def train_dense_latent_pong_no_batchnorm(beta):
# inputs
input_shape = (1, 84, 84)
epochs = 10
batch_size = 1
filters = 32
kernel_size = 6
pre_latent_size = 128
latent_size = 32
# define filename
name = 'cvae_atari_dense_latent_pong_no_batchnorm_beta_' + str(beta)
# builder hyperparameter dictionary
hp_dictionary = {
'epochs': epochs,
'batch_size': batch_size,
'beta': beta,
'filters': filters,
'kernel_size': kernel_size,
'loss': 'vae_loss',
'optimizer': 'adam'
}
# define log directory
log_dir = './summaries/' + experiment + '/' + utils.build_hyperparameter_string(name, hp_dictionary) + '/'
# make VAE
vae = DenseLatentPongNoBatchNorm(input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
pre_latent_size=pre_latent_size,
latent_size=latent_size,
beta=beta)
# compile VAE
from keras import optimizers
optimizer = optimizers.Adam(lr=1e-3)
vae.compile(optimizer=optimizer)
# get dataset
train_directory = './atari_agents/record/train/'
test_directory = './atari_agents/record/test/'
train_generator = utils.atari_generator(train_directory, batch_size=batch_size)
test_generator = utils.atari_generator(test_directory, batch_size=batch_size)
train_size = utils.count_images(train_directory)
test_size = utils.count_images(test_directory)
# print summaries
vae.print_model_summaries()
# fit VAE
steps_per_epoch = int(train_size / batch_size)
validation_steps = int(test_size / batch_size)
vae.fit_generator(train_generator,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_generator,
validation_steps=validation_steps)
def train_entangled_pong_network():
# inputs
input_shape = (1, 84, 84)
filters = 32
kernel_size = 6
beta = 1.0 # entangled latent space
epochs = 10
batch_size = 1
# define filename
name = 'cvae_atari_entangled_pong'
# builder hyperparameter dictionary
hp_dictionary = {
'epochs': epochs,
'batch_size': batch_size,
'beta': beta,
'filters': filters,
'kernel_size': kernel_size,
'loss': 'vae_loss',
'optimizer': 'adam'
}
# define log directory
log_dir = './summaries/' + experiment + '/' + utils.build_hyperparameter_string(
name, hp_dictionary) + '/'
# make VAE
vae = PongEntangledConvolutionalLatentVAE(input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
beta=beta)
# compile VAE
from keras import optimizers
optimizer = optimizers.Adam(lr=1e-1)
vae.compile(optimizer=optimizer)
# get dataset
train_directory = './atari_agents/record/train/'
test_directory = './atari_agents/record/test/'
train_generator = utils.atari_generator(train_directory,
batch_size=batch_size)
test_generator = utils.atari_generator(test_directory,
batch_size=batch_size)
train_size = utils.count_images(train_directory)
test_size = utils.count_images(test_directory)
# print summaries
vae.print_model_summaries()
# fit VAE
steps_per_epoch = int(train_size / batch_size)
validation_steps = int(test_size / batch_size)
vae.fit_generator(train_generator,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_generator,
validation_steps=validation_steps)
def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
kernel_size = 6
epochs = 20
batch_size = 1
# log directory
run = 'cvae_atari_space_invaders_no_batch_norm_18_May_20_33_10_batch_size_1_beta_8_epochs_10_filters_32_kernel_size_6_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# define model
vae = PongEntangledConvolutionalLatentNoBatchNormVAE(
input_shape, log_dir, filters=filters, kernel_size=kernel_size)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1)
X_test_size = 1000
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
sampling.encode_decode_sample(X_test, model)
def main():
# inputs
input_shape = (1, 84, 84)
epochs = 10
batch_size = 1
filters = 32
kernel_size = 6
pre_latent_size = 512
latent_size = 32
# log directory
run = 'cvae_atari_dense_latent_pong_no_batchnorm_beta_2_16_May_00_19_59_batch_size_1_beta_2_epochs_10_filters_32_kernel_size_6_loss_vae_loss_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# make VAE
vae = DenseLatentPongNoBatchNorm(input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
pre_latent_size=pre_latent_size,
latent_size=latent_size)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1)
X_test_size = 100
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
sampling.encode_decode_sample(X_test, model)
# plot filters
# sampling.show_convolutional_layers(X_test, encoder, 8, 8)
# sample from prior
# sampling.decode_prior_samples(5, decoder, latent_shape=(1, 32))
# sample from posterior
# num_iter = 100
# sampling.sample_posterior(X_test, model, num_iter, show_every=1)
latent_shape = (1, latent_size)
latent_index = 30
sampling.change_latent_variable(latent_shape, latent_index, decoder)
def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 6
epochs = 10
batch_size = 1
lr = 1e-4
img_channels = 1
beta = 1.0
# log directory
run = 'cvae_atari_average_filter_18_Jun_20_23_05_batch_size_1_beta_1_epochs_10_filters_32_img_channels_1_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
