def build_model(self):
if self.verbose: print 'Customized MLP'
# start graph construction from scratch
self.x = T.ftensor4('x')
self.y = T.lvector('y')
x_shuffled = self.x.dimshuffle(3, 0, 1, 2) # c01b to bc01
# bc01 from now on
flatten = Flatten(
input=x_shuffled, #5
input_shape=(self.batch_size, self.channels, self.input_width,
self.input_height), # (b, 3, 224, 224)
axis=2, # expand dimensions after the first dimension
printinfo=self.verbose
#output_shape = (b,64*64*3)
)
fc_4096 = FC(input=flatten,
n_out=4096,
W=Normal((flatten.output_shape[1], 4096), std=0.001),
b=Constant((4096, ), val=0.01),
printinfo=self.verbose
#input_shape = flatten.output_shape # (b, 9216)
)
dropout = Dropout(input=fc_4096,
n_out=fc_4096.output_shape[1],
prob_drop=0.5,
printinfo=self.verbose
#input_shape = fc_4096.output_shape # (b, 4096)
)
fc_4096 = FC(input=dropout,
n_out=4096,
W=Normal((dropout.output_shape[1], 4096), std=0.005),
b=Constant((4096, ), val=0.01),
printinfo=self.verbose
#input_shape = dropout.output_shape # (b, 4096)
)
dropout = Dropout(input=fc_4096,
n_out=fc_4096.output_shape[1],
prob_drop=0.5,
printinfo=self.verbose
#input_shape = fc_4096.output_shape # (b, 4096)
)
softmax = Softmax(input=dropout,
n_out=self.n_softmax_out,
W=Normal(
(dropout.output_shape[1], self.n_softmax_out),
std=0.005),
b=Constant((self.n_softmax_out, ), val=0),
printinfo=self.verbose
#input_shape = dropout.output_shape # (b, 4096)
)
self.output_layer = softmax
def set_dropout_on(self):
Dropout.SetDropoutOn()
def set_dropout_off(self):
Dropout.SetDropoutOff()
def build_model(self):
if self.verbose: print 'VGGNet_16 3/20'
self.name = 'vggnet'
# input shape in c01b
self.channels = 3 # 'c' mean(R,G,B) = (103.939, 116.779, 123.68)
self.input_width = self.config[
'input_width'] # '0' single scale training 224
self.input_height = self.config[
'input_height'] # '1' single scale training 224
self.batch_size = self.config['batch_size'] # 'b'
b = self.batch_size
# output dimension
self.n_softmax_out = self.config['n_softmax_out']
# start graph construction from scratch
self.x = T.ftensor4('x')
self.y = T.lvector('y')
x_shuffled = self.x.dimshuffle(3, 0, 1, 2) # c01b to bc01
layers = []
params = []
weight_types = [] # for distinguishing w and b later
# bc01 from now on
conv_3x3 = Conv(
input=x_shuffled,
input_shape=(b, self.channels, self.input_width,
self.input_height), # (b, 3, 224, 224)
convstride=1,
padsize=1,
W=Normal((64, self.channels, 3, 3), std=0.3), # bc01
b=Constant((64, ), val=0.2),
printinfo=self.verbose
#output_shape = (b, 64, 224, 224)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=pool_2x2.output_shape, # (b, 64, 112, 112)
convstride=1,
padsize=1,
W=Normal((64, conv_3x3.output_shape[1], 3, 3), std=0.1), # bc01
b=Constant((64, ), val=0.1),
printinfo=self.verbose
#output_shape = (b, 128, 112, 112)
)
pool_2x2 = Pool(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 64, 224, 224)
poolsize=2,
poolstride=2,
poolpad=0,
mode='max',
printinfo=self.verbose
#output_shape = (b, 64, 112, 112)
)
conv_3x3 = Conv(
input=pool_2x2,
#input_shape=pool_2x2.output_shape, # (b, 64, 112, 112)
convstride=1,
padsize=1,
W=Normal((128, pool_2x2.output_shape[1], 3, 3), std=0.1), # bc01
b=Constant((128, ), val=0.02),
printinfo=self.verbose
#output_shape = (b, 128, 112, 112)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=pool_2x2.output_shape, # (b, 64, 112, 112)
convstride=1,
padsize=1,
W=Normal((128, conv_3x3.output_shape[1], 3, 3), std=0.1), # bc01
b=Constant((128, ), val=0.02),
printinfo=self.verbose
#output_shape = (b, 128, 112, 112)
)
pool_2x2 = Pool(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 128, 112, 112)
poolsize=2,
poolstride=2,
poolpad=0,
mode='max',
printinfo=self.verbose
#output_shape = (b, 128, 56, 56)
)
conv_3x3 = Conv(
input=pool_2x2,
#input_shape=pool_2x2.output_shape, # (b, 128, 56, 56)
convstride=1,
padsize=1,
W=Normal((256, pool_2x2.output_shape[1], 3, 3), std=0.05), # bc01
b=Constant((256, ), val=0.02),
printinfo=self.verbose
#output_shape = (b, 256, 56, 56)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 256, 56, 56)
convstride=1,
padsize=1,
W=Normal((256, conv_3x3.output_shape[1], 3, 3), std=0.05), # bc01
b=Constant((256, ), val=0.01),
printinfo=self.verbose
#output_shape = (b, 256, 56, 56)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 256, 56, 56)
convstride=1,
padsize=1,
W=Normal((256, conv_3x3.output_shape[1], 3, 3), std=0.05), # bc01
b=Constant((256, ), val=0.01),
printinfo=self.verbose
