def main():
# load training data
# training data can be recorded using chapter7.py in training mode
(X_train, y_train), (X_test, y_test), _, _ = homebrew.load_data(
"datasets/faces_training.pkl",
num_components=50,
test_split=0.2,
save_to_file="datasets/faces_preprocessed.pkl",
seed=42)
if len(X_train) == 0 or len(X_test) == 0:
print "Empty data"
raise SystemExit
# convert to numpy
X_train = np.squeeze(np.array(X_train)).astype(np.float32)
y_train = np.array(y_train)
X_test = np.squeeze(np.array(X_test)).astype(np.float32)
y_test = np.array(y_test)
# find all class labels
labels = np.unique(np.hstack((y_train, y_test)))
# prepare training
num_features = len(X_train[0])
num_classes = len(labels)
params = dict(term_crit=(cv2.TERM_CRITERIA_COUNT, 300, 0.01),
train_method=cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale=0.001,
bp_moment_scale=0.9)
saveFile = 'params/mlp.xml'
# find best MLP configuration
print "---"
print "1-hidden layer networks"
best_acc = 0.0 # keep track of best accuracy
for l1 in xrange(10):
# gradually increase the hidden-layer size
layerSizes = np.int32(
[num_features, (l1 + 1) * num_features / 5, num_classes])
MLP = MultiLayerPerceptron(layerSizes, labels)
print layerSizes
MLP.fit(X_train, y_train, params=params)
(acc, _, _) = MLP.evaluate(X_train, y_train)
print " - train acc = ", acc
(acc, _, _) = MLP.evaluate(X_test, y_test)
print " - test acc = ", acc
if acc > best_acc:
# save best MLP configuration to file
MLP.save(saveFile)
best_acc = acc
def main():
# load training data
# training data can be recorded using chapter7.py in training mode
(X_train, y_train), (X_test, y_test), _, _ = homebrew.load_data(
"datasets/faces_training.pkl",
num_components=50,
test_split=0.2,
save_to_file="datasets/faces_preprocessed.pkl",
seed=42)
if len(X_train) == 0 or len(X_test) == 0:
print "Empty data"
raise SystemExit
# convert to numpy
X_train = np.squeeze(np.array(X_train)).astype(np.float32)
y_train = np.array(y_train)
X_test = np.squeeze(np.array(X_test)).astype(np.float32)
y_test = np.array(y_test)
# find all class labels
labels = np.unique(np.hstack((y_train, y_test)))
# prepare training
num_features = len(X_train[0])
num_classes = len(labels)
params = dict(term_crit=(cv2.TERM_CRITERIA_COUNT, 300, 0.01),
train_method=cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale=0.001, bp_moment_scale=0.9)
saveFile = 'params/mlp.xml'
# find best MLP configuration
print "---"
print "1-hidden layer networks"
best_acc = 0.0 # keep track of best accuracy
for l1 in xrange(10):
# gradually increase the hidden-layer size
layerSizes = np.int32([num_features, (l1 + 1) * num_features/5,
num_classes])
MLP = MultiLayerPerceptron(layerSizes, labels)
print layerSizes
MLP.fit(X_train, y_train, params=params)
(acc, _, _) = MLP.evaluate(X_train, y_train)
print " - train acc = ", acc
(acc, _, _) = MLP.evaluate(X_test, y_test)
print " - test acc = ", acc
if acc > best_acc:
# save best MLP configuration to file
MLP.save(saveFile)
best_acc = acc
def main():
(X_train, y_train), (X_test, y_test), _, _ = homebrew.load_data(
load_from_folder="/datasets",
num_components=50,
test_split=0.2,
save_to_file="datasets/faces_preprocessed.pkl",
seed=42)
if len(X_train) == 0 or len(X_test) == 0:
print "Empty data"
raise SystemExit
X_train = np.squeeze(np.array(X_train)).astype(np.float32)
y_train = np.array(y_train)
X_test = np.squeeze(np.array(X_test)).astype(np.float32)
y_test = np.array(y_test)
labels = np.unique(np.hstack((y_train, y_test)))
print X_train.shape
num_features = len(X_train[0])
print num_features
num_classes = len(labels)
print num_classes
params = dict(term_crit=(cv2.TERM_CRITERIA_COUNT, 300, 0.01),
train_method=cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale=0.001,
bp_moment_scale=0.9)
saveFile = 'params/mlp.xml'
print "---"
print "1-hidden layer networks"
best_acc = 0.0
for l1 in xrange(20):
layerSizes = np.int32([
num_features, (l1 + 1) * num_features / 5,
(l1 + 1) * num_features / 10, num_classes
])
MLP = MultiLayerPerceptron(layerSizes, labels)
print layerSizes
MLP.fit(X_train, y_train, params=params)
(acc, _, _) = MLP.evaluate(X_train, y_train)
print " - train acc = ", acc
(acc, _, _) = MLP.evaluate(X_test, y_test)
print " - test acc = ", acc
if acc > best_acc:
MLP.save(saveFile)
best_acc = acc