# run = 'cvae_atari_average_filter_18_Jun_22_25_44_batch_size_1_beta_2_epochs_10_filters_32_img_channels_1_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
# run = 'cvae_atari_average_filter_19_Jun_00_24_43_batch_size_1_beta_4_epochs_10_filters_32_img_channels_1_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# make VAE
vae = IndiscriminateDecoupling(input_shape,
log_dir,
filters=filters,
latent_filters=latent_filters,
kernel_size=kernel_size,
img_channels=img_channels,
beta=beta)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1, shuffle=False)
X_test_size = 1000
X_test = np.asarray([next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
# for sample_number in range(4):
# sampling.encode_decode_sample(X_test, model, sample_number=sample_number, save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/indiscriminate_decoupling/', base='beta_4_')
# plt.show()
# plot filters
# sampling.show_convolutional_layers(X_test, encoder, 2, 4, init_sample_num=3, save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/indiscriminate_decoupling/', base='beta_4_')
# plt.show()
# sample from prior
sampling.decode_prior_samples(5, decoder, latent_shape=(1, 8, 8, 8), save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/indiscriminate_decoupling/', base='beta_1_')
def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 6
epochs = 10
batch_size = 1
lr = 1e-4
beta = 1.0
# log directory
run = 'cvae_atari_28_May_16_14_52_batch_size_1_beta_1_epochs_12_filters_32_kernel_size_7_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# make VAE
vae = ConvolutionalLatentAverageFilterShallowVAE(
input_shape,
log_dir,
filters=filters,
latent_filters=latent_filters,
kernel_size=kernel_size,
beta=beta)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory,
batch_size=1,
shuffle=False)
X_test_size = 330
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
sampling.encode_decode_sample(X_test, model)
plt.show()
def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 6
epochs = 10
batch_size = 1
lr = 1e-4
img_channels = 1
beta = 1.0
# log directory
run = 'weighted_average_filter_19_Jun_04_15_36_batch_size_1_beta_1_epochs_10_filters_32_img_channels_3_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# make VAE
vae = WeightedAverageFilters(input_shape,
log_dir,
filters=filters,
latent_filters=latent_filters,
kernel_size=kernel_size,
img_channels=img_channels,
beta=beta)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1)
X_test_size = 1000
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
sampling.encode_decode_sample(X_test, model)
plt.show()
def main():
# inputs
input_shape = (1, 84, 84)
epochs = 10
batch_size = 1
beta = 1.0
filters = 32
kernel_size = 6
pre_latent_size = 512
latent_size = 32
# log directory
experiment = 'experiment_optimal_network_dense_latent_pong'
run = 'cvae_atari_dense_latent_pong_reconstruction_only_15_May_15_50_14_batch_size_1_beta_0.0_epochs_20_filters_32_kernel_size_6_loss_vae_loss_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# define model
vae = DenseLatentPong(input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
pre_latent_size=pre_latent_size,
latent_size=latent_size,
beta=beta)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1)
X_test_size = 100
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
sampling.encode_decode_sample(X_test, model)
def train_average_filter(beta):
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 7
epochs = 12
batch_size = 1
lr = 1e-4
# define filename
name = 'cvae_atari'
# builder hyperparameter dictionary
hp_dictionary = {
'epochs': epochs,
'batch_size': batch_size,
'beta': beta,
'filters': filters,
'latent_filters': latent_filters,
'kernel_size': kernel_size,
'lr': lr,
'loss': 'vae_loss',
'optimizer': 'adam'
}
# define log directory
log_dir = './summaries/' + experiment + '/' + utils.build_hyperparameter_string(
name, hp_dictionary) + '/'
# make VAE
vae = ConvolutionalLatentAverageFilterShallowVAE(
input_shape,
log_dir,
filters=filters,
latent_filters=latent_filters,
kernel_size=kernel_size,
beta=beta)
# compile VAE
from keras import optimizers
optimizer = optimizers.Adam(lr=lr)
vae.compile(optimizer=optimizer)
# get dataset
train_directory = './atari_agents/record/train/'
test_directory = './atari_agents/record/test/'
train_generator = utils.atari_generator(train_directory,
batch_size=batch_size)
test_generator = utils.atari_generator(test_directory,
batch_size=batch_size)
train_size = utils.count_images(train_directory)
test_size = utils.count_images(test_directory)
# print summaries
vae.print_model_summaries()
# fit VAE
steps_per_epoch = int(train_size / batch_size)
validation_steps = int(test_size / batch_size)
vae.fit_generator(train_generator,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_generator,
validation_steps=validation_steps)
def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 6
epochs = 10
batch_size = 1
lr = 1e-4
img_channels = 1
beta = 1.0