#output_shape = (b, 256, 56, 56)
)
pool_2x2 = Pool(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 256, 56, 56)
poolsize=2,
poolstride=2,
poolpad=0,
mode='max',
printinfo=self.verbose
#output_shape = (b, 256, 28, 28)
)
conv_3x3 = Conv(
input=pool_2x2,
#input_shape=pool_2x2.output_shape, # (b, 256, 28, 28)
convstride=1,
padsize=1,
W=Normal((512, pool_2x2.output_shape[1], 3, 3), std=0.05), # bc01
b=Constant((512, ), val=0.02),
printinfo=self.verbose
#output_shape = (b, 512, 28, 28)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 512, 28, 28)
convstride=1,
padsize=1,
W=Normal((512, conv_3x3.output_shape[1], 3, 3), std=0.01), # bc01
b=Constant((512, ), val=0.01),
printinfo=self.verbose
#output_shape = (b, 512, 28, 28)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 512, 28, 28)
convstride=1,
padsize=1,
W=Normal((512, conv_3x3.output_shape[1], 3, 3), std=0.01), # bc01
b=Constant((512, ), val=0.01),
printinfo=self.verbose
#output_shape = (b, 512, 28, 28)
)
pool_2x2 = Pool(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 512, 28, 28)
poolsize=2,
poolstride=2,
poolpad=0,
mode='max',
printinfo=self.verbose
#output_shape = (b, 512, 14, 14)
)
conv_3x3 = Conv(
input=pool_2x2,
#input_shape=pool_2x2.output_shape, # (b, 512, 14, 14)
convstride=1,
padsize=1,
W=Normal((512, pool_2x2.output_shape[1], 3, 3), std=0.005), # bc01
b=Constant((512, )),
printinfo=self.verbose
#output_shape = (b, 512, 14, 14)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 512, 14, 14)
convstride=1,
padsize=1,
W=Normal((512, conv_3x3.output_shape[1], 3, 3), std=0.005), # bc01
b=Constant((512, )),
printinfo=self.verbose
#output_shape = (b, 512, 14, 14)
)
conv_3x3 = Conv(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 512, 14, 14)
convstride=1,
padsize=1,
W=Normal((512, conv_3x3.output_shape[1], 3, 3), std=0.005), # bc01
b=Constant((512, )),
printinfo=self.verbose
#output_shape = (b, 512, 14, 14)
)
pool_2x2 = Pool(
input=conv_3x3,
#input_shape=conv_3x3.output_shape, # (b, 512, 14, 14)
poolsize=2,
poolstride=2,
poolpad=0,
mode='max',
printinfo=self.verbose
#output_shape = (b, 512, 7, 7)
)
flatten = Flatten(
input=pool_2x2, #5
#input_shape = pool_2x2.output_shape, # (b, 512, 7, 7)
axis=2, # expand dimensions after the first dimension
printinfo=self.verbose
#output_shape = (b, 25088)
)
fc_4096 = FC(input=flatten,
n_out=4096,
W=Normal((flatten.output_shape[1], 4096), std=0.001),
b=Constant((4096, ), val=0.01),
printinfo=self.verbose
#input_shape = flatten.output_shape # (b, 25088)
)
dropout = Dropout(input=fc_4096,
n_out=fc_4096.output_shape[1],
prob_drop=0.5,
printinfo=self.verbose
#input_shape = fc_4096.output_shape # (b, 4096)
)
fc_4096 = FC(input=dropout,
n_out=4096,
W=Normal((dropout.output_shape[1], 4096), std=0.005),
b=Constant((4096, ), val=0.01),
printinfo=self.verbose
#input_shape = dropout.output_shape # (b, 4096)
)
dropout = Dropout(input=fc_4096,
n_out=fc_4096.output_shape[1],
prob_drop=0.5,
printinfo=self.verbose
#input_shape = fc_4096.output_shape # (b, 4096)
)
softmax = Softmax(input=dropout,
n_out=self.n_softmax_out,
W=Normal(
(dropout.output_shape[1], self.n_softmax_out),
std=0.005),
b=Constant((self.n_softmax_out, ), val=0),
printinfo=self.verbose
#input_shape = dropout.output_shape # (b, 4096)
)
self.output_layer = softmax
self.output = self.output_layer.output
self.layers = get_layers(lastlayer=self.output_layer)
self.layers = [layer for layer in self.layers \
if layer.name not in ['LRN\t','Pool\t','Flatten\t','Dropout'+ str(0.5)]]
self.params, self.weight_types = get_params(self.layers)
# training related
self.base_lr = np.float32(self.config['learning_rate'])
self.shared_lr = theano.shared(self.base_lr)
self.step_idx = 0
self.mu = self.config['momentum'] # def: 0.9 # momentum
self.eta = self.config['weight_decay'] #0.0002 # weight decay
self.shared_x = theano.shared(np.zeros(
(3, self.input_width, self.input_height,
self.config['file_batch_size']),
dtype=theano.config.floatX),
borrow=True)
self.shared_y = theano.shared(np.zeros(
(self.config['file_batch_size'], ), dtype=int),
borrow=True)
self.grads = T.grad(self.cost, self.params)
subb_ind = T.iscalar('subb') # sub batch index
#print self.shared_x[:,:,:,subb_ind*self.batch_size:(subb_ind+1)*self.batch_size].shape.eval()
self.subb_ind = subb_ind
self.shared_x_slice = self.shared_x[:, :, :, subb_ind *
self.batch_size:(subb_ind + 1) *
self.batch_size]
self.shared_y_slice = self.shared_y[subb_ind *
self.batch_size:(subb_ind + 1) *
self.batch_size]