def test_save_load():
print("========== Test save, load tensorflow models ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_iris()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
clf = TensorFlowGLM(model_name="iris_TensorFlowGLM_softmax",
link='softmax',
loss='softmax',
num_epochs=5,
random_state=random_seed())
clf.fit(x_train, y_train)
print("After training:")
train_err = 1.0 - clf.score(x_train, y_train)
test_err = 1.0 - clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
save_file_path = clf.save()
clf1 = TensorFlowModel.load_model(save_file_path)
print("After save and load:")
train_err1 = 1.0 - clf1.score(x_train, y_train)
test_err1 = 1.0 - clf1.score(x_test, y_test)
print("Training error = %.4f" % train_err1)
print("Testing error = %.4f" % test_err1)
assert abs(train_err - train_err1) < 1e-6
assert abs(test_err - test_err1) < 1e-6
def test_dfm_save_and_load(show_figure=False, block_figure_on_end=False):
print("========== Test Save and Load functions of DFM on MNIST data ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_mnist()
x_train = x_train.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[{'metrics': ['d_loss', 'g_loss'],
'type': 'line',
'labels': ["discriminator loss", "generator loss"],
'title': "Losses",
'xlabel': "epoch",
'ylabel': "loss",
},
])
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[{'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = DFM(model_name="DFM_MNIST_SaveLoad",
num_z=10, # set to 100 for a full run
img_size=(28, 28, 1),
batch_size=32, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
alpha=0.03 / 10, # 0.03 / 1024
noise_std=1.0,
num_dfm_layers=1, # 2
num_dfm_hidden=10, # 1024
metrics=['d_loss', 'g_loss'],
callbacks=[loss_display, sample_display],
num_epochs=4, # set to 100 for a full run
random_state=random_seed(),
verbose=1)
model.fit(x_train)
save_file_path = model.save()
model1 = TensorFlowModel.load_model(save_file_path)
model1.num_epochs = 10
model1.fit(x_train)
def test_continue_training():
print("========== Test continue training tensorflow models ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_iris()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
num_epochs = 5
clf = TensorFlowGLM(model_name="iris_TensorFlowGLM_softmax",
link='softmax',
loss='softmax',
optimizer='sgd',
batch_size=10,
num_epochs=num_epochs,
random_state=random_seed())
clf.fit(x_train, y_train)
print("After training for {0:d} epochs".format(num_epochs))
train_err = 1.0 - clf.score(x_train, y_train)
test_err = 1.0 - clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
clf.num_epochs = 10
print("Set number of epoch to {0:d}, then continue training...".format(clf.num_epochs))
clf.fit(x_train, y_train)
train_err = 1.0 - clf.score(x_train, y_train)
test_err = 1.0 - clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
save_file_path = clf.save()
clf1 = TensorFlowModel.load_model(save_file_path)
clf1.num_epochs = 15
print("Save, load, set number of epoch to {0:d}, "
"then continue training...".format(clf1.num_epochs))
clf1.fit(x_train, y_train)
train_err = 1.0 - clf1.score(x_train, y_train)
test_err = 1.0 - clf1.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
def test_dcgan_cifar10_inception_metric(show_figure=False,
block_figure_on_end=False):
print(
"========== Test DCGAN with Inception Score and FID on CIFAR10 data =========="
)
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/DCGAN/cifar10")
checkpoints_is = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_is/the_best_is.ckpt"),
mode='max',
monitor='inception_score',
verbose=1,
save_best_only=True)
checkpoints_fid = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_fid/the_best_fid.ckpt"),
mode='min',
monitor='FID',
verbose=1,
save_best_only=True)
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir,
"loss/loss_{epoch:04d}.png"),
os.path.join(root_dir,
"loss/loss_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
inception_score_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.png"),
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['inception_score'],
'type': 'line',
'labels': ["Inception Score"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
fid_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.png"),
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['FID'],
'type': 'line',
'labels': ["FID"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
filepath=os.path.join(
root_dir, "samples/samples_{epoch:04d}.png"),
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = DCGAN(
model_name="DCGAN_CIFAR10",
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=16, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=[
'd_loss', 'g_loss', 'inception_score', 'inception_score_std', 'FID'
],
callbacks=[
loss_display, inception_score_display, fid_display, sample_display,
checkpoints_is, checkpoints_fid
],
num_epochs=4, # set to 100 for a full run
inception_metrics=[InceptionScore(),
FID(data="cifar10")],
inception_metrics_freq=1,
# summary_freq=1, # uncomment this for a full run
log_path=os.path.join(root_dir, "logs"),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
filepath = os.path.join(root_dir, "checkpoints_fid/the_best_fid.ckpt")
print("Reloading the latest model at: {}".format(filepath))
model1 = TensorFlowModel.load_model(filepath)
model1.inception_metrics = InceptionMetricList(
[InceptionScore(), FID(data="cifar10")])
model1.num_epochs = 6
model1.fit(x_train)
print("Done!")