# log directory
# run = 'cvae_atari_average_filter_18_Jun_15_40_50_batch_size_1_beta_1_epochs_10_filters_32_img_channels_1_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
# run = 'cvae_atari_average_filter_18_Jun_18_07_12_batch_size_1_beta_2_epochs_10_filters_32_img_channels_1_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
run = 'cvae_atari_average_filter_18_Jun_20_33_08_batch_size_1_beta_4_epochs_10_filters_32_img_channels_1_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# make VAE
vae = ConvolutionalLatentAverageFilterShallowVAE(input_shape,
log_dir,
filters=filters,
latent_filters=latent_filters,
kernel_size=kernel_size,
img_channels=img_channels,
beta=beta)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1, shuffle=False)
X_test_size = 1000
X_test = np.asarray([next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
# for sample_number in range(4):
# sampling.encode_decode_sample(X_test, model, sample_number=sample_number, save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/naive_average/', base='beta_4_')
# plot filters
# sampling.show_convolutional_layers(X_test, encoder, 2, 4, init_sample_num=3, save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/naive_average/', base='beta_4_')
# plt.show()
# sample from prior
# sampling.decode_prior_samples(5, decoder, latent_shape=(1, 8, 8, 8), save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/naive_average/', base='beta_4_')
# plt.show()
# sample from posterior
# num_iter = 100
# sampling.sample_posterior(X_test, model, num_iter, show_every=1, save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/naive_average/', base='beta_4_')
# change latent variable
# latent_shape = (1, 8, 8, 8)
# filter_index = 0
# sampling.change_latent_filter(X_test,
# latent_shape,
# filter_index,
# encoder,
# decoder,
# num_samples=10,
# init_sample_num=0,
# noise_factor=1.0,
# std_dev=1.0,
# mean=0.0)
# plot mean activation over latent space
sampling.plot_mean_latent_activation(X_test, encoder, 2, 4, threshold=True, threshold_val=0.1)
plt.subplots_adjust(left=0.14, bottom=0.63, right=0.5, top=1, wspace=0.14, hspace=0)
plt.savefig('/home/dane/Documents/Thesis/thesis/figures/results/naive_average/' + 'beta_4_' + 'average_activation' + '.png', bbox_inches='tight')
def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 6
epochs = 10
batch_size = 1
lr = 1e-4
img_channels = 1
beta = 1.0
# log directory
run = 'cvae_atari_average_filter_19_Jun_05_05_40_batch_size_1_beta_4_epochs_10_filters_32_img_channels_1_kernel_size_6_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# make VAE
vae = LatentImage(input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
img_channels=img_channels,
beta=beta)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1, shuffle=False)
X_test_size = 1000
X_test = np.asarray([next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
for sample_number in range(4):
sampling.encode_decode_sample(X_test, model, sample_number=sample_number, save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/latent_image/', base='beta_4_')
# plt.show()
# plot filters
# for sample_number in [30, 70, 10, 90]:
# plt.figure(1)
# plt.xticks([], [])
# plt.yticks([], [])
# plt.imshow(X_test[sample_number][0])
# plt.gray()
# plt.savefig('/home/dane/Documents/Thesis/thesis/figures/results/latent_image/beta_' + str(int(beta)) + '_sample_' + str(sample_number) + '_original.png', bbox_inches='tight')
# # plt.show()
# x_encoded = encoder.predict(np.asarray([X_test[sample_number]])) # shape (1, num_filters, width, height)
# x_encoded = x_encoded[0] # shape (num_filters, width, height)
# plt.figure(2)
# plt.xticks([], [])
# plt.yticks([], [])
# plt.imshow(x_encoded[0])
# plt.gray()
# plt.savefig('/home/dane/Documents/Thesis/thesis/figures/results/latent_image/beta_' + str(int(beta)) + '_sample_' + str(sample_number) + '_latent.png', bbox_inches='tight')
# plt.show()
# sample from prior
# sampling.decode_prior_samples(4, decoder, latent_shape=(1, 1, 8, 8), save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/latent_image/', base='beta_1_')
# plt.show()
# sample from posterior
# num_iter = 1000
# sampling.sample_posterior(X_test, model, num_iter, show_every=1, save=True, save_path='/home/dane/Documents/Thesis/thesis/figures/results/latent_image/', base='beta_1_')
# change latent variable
# latent_shape = (1, 8, 8, 8)
# filter_index = 0
# sampling.change_latent_filter(X_test,
# latent_shape,
# filter_index,
# encoder,
# decoder,
# num_samples=10,
# init_sample_num=0,
# noise_factor=1.0,
# std_dev=1.0,
# mean=0.0)
# plot mean activation over latent space
sampling.plot_mean_latent_activation(X_test, encoder, 1, 1)
plt.savefig('/home/dane/Documents/Thesis/thesis/figures/results/latent_image/' + 'beta_1_' + 'average_activation' + '.png', bbox_inches='tight')
vae = FreyVAE((1, 28, 20), log_dir)
# load weights
encoder = vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
'''
Load data
'''
dataset = 'frey'
if dataset == 'atari':
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1)
X_test_size = 100
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
elif dataset == 'mnist':
# import dataset
(_, _), (X_test, _) = utils.load_keras()
elif dataset == 'frey':
# import dataset
(_, _), (X_test, _) = utils.load_frey()
else:
print("Dataset not found.")
