Std_skel = SK_normalization['Std1']
####################################################################
# DBN for skeleton modules
####################################################################
# ------------------------------------------------------------------------------
# symbolic variables
x_skeleton = ndtensor(len(tr._skeleon_in_shape))(
name='x_skeleton') # video input
x_skeleton_ = _shared(empty(tr._skeleon_in_shape))
dbn = GRBM_DBN(numpy_rng=random.RandomState(123), n_ins=891, \
hidden_layers_sizes=[2000, 2000, 1000], n_outs=101, input_x=x_skeleton, label=y )
# we load the pretrained DBN skeleton parameteres here
load_path = '/idiap/user/dwu/chalearn/result/try/36.7% 2015.07.09.17.53.10'
dbn.load_params_DBN(os.path.join(load_path, 'paramsbest.zip'))
test_model = function([],
dbn.logLayer.p_y_given_x,
givens={x_skeleton: x_skeleton_},
on_unused_input='ignore')
for file_count, file in enumerate(samples):
condition = (file_count > -1)
if condition: #wudi only used first 650 for validation !!! Lio be careful!
save_path = os.path.join(data, file)
print file
time_start = time()
# we load precomputed feature set or recompute the whole feature set
if os.path.isfile(save_path):
print "loading exiting file"
####################################################################
# DBN for skeleton modules
####################################################################
# ------------------------------------------------------------------------------
# symbolic variables
x_skeleton = ndtensor(len(tr._skeleon_in_shape))(name = 'x_skeleton') # video input
x_skeleton_ = _shared(empty(tr._skeleon_in_shape))
########sample number:39,hidden_layer_size=[2000,1000]->error rate=77.1%;[2000,2000,1000],78.4%
dbn = GRBM_DBN(numpy_rng=random.RandomState(123), n_ins=891, \
hidden_layers_sizes=[2000,2000,1000], n_outs=101, input_x=x_skeleton, label=y )
# we load the pretrained DBN skeleton parameteres here, currently pretraining is done
# unsupervisedly, we can load the supervised pretrainining parameters later
#when u pretain the network, comment the following line
dbn.load_params_DBN("/home/zhiquan/fancy/meterials/chalearn2014_fancy_data/result_temp/dbn/try/63.9% 2018.05.06.19.54.43/paramsbest.zip")
cost = dbn.finetune_cost
# function computing the number of errors
errors = dbn.errors
# wudi add the mean and standard deviation of the activation values to exam the neural net
# Reference: Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot, Yoshua Bengio
out_mean = T.stack(dbn.out_mean)
out_std = T.stack(dbn.out_std)
gparams = T.grad(cost, dbn.params)
# DBN for skeleton modules
####################################################################
# ------------------------------------------------------------------------------
# symbolic variables
x_skeleton = ndtensor(len(tr._skeleon_in_shape))(
name='x_skeleton') # video input
x_skeleton_ = _shared(empty(tr._skeleon_in_shape))
dbn = GRBM_DBN(numpy_rng=random.RandomState(123), n_ins=891, \
hidden_layers_sizes=[2000, 2000, 1000], n_outs=101, input_x=x_skeleton, label=y )
# we load the pretrained DBN skeleton parameteres here, currently pretraining is done
# unsupervisedly, we can load the supervised pretrainining parameters later
dbn.load_params_DBN(
"/idiap/user/dwu/chalearn/result/try/37.8% 2015.07.09.13.26.11/paramsbest.zip"
)
cost = dbn.finetune_cost
# function computing the number of errors
errors = dbn.errors
# wudi add the mean and standard deviation of the activation values to exam the neural net
# Reference: Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot, Yoshua Bengio
out_mean = T.stack(dbn.out_mean)
out_std = T.stack(dbn.out_std)
gparams = T.grad(cost, dbn.params)
params = dbn.params
Std1) # Lio changed it to read from HDF5 files
####################################################################
# DBN for skeleton modules
####################################################################
