def test_tfglm_regression():
print("========== Test TensorFlowGLM for regression ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_housing()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
clf = TensorFlowGLM(
model_name="TensorFlowGLM_regression",
task='regression',
link='linear', # link function
loss='quadratic', # loss function
l2_penalty=0.0, # ridge regularization
l1_penalty=0.0, # Lasso regularization
l1_smooth=1E-5, # smoothing for Lasso regularization
l1_method='pseudo_huber', # approximation method for L1-norm
learning_rate=0.001,
random_state=random_seed())
clf.fit(x_train, y_train)
train_err = -clf.score(x_train, y_train)
test_err = -clf.score(x_test, y_test)
print("Training MSE = %.4f" % train_err)
print("Testing MSE = %.4f" % test_err)
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_tfglm_regression_gridsearch():
print(
"========== Tune parameters for TensorFlowGLM for regression =========="
)
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_housing()
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))
params = {'l1_penalty': [0.0, 0.0001], 'l2_penalty': [0.0001, 0.001, 0.01]}
ps = PredefinedSplit(test_fold=[-1] * x_train.shape[0] +
[1] * x_test.shape[0])
clf = TensorFlowGLM(
model_name="TensorFlowGLM_regression_gridsearch",
task='regression',
link='linear', # link function
loss='quadratic', # loss function
l2_penalty=0.0, # ridge regularization
l1_penalty=0.0, # Lasso regularization
l1_smooth=1E-5, # smoothing for Lasso regularization
l1_method='pseudo_huber', # approximation method for L1-norm
learning_rate=0.0001,
catch_exception=True,
random_state=random_seed())
gs = GridSearchCV(clf, params, cv=ps, n_jobs=1, refit=False, verbose=True)
gs.fit(x, y)
print("Best MSE {} @ params {}".format(-gs.best_score_, gs.best_params_))
best_clf = clone(clf).set_params(**gs.best_params_)
best_clf.fit(x_train, y_train)
train_err = -best_clf.score(x_train, y_train)
test_err = -best_clf.score(x_test, y_test)
print("Training MSE = %.4f" % train_err)
print("Testing MSE = %.4f" % test_err)
assert abs(test_err + gs.best_score_) < 1e-4
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_tfglm_logit():
print("========== Test TensorFlowGLM for binary classification ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_pima()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
clf = TensorFlowGLM(model_name="TensorFlowGLM_logit",
l1_penalty=0.0,
l2_penalty=0.0,
num_epochs=10,
random_state=random_seed())
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)
def test_tfglm_softmax_gridsearch():
print(
"========== Tune parameters for TensorFlowGLM for multiclass classification =========="
)
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))
params = {'l1_penalty': [0.0, 0.0001], 'l2_penalty': [0.0001, 0.001, 0.01]}
ps = PredefinedSplit(test_fold=[-1] * x_train.shape[0] +
[1] * x_test.shape[0])
clf = TensorFlowGLM(model_name="TensorFlowGLM_softmax_gridsearch",
link='softmax',
loss='softmax',
num_epochs=10,
catch_exception=True,
random_state=random_seed())
gs = GridSearchCV(clf, params, cv=ps, n_jobs=1, refit=False, verbose=True)
gs.fit(x, y)
print("Best error {} @ params {}".format(1 - gs.best_score_,
gs.best_params_))
best_clf = clone(clf).set_params(**gs.best_params_)
best_clf.fit(x_train, y_train)
train_err = 1.0 - best_clf.score(x_train, y_train)
test_err = 1.0 - best_clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
assert abs(test_err - (1.0 - gs.best_score_)) < 1e-4
def test_tfglm_softmax():
print(
"========== Test TensorFlowGLM for multiclass classification =========="
)
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="TensorFlowGLM_softmax",
link='softmax',
loss='softmax',
num_epochs=10,
random_state=random_seed())
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)
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)