'''
Sampling functions
'''
def train_winner_takes_all(beta):
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 6
epochs = 5
batch_size = 1
lr = 1e-4
img_channels = 1
# define filename
name = 'cvae_atari_winner_takes_all'
# builder hyperparameter dictionary
hp_dictionary = {
'epochs': epochs,
'batch_size': batch_size,
'beta': beta,
'filters': filters,
'latent_filters': latent_filters,
'kernel_size': kernel_size,
'img_channels': img_channels,
'lr': lr,
'loss': 'vae_loss',
'optimizer': 'adam'
}
# define log directory
log_dir = '/vol/bitbucket/dgs13/summaries/' + experiment + '/' + utils.build_hyperparameter_string(
name, hp_dictionary) + '/'
# make VAE
vae = WinnerTakesAll(input_shape,
log_dir,
filters=filters,
latent_filters=latent_filters,
kernel_size=kernel_size,
img_channels=img_channels,
beta=beta)
# compile VAE
from keras import optimizers
optimizer = optimizers.Adam(lr=lr)
vae.compile(optimizer=optimizer)
# get dataset
train_directory = '/vol/bitbucket/dgs13/record/train/'
test_directory = '/vol/bitbucket/dgs13/record/test/'
train_generator = utils.atari_generator(train_directory,
batch_size=batch_size,
img_channels=img_channels)
test_generator = utils.atari_generator(test_directory,
batch_size=batch_size,
img_channels=img_channels)
train_size = utils.count_images(train_directory)
test_size = utils.count_images(test_directory)
# print summaries
vae.print_model_summaries()
# fit VAE
steps_per_epoch = int(train_size / batch_size)
validation_steps = int(test_size / batch_size)
vae.fit_generator(train_generator,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_generator,
validation_steps=validation_steps)
def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
latent_filters = 8
kernel_size = 7
epochs = 10
batch_size = 1
lr = 1e-4
beta = 1.0
# log directory
run = 'cvae_atari_average_filter_30_May_20_46_38_batch_size_1_beta_128_epochs_5_filters_32_kernel_size_7_latent_filters_8_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# make VAE
vae = ConvolutionalLatentAverageFilterShallowVAE(
input_shape,
log_dir,
filters=filters,
latent_filters=latent_filters,
kernel_size=kernel_size,
beta=beta)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory,
batch_size=1,
shuffle=False)
X_test_size = 1113
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
# sampling.encode_decode_sample(X_test, model)
# plt.show()
# plot filters
# sampling.show_convolutional_layers(X_test, encoder, 4, 2, threshold=True, threshold_val=0.0)
# plt.show()
# sample from prior
# sampling.decode_prior_samples(5, decoder, latent_shape=(1, 8, 7, 7))
# plt.show()
# sample from posterior
# num_iter = 100
# sampling.sample_posterior(X_test, model, num_iter, show_every=1)
# change latent variable
# latent_shape = (1, 8, 7, 7)
# filter_index = 1
# sampling.change_latent_filter(X_test,
# latent_shape,
# filter_index,
# encoder,
# decoder,
# num_samples=10,
# init_sample_num=0,
# noise_factor=1.0,
# std_dev=1.0,
# mean=0.0)
# plot mean activation over latent space
sampling.plot_mean_latent_activation(X_test,
encoder,
4,
2,
threshold=True,
threshold_val=0.0)
plt.show()
