def loadModel2(pklname):
ishape = Conv2DSpace(shape=[48, 48], num_channels=1)
nclass = 7
# create layers
nk = [30, 40] # train3040.pkl.cpu
#nk = [32, 20, 10]
#nk = [40,30,20]
ks = [[8, 8], [5, 5], [3, 3]]
ir = [0.05, 0.05, 0.05]
ps = [[4, 4], [4, 4], [2, 2]]
pd = [[2, 2], [2, 2], [2, 2]]
kn = [0.9, 0.9, 0.9]
layers = DBL_ConvLayers(nk, ks, ir, ps, pd, kn)
layer_soft = Softmax(
layer_name='y',
#max_col_norm = 1.9365,
n_classes=nclass,
#init_bias_target_marginals=DBL.ds_train,
#istdev = .05
irange=.0)
#layers.append(layer_soft)
# create DBL_model
model = MLP(layers, input_space=ishape)
layer_params = cPickle.load(open(pklname))
layer_id = 0
for layer in model.layers:
if layer_id < len(layers) - 1:
layer.set_weights(layer_params[layer_id][0])
layer.set_biases(layer_params[layer_id][1])
layer_id = layer_id + 1
return model
def loadModel(pklname):
ishape = Conv2DSpace(
shape = [48, 48],
num_channels = 1
)
nclass = 7
# create layers
#nk = [30, 40]
nk = [32,20,10]
ks = [[8,8],[5,5],[3,3]]
ir = [0.05,0.05,0.05]
ps = [[4,4],[4,4],[2,2]]
pd = [[2,2],[2,2],[2,2]]
kn = [0.9,0.9,0.9]
layers = DBL_ConvLayers(nk,ks,ir,ps,pd,kn)
layer_soft = Softmax(
layer_name='y',
#max_col_norm = 1.9365,
n_classes = nclass,
#init_bias_target_marginals=DBL.ds_train,
#istdev = .05
irange = .0
)
layers.append(layer_soft)
# create DBL_model
model = MLP(layers, input_space=ishape)
layer_params = cPickle.load(open(pklname))
layer_id = 0
for layer in model.layers:
if layer_id < len(layers) - 1:
layer.set_weights(layer_params[layer_id][0])
layer.set_biases(layer_params[layer_id][1])
else:
layer.set_weights(layer_params[layer_id][1])
layer.set_biases(layer_params[layer_id][0])
layer_id = layer_id + 1
return model
def DBL_model_test1(basepath, cutoff=[-1, -1], pklname='', newdata=None):
# data
ishape = Conv2DSpace(shape=[48, 48], num_channels=1)
preproc = [0, 0]
nclass = 7
DBL = DBL_model(basepath, nclass, np.append(ishape.shape, 1), preproc,
cutoff)
# create layers
nk = [30]
#nk = [40,30,20]
ks = [[8, 8], [5, 5], [3, 3]]
ir = [0.05, 0.05, 0.05]
ps = [[4, 4], [4, 4], [2, 2]]
pd = [[2, 2], [2, 2], [2, 2]]
kn = [0.9, 0.9, 0.9]
layers = DBL_ConvLayers(nk, ks, ir, ps, pd, kn)
layer_soft = Softmax(
layer_name='y',
#max_col_norm = 1.9365,
n_classes=nclass,
init_bias_target_marginals=DBL.ds_train,
#istdev = .05
irange=.0)
layers.append(layer_soft)
# create DBL_model
model = MLP(layers, input_space=ishape)
if pklname != '' and os.path.isfile(pklname):
# load and rebuild model
layer_params = cPickle.load(open(pklname + '.cpu'))
layer_id = 0
for layer in model.layers:
if layer_id < len(layers) - 1:
layer.set_weights(layer_params[layer_id][0])
layer.set_biases(layer_params[layer_id][1])
else:
layer.set_weights(layer_params[layer_id][1])
layer.set_biases(layer_params[layer_id][0])
layer_id = layer_id + 1
DBL.model = model
DBL.test_raw(newdata)
else:
algo_term = EpochCounter(500) # number of epoch iteration
algo = SGD(learning_rate=0.001,
batch_size=500,
init_momentum=.5,
monitoring_dataset=DBL.ds_valid,
termination_criterion=algo_term)
DBL.run_model(model, algo)
# save the model
if pklname != '':
layer_params = []
for layer in layers:
param = layer.get_params()
print param
print param[0].get_value()
layer_params.append(
[param[0].get_value(), param[1].get_value()])
#cPickle.dump(DBL,open(pklname, 'wb'))
#cPickle.dump(layer_params, open(pklname + '.cpu', 'wb'))
cPickle.dump(layer_params, open(pklname + '.cpu', 'wb'))
print DBL.result_valid[1], DBL.result_test[1]
return DBL.result_valid[1], DBL.result_test[1]
def DBL_model_test1(basepath,cutoff=[-1,-1],pklname='',newdata=None):
# data
ishape = Conv2DSpace(
shape = [48, 48],
num_channels = 1
)
preproc=[0,0]
nclass = 7
DBL = DBL_model(basepath,nclass,np.append(ishape.shape,1),preproc,cutoff)
# create layers
nk = [30]
#nk = [40,30,20]
ks = [[8,8],[5,5],[3,3]]
ir = [0.05,0.05,0.05]
ps = [[4,4],[4,4],[2,2]]
pd = [[2,2],[2,2],[2,2]]
kn = [0.9,0.9,0.9]
layers = DBL_ConvLayers(nk,ks,ir,ps,pd,kn)
layer_soft = Softmax(
layer_name='y',
#max_col_norm = 1.9365,
n_classes = nclass,
init_bias_target_marginals=DBL.ds_train,
#istdev = .05
irange = .0
)
layers.append(layer_soft)
# create DBL_model
model = MLP(layers, input_space=ishape)
if pklname!='' and os.path.isfile(pklname):
# load and rebuild model
layer_params = cPickle.load(open(pklname + '.cpu'))
layer_id = 0
for layer in model.layers:
if layer_id < len(layers) - 1:
layer.set_weights(layer_params[layer_id][0])
layer.set_biases(layer_params[layer_id][1])
else:
layer.set_weights(layer_params[layer_id][1])
layer.set_biases(layer_params[layer_id][0])
layer_id = layer_id + 1
DBL.model = model
DBL.test_raw(newdata)
else:
algo_term = EpochCounter(500) # number of epoch iteration
algo = SGD(learning_rate = 0.001,
batch_size = 500,
init_momentum = .5,
monitoring_dataset = DBL.ds_valid,
termination_criterion=algo_term
)
DBL.run_model(model,algo)
# save the model
if pklname!='':
layer_params = []
for layer in layers:
param = layer.get_params()
print param
print param[0].get_value()
layer_params.append([param[0].get_value(), param[1].get_value()])
#cPickle.dump(DBL,open(pklname, 'wb'))
#cPickle.dump(layer_params, open(pklname + '.cpu', 'wb'))
cPickle.dump(layer_params, open(pklname + '.cpu', 'wb'))
print DBL.result_valid[1], DBL.result_test[1]
return DBL.result_valid[1], DBL.result_test[1]
