def test_srbm_regression(show_figure=False, block_figure_on_end=False):
print("========== Test Supervised RBM for Regression ==========")
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_mnist()
x = np.vstack([x_train, x_test])
y = np.concatenate([y_train, y_test])
learning_display = Display(
title="Learning curves",
dpi='auto',
layout=(3, 1),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['recon_err', 'val_recon_err'],
'type': 'line',
'labels': ["training recon error", "validation recon error"],
'title': "Reconstruction Errors",
'xlabel': "epoch",
'ylabel': "error",
},
{
'metrics': ['loss', 'val_loss'],
'type': 'line',
'labels': ["training loss", "validation loss"],
'title': "Learning Losses",
'xlabel': "epoch",
'ylabel': "loss",
},
{
'metrics': ['err', 'val_err'],
'type': 'line',
'labels': ["training error", "validation error"],
'title': "Prediction Errors",
'xlabel': "epoch",
'ylabel': "error",
},
# {'metrics': ['loglik_csl', 'val_loglik_csl'],
# 'type': 'line',
# 'labels': ["training loglik (CSL)", "validation loglik (CSL)"],
# 'title': "Loglikelihoods using CSL",
# 'xlabel': "epoch",
# 'ylabel': "loglik",
# },
])
filter_display = Display(title="Receptive Fields",
dpi='auto',
layout=(1, 1),
figsize=(8, 8),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['filters'],
'title': "Receptive Fields",
'type': 'img',
'num_filters': 15,
'disp_dim': (28, 28),
'tile_shape': (3, 5),
},
])
hidden_display = Display(title="Hidden Activations",
dpi='auto',
layout=(1, 1),
figsize=(8, 8),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['hidden_activations'],
'title': "Hidden Activations",
'type': 'img',
'data': x_train[:100],
},
])
early_stopping = EarlyStopping(monitor='val_loss', patience=2, verbose=1)
filepath = os.path.join(model_dir(),
"male/sRBM/mnist_{epoch:04d}_{val_loss:.6f}.pkl")
checkpoint = ModelCheckpoint(filepath,
mode='min',
monitor='val_loss',
verbose=0,
save_best_only=True)
model = SupervisedRBM(task='regression',
num_hidden=15,
num_visible=784,
batch_size=100,
num_epochs=4,
w_init=0.01,
learning_rate=0.01,
momentum_method='sudden',
weight_cost=0.0,
inference_engine='variational_inference',
approx_method='first_order',
metrics=['recon_err', 'loss', 'err'],
callbacks=[
filter_display, learning_display, hidden_display,
early_stopping, checkpoint
],
cv=[-1] * x_train.shape[0] + [0] * x_test.shape[0],
random_state=random_seed(),
verbose=1)
model.fit(x, y)
print("Test reconstruction error = %.4f" %
model.get_reconstruction_error(x_test).mean())
print("Test loss = %.4f" % model.get_loss(x_test, y_test))
print("=========== Predicted by sRBM ============")
print("Train MSE = {0:>1.4f}\tTest MSE = {1:>1.4f}".format(
-model.score(x_train, y_train), -model.score(x_test, y_test)))
# fit a Linear Regressor
lr = LinearRegression()
lr.fit(x_train, y_train)
print("=========== Predicted by Linear Regressor ============")
print("Train MSE = {0:>1.4f}\tTest MSE = {1:>1.4f}".format(
mean_squared_error(y_train, lr.predict(x_train)),
mean_squared_error(y_test, lr.predict(x_test))))