def __init__(self, config):
self.config = config
batch_size = config['batch_size']
num_seq = config['num_seq']
self.n_timesteps = config['num_timesteps']
num_joints = config['num_joints']
classes_num = config['classes_num']
# ##################### BUILD NETWORK ##########################
mask = T.fvector('mask')
y = T.lvector('y')
target = T.ftensor3('target')
rand = T.fvector('rand')
trng = RandomStreams(1234)
use_noise = T.fscalar('use_noise')
print '... building the model'
self.layers = []
params = []
weight_types = []
conv_fea = T.ftensor4('conv_fea') #(49, 16, 8, 1024)
lstm_att_layer15 = JointAttentionLstmLayer(config,
num_joints,
conv_fea=conv_fea,
mask=mask,
batch_size=batch_size,
num_seq=num_seq,
trng=trng,
use_noise=use_noise,
n_in=1024 * 5,
n_out=1024,
dim_part=32)
self.layers.append(lstm_att_layer15)
params += lstm_att_layer15.params
weight_types += lstm_att_layer15.weight_type
self.conv_fea = conv_fea
softmax_input = lstm_att_layer15.output
softmax_layer15 = SoftmaxLayer(input=softmax_input,
n_in=1024,
n_out=21)
self.layers.append(softmax_layer15)
params += softmax_layer15.params
weight_types += softmax_layer15.weight_type
# #################### NETWORK BUILT #######################
self.cost_nll = softmax_layer15.negative_log_likelihood(y, mask)
self.cost_jhmdb_attention = T.mean(T.sum(T.sum(
0.5 * (lstm_att_layer15.attention - target)**2, axis=1),
axis=1),
axis=0,
dtype=theano.config.floatX)
self.cost = self.cost_nll + self.cost_jhmdb_attention
self.errors_video = softmax_layer15.errors_video(
y, mask, batch_size, num_seq)
self.params = params
self.prob = softmax_layer15.p_y_given_x
self.mask = mask
self.y = y
self.target = target
self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
self.num_seq = num_seq
self.use_noise = use_noise
