._output_disc_fake)
self._disc_loss = (-(tf.reduce_mean(self._output_disc_real) -
tf.reduce_mean(self._output_disc_fake)) +
10 * self._define_gradient_penalty())
elif loss == "cross-entropy":
self._logits_real = tf.math.log(
self._output_disc_real / (1 + eps - self._output_disc_real) +
eps)
self._logits_fake = tf.math.log(
self._output_disc_fake / (1 + eps - self._output_disc_fake) +
eps)
self._generator_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(self._logits_fake),
logits=self._logits_fake))
self._disc_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_one(self._logits_real),
logits=self._logits_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_zero(self._logits_fake),
logits=self._logits_fake))
elif loss == "L2":
self._generator_loss = tf.reduce_mean(
tf.square(self._output_disc_fake -
tf.ones_like(self._output_disc_fake)))
self._disc_loss = (tf.reduce_mean(
tf.square(self._output_disc_real -
get_labels_one(self._output_disc_real)) +
tf.square(self._output_disc_fake))) / 2.0
elif loss == "KL":
self._logits_real = tf.math.log(
self._output_disc_real / (1 + eps - self._output_disc_real) +
eps)
self._logits_fake = tf.math.log(
self._output_disc_fake / (1 + eps - self._output_disc_fake) +
eps)
self._generator_loss = -tf.reduce_mean(self._logits_fake)
self._disc_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_one(self._logits_real),
logits=self._logits_real) +
tf.nn.sigmoid_cross_entropy_with_logits(
labels=get_labels_zero(self._logits_fake),
logits=self._logits_fake))
else:
raise ValueError(
"Loss not implemented. Choose from {}. Given: {}.".format(
possible_losses, loss))
with tf.name_scope("Loss") as scope:
tf.summary.scalar("Generator_vanilla_loss", self._generator_loss)
self._recon_loss = lmbda * tf.reduce_mean(
tf.abs(self._Y_input - self._output_gen))
tf.summary.scalar("Generator_recon_loss", self._recon_loss)
self._gen_loss = self._generator_loss + self._recon_loss
tf.summary.scalar("Generator_total_loss", self._gen_loss)
tf.summary.scalar("Discriminator_loss", self._disc_loss)
def _define_gradient_penalty(self):
alpha = tf.random_uniform(shape=tf.shape(self._Y_input),
minval=0.,
maxval=1.)
differences = self._output_gen - self._Y_input
interpolates = self._Y_input + (alpha * differences)
interpolates = tf.concat(values=[interpolates, self._X_input], axis=3)
gradients = tf.gradients(
self._discriminator.generate_net(interpolates,
tf_trainflag=self._is_training),
[interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients)))
with tf.name_scope("Loss") as scope:
self._gradient_penalty = tf.reduce_mean((slopes - 1.)**2)
tf.summary.scalar("Gradient_penalty", self._gradient_penalty)
return self._gradient_penalty
def train(self,
x_train,
y_train,
x_test=None,
y_test=None,
epochs=100,
batch_size=64,
gen_steps=1,
disc_steps=5,
steps=None,
log_step=3,
batch_log_step=None,
gpu_options=None):
if steps is not None:
gen_steps = 1
disc_steps = steps
self._set_up_training(log_step=log_step, gpu_options=gpu_options)
self._set_up_test_train_sample(x_train, y_train, x_test, y_test)
self._prepare_monitoring()
self._log_results(epoch=0, epoch_time=0)
nr_batches = np.floor(len(x_train) / batch_size)
for epoch in range(epochs):
batch_nr = 0
disc_loss_epoch = 0
gen_loss_epoch = 0
start = time.clock()
trained_examples = 0
while trained_examples < len(x_train):
disc_loss_batch, gen_loss_batch = self._optimize(
self._trainset, batch_size, disc_steps, gen_steps)
trained_examples += batch_size
disc_loss_epoch += disc_loss_batch
gen_loss_epoch += gen_loss_batch
if (batch_log_step is not None) and (batch_nr % batch_log_step
== 0):
self._count_batches += batch_log_step
batch_train_time = (time.clock() - start) / 60
self._log(self._count_batches, batch_train_time)
batch_nr += 1
disc_loss_epoch /= nr_batches
gen_loss_epoch /= nr_batches
epoch_train_time = (time.clock() - start) / 60
disc_loss_epoch = np.round(disc_loss_epoch, 7)
gen_loss_epoch = np.round(gen_loss_epoch, 7)
print("Epoch {}: Discrimiantor: {} \n\t\t\tGenerator: {}.".format(
epoch + 1, disc_loss_epoch, gen_loss_epoch))
if batch_log_step is None and (log_step
is not None) and (epoch % log_step
== 0):
self._log(epoch + 1, epoch_train_time)
def _optimize(self, dataset, batch_size, disc_steps, gen_steps):
d_loss = 0
for i in range(disc_steps):
current_batch_x, current_batch_y = dataset.get_next_batch(
batch_size)
_, disc_loss_batch = self._sess.run(
[self._disc_optimizer_op, self._disc_loss],
feed_dict={
self._X_input: current_batch_x,
self._Y_input: current_batch_y,
self._is_training: True
})
d_loss += disc_loss_batch
d_loss /= disc_steps
g_loss = 0
for _ in range(gen_steps):
current_batch_x, current_batch_y = dataset.get_next_batch(
batch_size)
_, gen_loss_batch = self._sess.run(
[self._gen_optimizer_op, self._gen_loss],
feed_dict={
self._X_input: current_batch_x,
