import gmllib.dataset as ds
import gmllib.neural_networks as nn
import gmllib.helpers as hlp
if __name__ == '__main__':
dataset = ds.DataSet(name='xor', trainx='xor_train_x.npy', trainy='xor_train_y.npy',
validationx='xor_val_x.npy', validationy='xor_val_y.npy',
testx='xor_test_x.npy', testy='xor_test_y.npy')
"""
train_x_gpu = nn.gnp.garray(hlp.normalize_dataset(train_x))
test_x_gpu = nn.gnp.garray(hlp.normalize_dataset(test_x))
val_x_gpu = nn.gnp.garray(hlp.normalize_dataset(validation_x))
train_y_gpu = nn.gnp.garray(train_vy)
test_y_gpu = nn.gnp.garray(test_vy)
val_y_gpu = nn.gnp.garray(validation_vy)
"""
ws_vh, ws_v, ws_h, h, error = nn.dbn_train(dataset.train.x, H=[10,10], batch_size=100,
epoch_count=50, epsilon=0.1, momentum=.95, return_hidden=False, verbose=True)
# nn.dbn_save(ws_vh, ws_v, ws_h, path='./datasets/synthetic_xor/', file_prefix='unsupervised_')
w, b, val_pred, err = nn.dbn_supervised_finetune(ws_vh, ws_h, dataset=dataset, batch_size=1, epoch_count=10, epsilon=0.1,
momentum=0.9, stop_if_val_error_increase=False, verbose=True)
h = nn.nn_forward_pass(dataset.test.x.T, w, b)
test_err = hlp.calculate_classification_error(dataset.test.y, h[-1].T)
print('Test set classification error: ' + repr(test_err))
## j, val_h = nn.dbn_forward_pass(ws_vh, ws_v, ws_h, x=val_x_gpu)
## j, test_h = nn.dbn_forward_pass(ws_vh, ws_v, ws_h, x=test_x_gpu)
## hlp.save_dataset('mnist_dbn_unsup', './', train_x=train_h, val_x=val_h, test_x=test_h)
##
## # code for loading the dbn back from disk
## # ws_vh, ws_v, ws_h = nn.dbn_load(layer_count=3, path='./', file_prefix='unsup_')
## # ------- TRAIN DBN (UNSUPERVISED) END-------------------------------------#
# ---- TEST DBN (SUPERVISED) ---------------------------------------------#
# use DBN to predict outputs for test set
# load supervised dbn
ws_vh, ws_v, ws_h = nn.dbn_load(layer_count=3, path='./', file_prefix='')
test_pred_h = nn.dbn_supervised_predict_sample(ws_vh, ws_v, ws_h, test_x_gpu, k=20)
test_err_sup_s = hlp.calculate_classification_error(test_y_gpu, test_pred_h)
print('Test set classification error with supervised DBN (sample): ' + repr(test_err_sup_s))
# error about 13%
test_pred_h = nn.dbn_supervised_predict_exact(ws_vh, ws_v, ws_h, test_x_gpu)
test_err_sup_e = hlp.calculate_classification_error(test_y_gpu, test_pred_h)
print('Test set classification error with supervised DBN (exact): ' + repr(test_err_sup_e))
# error about 5.5%
# ---- TEST DBN (SUPERVISED) END------------------------------------------#
## # ----TEST DBN (UNSUPERVISED) --------------------------------------------#
## # train a single layer neural network on the learned latent features (output of DBN)
##
## # What we do and don't do here:
## # Now we have trained a DBN on MNIST, we have extracted latent features
# 14 Feb 2014
#
# Goker Erdogan
import gmllib.dataset as ds
import gmllib.neural_networks as nn
import gmllib.helpers as hlp
if __name__ == '__main__':
# multilayer feedforward neural network example on synthetic xor data
dataset = ds.DataSet(name='xor',
trainx='xor_train_x.npy',
trainy='xor_train_y.npy',
validationx='xor_val_x.npy',
validationy='xor_val_y.npy',
testx='xor_test_x.npy',
testy='xor_test_y.npy')
# if you increase batch size, increasing momentum and epoch_count seems to work
# epsilon does not seem to need adjusting. 0.1 is good.
# H=2 also works
w, b, val_pred, err = nn.nn_train(dataset,
H=[4],
epoch_count=10,
epsilon=.1,
batch_size=1,
momentum=0.9)
h = nn.nn_forward_pass(dataset.test.x.T, w, b)
test_err = hlp.calculate_classification_error(dataset.test.y, h[-1].T)
print('Test set classification error: ' + repr(test_err))
## # code for loading the dbn back from disk
## # ws_vh, ws_v, ws_h = nn.dbn_load(layer_count=3, path='./', file_prefix='unsup_')
## # ------- TRAIN DBN (UNSUPERVISED) END-------------------------------------#
# ---- TEST DBN (SUPERVISED) ---------------------------------------------#
# use DBN to predict outputs for test set
# load supervised dbn
ws_vh, ws_v, ws_h = nn.dbn_load(layer_count=3, path='./', file_prefix='')
test_pred_h = nn.dbn_supervised_predict_sample(ws_vh,
ws_v,
ws_h,
test_x_gpu,
k=20)
test_err_sup_s = hlp.calculate_classification_error(
test_y_gpu, test_pred_h)
print('Test set classification error with supervised DBN (sample): ' +
repr(test_err_sup_s))
# error about 13%
test_pred_h = nn.dbn_supervised_predict_exact(ws_vh, ws_v, ws_h,
test_x_gpu)
test_err_sup_e = hlp.calculate_classification_error(
test_y_gpu, test_pred_h)
print('Test set classification error with supervised DBN (exact): ' +
repr(test_err_sup_e))
# error about 5.5%
# ---- TEST DBN (SUPERVISED) END------------------------------------------#
## # ----TEST DBN (UNSUPERVISED) --------------------------------------------#
## # train a single layer neural network on the learned latent features (output of DBN)