# ------------------------------------------------------------------------------
# symbolic variables
x_skeleton = ndtensor(len(tr._skeleon_in_shape))(
name='x_skeleton') # video input
x_skeleton_ = _shared(empty(tr._skeleon_in_shape))
dbn = GRBM_DBN(numpy_rng=random.RandomState(123), n_ins=891, \
hidden_layers_sizes=[2000, 2000, 1000], n_outs=101, input_x=x_skeleton, label=y )
# we load the pretrained DBN skeleton parameteres here
dbn.load_params_DBN(
'/home/zhiquan/fancy/meterials/chalearn2014_fancy_data/result_temp/dbn/try/57.6% 2018.05.06.23.42.32/paramsbest.zip'
)
####################################################################
# 3DCNN for video module
####################################################################
# we load the CNN parameteres here
use.load = True
load_path = '/home/zhiquan/fancy/meterials/chalearn2014_fancy_data/result_temp/3dcnn/try/55.0% 2018.05.07.21.06.05/'
video_cnn = conv3d_chalearn(x, use, lr, batch, net, reg, drop, mom, tr,
res_dir, load_path)
#####################################################################
# fuse the ConvNet output with skeleton output -- need to change here
######################################################################
out = T.concatenate([video_cnn.out, dbn.sigmoid_layers[-1].output], axis=1)
Mean_skel, Std_skel, Mean_CNN, Std_CNN = net_convnet3d_grbm_early_fusion.load_normalisation_constant(load_path)
####################################################################
# DBN for skeleton modules
####################################################################
# ------------------------------------------------------------------------------
# symbolic variables
x_skeleton = ndtensor(len(tr._skeleon_in_shape))(name = 'x_skeleton') # video input
x_skeleton_ = _shared(empty(tr._skeleon_in_shape))
dbn = GRBM_DBN(numpy_rng=random.RandomState(123), n_ins=891, \
hidden_layers_sizes=[2000, 2000, 1000], n_outs=101, input_x=x_skeleton, label=y )
# we load the pretrained DBN skeleton parameteres here
load_path = '/idiap/user/dwu/chalearn/result/try/36.7% 2015.07.09.17.53.10'
dbn.load_params_DBN(os.path.join(load_path,'paramsbest.zip'))
test_model = function([], dbn.logLayer.p_y_given_x,
givens={x_skeleton: x_skeleton_},
on_unused_input='ignore')
for file_count, file in enumerate(samples):
condition = (file_count > -1)
if condition: #wudi only used first 650 for validation !!! Lio be careful!
save_path= os.path.join(data, file)
print file
time_start = time()
# we load precomputed feature set or recompute the whole feature set
if os.path.isfile(save_path):
print "loading exiting file"
loader = DataLoader_with_skeleton_normalisation(src, tr.batch_size, 0, 1, Mean1, Std1) # Lio changed it to read from HDF5 files
####################################################################
# DBN for skeleton modules
####################################################################
# ------------------------------------------------------------------------------
# symbolic variables
x_skeleton = ndtensor(len(tr._skeleon_in_shape))(name = 'x_skeleton') # video input
x_skeleton_ = _shared(empty(tr._skeleon_in_shape))
dbn = GRBM_DBN(numpy_rng=random.RandomState(123), n_ins=891, \
hidden_layers_sizes=[2000, 2000, 1000], n_outs=101, input_x=x_skeleton, label=y )
# we load the pretrained DBN skeleton parameteres here, currently pretraining is done
# unsupervisedly, we can load the supervised pretrainining parameters later
dbn.load_params_DBN("/idiap/user/dwu/chalearn/result/try/37.8% 2015.07.09.13.26.11/paramsbest.zip")
cost = dbn.finetune_cost
# function computing the number of errors
errors = dbn.errors
# wudi add the mean and standard deviation of the activation values to exam the neural net
# Reference: Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot, Yoshua Bengio
out_mean = T.stack(dbn.out_mean)
out_std = T.stack(dbn.out_std)
gparams = T.grad(cost, dbn.params)