self._Y_input: current_batch_y,
self._is_training: True
})
g_loss += gen_loss_batch
g_loss /= gen_steps
return d_loss, g_loss
def _prepare_monitoring(self):
self._total_train_time = 0
self._total_log_time = 0
self._count_batches = 0
self._batches = []
self._max_allowed_failed_checks = 20
self._gen_grads_and_vars = self._gen_optimizer.compute_gradients(
self._gen_loss, var_list=self._get_vars("Generator"))
self._disc_grads_and_vars = self._disc_optimizer.compute_gradients(
self._disc_loss, var_list=self._get_vars("Discriminator"))
self._monitor_dict = {
"Gradients":
[[self._gen_grads_and_vars, self._disc_grads_and_vars],
["Generator", "Adversarial"], [[] for i in range(6)]],
"Losses": [[
self._gen_loss, self._disc_loss, self._generator_loss,
self._recon_loss
],
[
"Generator (V+R)", "Adversarial",
"Vanilla_Generator", "Reconstruction"
], [[] for i in range(4)]],
"Output Adversarial":
[[self._output_disc_fake, self._output_disc_real],
["Fake", "Real"], [[] for i in range(3)], [np.mean]]
}
self._check_dict = {
"Dominating Discriminator": {
"Tensors": [self._output_disc_real, self._output_disc_fake],
"OPonTensors": [np.mean, np.mean],
"Relation": [">", "<"],
"Threshold": [
self._label_smoothing * 0.95,
(1 - self._label_smoothing) * 1.05
],
"TensorRelation":
np.logical_and
},
"Generator outputs zeros": {
"Tensors": [self._output_gen],
"OPonTensors": [np.max],
"Relation": ["<"],
"Threshold": [0.05]
}
}
self._check_count = [0 for key in self._check_dict]
def evaluate(self, true, condition, epoch):
log_start = time.clock()
self._batches.append(epoch)
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(18, 10))
axs = np.ravel(axs)
if "Gradients" in self._monitor_dict:
colors = ["green", "blue", "red"]
axy_min = np.inf
axy_max = -np.inf
for go, gradient_ops in enumerate(
self._monitor_dict["Gradients"][0]):
grads = [
self._sess.run(gv[0],
feed_dict={
self._X_input: self._x_test,
self._Y_input: self._y_test,
self._is_training: False
}) for gv in gradient_ops
]
for op_idx, op in enumerate([np.mean, np.max, np.min]):
self._monitor_dict["Gradients"][2][go * 3 + op_idx].append(
op([op(grad) for grad in grads]))
vals = self._monitor_dict["Gradients"][2][go * 3 + op_idx]
if op_idx == 0:
axs[0].plot(
self._batches,
vals,
label=self._monitor_dict["Gradients"][1][go],
color=colors[go])
else:
axs[0].plot(self._batches,
vals,
linewidth=0.5,
linestyle="--",
color=colors[go])
upper = np.mean(vals)
lower = np.mean(vals)
if upper > axy_max:
axy_max = upper
if lower < axy_min:
axy_min = lower
axs[0].set_title("Gradients")
axs[0].legend()
axs[0].set_ylim([axy_min, axy_max])
current_batch_x, current_batch_y = self._trainset.get_next_batch(
batch_size)
print("Batch ", epoch)
colors = ["green", "blue", "red", "orange", "purple", "brown"]
for k, key in enumerate(self._monitor_dict):
if key == "Gradients":
continue
key_results = self._sess.run(self._monitor_dict[key][0],
feed_dict={
self._X_input: current_batch_x,
self._Y_input: current_batch_y,
self._is_training: True
})
for kr, key_result in enumerate(key_results):
try:
self._monitor_dict[key][2][kr].append(
self._monitor_dict[key][3][0](key_result))
except IndexError:
self._monitor_dict[key][2][kr].append(key_result)
axs[k].plot(self._batches,
self._monitor_dict[key][2][kr],
label=self._monitor_dict[key][1][kr],
color=colors[kr])
axs[k].legend()
axs[k].set_title(key)
print("; ".join([
"{}: {}".format(name, round(float(val[-1]), 5)) for name, val
in zip(self._monitor_dict[key][1], self._monitor_dict[key][2])
]))
gen_samples = self._sess.run([self._output_gen],
feed_dict={
self._X_input: current_batch_x,
self._is_training: False
})
axs[-1].hist([np.ravel(gen_samples),
np.ravel(current_batch_y)],
label=["Generated", "True"])
axs[-1].set_title("Pixel distribution")
axs[-1].legend()
for check_idx, check_key in enumerate(self._check_dict):
result_bools_of_check = []
check = self._check_dict[check_key]
for tensor_idx in range(len(check["Tensors"])):
tensor_ = self._sess.run(check["Tensors"][tensor_idx],
feed_dict={
self._X_input: self._x_test,
self._Y_input: self._y_test,
self._is_training: False
})
tensor_op = check["OPonTensors"][tensor_idx](tensor_)
if eval(
str(tensor_op) + check["Relation"][tensor_idx] +
str(check["Threshold"][tensor_idx])):
result_bools_of_check.append(True)
else:
result_bools_of_check.append(False)
if (tensor_idx > 0 and check["TensorRelation"](
*result_bools_of_check)) or (result_bools_of_check[0]):
self._check_count[check_idx] += 1
if self._check_count[
check_idx] == self._max_allowed_failed_checks:
raise GeneratorExit(check_key)
else:
self._check_count[check_idx] = 0
self._total_log_time += (time.clock() - log_start)
fig.suptitle("Train {} / Log {} / Fails {}".format(
np.round(self._total_train_time, 2),
np.round(self._total_log_time, 2), self._check_count))
plt.savefig(self._folder + "/TrainStatistics.png")