def test_dcgan_save_and_load(show_figure=False, block_figure_on_end=False):
print(
"========== Test Save and Load functions of DCGAN on MNIST data =========="
)
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_mnist()
x_train = x_train.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/DCGAN/MNIST")
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = DCGAN(
model_name="DCGAN_MNIST_SaveLoad",
num_z=10, # set to 100 for a full run
img_size=(28, 28, 1),
batch_size=16, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[loss_display, sample_display],
num_epochs=2, # set to 100 for a full run
log_path=os.path.join(root_dir, "logs"),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
print("Saving model...")
save_file_path = model.save(os.path.join(root_dir, "checkpoints/ckpt"))
print("Reloading model...")
model1 = TensorFlowModel.load_model(save_file_path)
model1.num_epochs = 4
model1.fit(x_train)
print("Done!")
def test_wgan_gp_resnet_cifar10(show_figure=False, block_figure_on_end=False):
print("========== Test WGAN-GP-ResNet on CIFAR10 data ==========")
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/WGAN-GP-ResNet/CIFAR10")
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir,
"loss/loss_{epoch:04d}.png"),
os.path.join(root_dir,
"loss/loss_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = WGAN_GP_ResNet(
model_name="WGAN_GP_ResNet_CIFAR10",
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=16, # set to 64 for a full run
g_blocks=('up', 'up', 'up'),
d_blocks=('down', 'down', None, None),
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[loss_display, sample_display],
num_epochs=2, # set to 100 for a full run
random_state=random_seed(),
log_path=os.path.join(root_dir, "logs"),
verbose=1)
model.fit(x_train)
print("Saving model...")
save_file_path = model.save(os.path.join(root_dir, "checkpoints/ckpt"))
print("Reloading model...")
model1 = TensorFlowModel.load_model(save_file_path)
model1.num_epochs = 4
model1.fit(x_train)
print("Done!")
def test_wgan_gp_resnet_cifar10_inception_metric(show_figure=False,
block_figure_on_end=False):
print(
"========== Test WGAN-GP-ResNet with Inception Score and FID on CIFAR10 data =========="
)
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
# uncomment for full run
'''
import pickle
from male.configs import data_dir
tmp = pickle.load(open(os.path.join(data_dir(), "cifar10/cifar10_train.pkl"), "rb"))
x_train = tmp['data'].astype(np.float32).reshape(
[-1, 32, 32, 3]) / 127.5 - 1.
'''
root_dir = os.path.join(model_dir(), "male/WGAN-GP-ResNet/CIFAR10")
checkpoints_is = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_is/the_best_is.ckpt"),
mode='max',
monitor='inception_score',
verbose=1,
save_best_only=True)
checkpoints_fid = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_fid/the_best_fid.ckpt"),
mode='min',
monitor='FID',
verbose=1,
save_best_only=True)
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir,
"loss/loss_{epoch:04d}.png"),
os.path.join(root_dir,
"loss/loss_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
inception_score_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.png"),
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['inception_score'],
'type': 'line',
'labels': ["Inception Score"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
fid_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.png"),
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['FID'],
'type': 'line',
'labels': ["FID"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
filepath=os.path.join(
root_dir, "samples/samples_{epoch:04d}.png"),
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = WGAN_GP_ResNet(
model_name="WGAN_GP_ResNet_CIFAR10",
num_z=8, # set to 128 for a full run
img_size=(32, 32, 3),
batch_size=64, # set to 64 for a full run
g_blocks=('up', 'up', 'up'),
d_blocks=('down', 'down', None, None),
num_gen_feature_maps=8, # set to 128 for a full run
num_dis_feature_maps=8, # set to 128 for a full run
metrics=[
'd_loss', 'g_loss', 'inception_score', 'inception_score_std', 'FID'
],
callbacks=[
loss_display, inception_score_display, fid_display, sample_display,
checkpoints_is, checkpoints_fid
],
num_epochs=2, # set to 500 for a full run
inception_metrics=[InceptionScore(),
FID(data='cifar10')],
inception_metrics_freq=1,
num_inception_samples=100, # set to 50000 for a full run
# summary_freq=1, # uncomment this for a full run
log_path=os.path.join(root_dir, 'logs'),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
filepath = os.path.join(root_dir, 'checkpoints_fid/the_best_fid.ckpt')
print('Reloading the latest model at: {}'.format(filepath))
model1 = TensorFlowModel.load_model(filepath)
model1.inception_metrics = InceptionMetricList(
[InceptionScore(), FID(data='cifar10')])
model1.num_epochs = 4
model1.fit(x_train)
print('Done!')
def test_tfglm_save_load(show=False, block_figure_on_end=False):
print(
"========== Test Save and Load functions for TensorFlowGLM ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_iris()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
x = np.vstack([x_train, x_test])
y = np.concatenate([y_train, y_test])
early_stopping = EarlyStopping(monitor='val_err', patience=5, verbose=1)
filepath = os.path.join(
model_dir(), "male/TensorFlowGLM/iris_{epoch:04d}_{val_err:.6f}.pkl")
checkpoint = ModelCheckpoint(filepath,
mode='min',
monitor='val_err',
verbose=0,
save_best_only=True)
loss_display = Display(title="Learning curves",
dpi='auto',
layout=(3, 1),
freq=1,
show=show,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['loss', 'val_loss'],
'type': 'line',
'labels':
["training loss", "validation loss"],
'title': "Learning losses",
'xlabel': "epoch",
'ylabel': "loss",
},
{
'metrics': ['err', 'val_err'],
'type': 'line',
'title': "Learning errors",
'xlabel': "epoch",
'ylabel': "error",
},
{
'metrics': ['err'],
'type': 'line',
'labels': ["training error"],
'title': "Learning errors",
'xlabel': "epoch",
'ylabel': "error",
},
])
weight_display = Display(title="Filters",
dpi='auto',
layout=(1, 1),
figsize=(6, 15),
freq=1,
show=show,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['weights'],
'title': "Learned weights",
'type': 'img',
'disp_dim': (2, 2),
'tile_shape': (3, 1),
},
])
clf = TensorFlowGLM(
model_name="TensorFlowGLM_softmax_cv",
link='softmax',
loss='softmax',
optimizer='sgd',
num_epochs=4,
batch_size=10,
task='classification',
metrics=['loss', 'err'],
callbacks=[early_stopping, checkpoint, loss_display, weight_display],
cv=[-1] * x_train.shape[0] + [0] * x_test.shape[0],
random_state=random_seed(),
verbose=1)
clf.fit(x, y)
train_err = 1.0 - clf.score(x_train, y_train)
test_err = 1.0 - clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
save_file_path = os.path.join(model_dir(),
"male/TensorFlowGLM/saved_model.ckpt")
clf.save(file_path=save_file_path)
clf1 = TensorFlowModel.load_model(save_file_path)
clf1.num_epochs = 10
clf1.fit(x, y)
train_err = 1.0 - clf1.score(x_train, y_train)
test_err = 1.0 - clf1.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
def test_gan_save_and_load(show_figure=False, block_figure_on_end=False):
print(
"========== Test Save and Load functions of GAN on MNIST data =========="
)
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_mnist()
x_train = x_train.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/GAN/MNIST")
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = GAN(model_name="GAN_MNIST_SaveLoad",
num_x=784,
num_discriminator_hiddens=(16, ),
discriminator_batchnorm=False,
discriminator_act_funcs=('lrelu', ),
discriminator_learning_rate=0.001,
num_z=8,
generator_distribution=Uniform(low=(-1.0, ) * 8,
high=(1.0, ) * 8),
generator_batchnorm=False,
num_generator_hiddens=(16, ),
generator_act_funcs=('lrelu', ),
generator_out_func='sigmoid',
generator_learning_rate=0.001,
batch_size=32,
metrics=['d_loss', 'g_loss'],
callbacks=[loss_display, sample_display],
num_epochs=2,
log_path=os.path.join(root_dir, "logs"),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
print("Saving model...")
save_file_path = model.save(os.path.join(root_dir, "checkpoints/ckpt"))
print("Reloading model...")
model1 = TensorFlowModel.load_model(save_file_path)
model1.num_epochs = 4
model1.fit(x_train)
print("Done!")