def __init__(self, config):
ModelBase.__init__(self)
self.config = config
self.verbose = self.config['verbose']
self.name = 'alexnet'
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
n_softmax_out = config['n_softmax_out']
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4('x')
y = T.lvector('y')
rand = T.fvector('rand')
lr = T.scalar('lr')
if self.verbose: print 'AlexNet 2/16'
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(input=layer1_input,
image_shape=(3, 227, 227, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4,
padsize=0,
group=1,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=True,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(96, 27, 27, batch_size),
filter_shape=(96, 5, 5, 256),
convstride=1,
padsize=2,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.1,
lrn=True,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(256, 13, 13, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1,
padsize=1,
group=1,
poolsize=1,
poolstride=0,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1,
padsize=1,
group=2,
poolsize=1,
poolstride=0,
bias_init=0.1,
lrn=False,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1,
padsize=1,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input,
n_in=9216,
n_out=4096,
verbose=self.verbose)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output,
n_in=4096,
n_out=4096,
verbose=self.verbose)
fc_layer7 = FCLayer(input=dropout_layer6.output,
n_in=4096,
n_out=4096,
verbose=self.verbose)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output,
n_in=4096,
n_out=4096,
verbose=self.verbose)
softmax_layer8 = SoftmaxLayer(input=dropout_layer7.output,
n_in=4096,
n_out=n_softmax_out,
verbose=self.verbose)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.p_y_given_x = softmax_layer8.p_y_given_x
self.y_pred = softmax_layer8.y_pred
self.output = self.p_y_given_x
self.cost = softmax_layer8.negative_log_likelihood(y)
self.error = softmax_layer8.errors(y)
if n_softmax_out < 5:
self.error_top_5 = softmax_layer8.errors_top_x(y, n_softmax_out)
else:
self.error_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
# inputs
self.x = x
self.y = y
self.rand = rand
self.lr = lr
self.shared_x = theano.shared(
np.zeros(
(3, config['input_width'], config['input_height'],
config['file_batch_size']), # for loading large batch
dtype=theano.config.floatX),
borrow=True)
self.shared_y = theano.shared(np.zeros((config['file_batch_size'], ),
dtype=int),
borrow=True)
self.shared_lr = theano.shared(np.float32(config['learning_rate']))
# training related
self.base_lr = np.float32(config['learning_rate'])
self.step_idx = 0
self.mu = config['momentum'] # def: 0.9 # momentum
self.eta = config['weight_decay'] #0.0002 # weight decay
self.weight_types = weight_types
self.batch_size = batch_size
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]
def __init__(self, config):
ModelBase.__init__(self)
self.config = config
self.verbose = self.config['verbose']
self.name = 'alexnet'
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
n_softmax_out=config['n_softmax_out']
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4('x')
y = T.lvector('y')
rand = T.fvector('rand')
lr = T.scalar('lr')
if self.verbose: print 'AlexNet 2/16'
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x, image_shape=(3, 256, 256,
batch_size),
cropsize=227, rand=rand, mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(input=layer1_input,
image_shape=(3, 227, 227, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4, padsize=0, group=1,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=True,
lib_conv=lib_conv,
verbose = self.verbose
)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(96, 27, 27, batch_size),
filter_shape=(96, 5, 5, 256),
convstride=1, padsize=2, group=2,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=True,
lib_conv=lib_conv,
verbose = self.verbose
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(256, 13, 13, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=0,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
verbose = self.verbose
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1, padsize=1, group=2,
poolsize=1, poolstride=0,
bias_init=0.1, lrn=False,
lib_conv=lib_conv,
verbose = self.verbose
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1, padsize=1, group=2,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
verbose = self.verbose
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input,
n_in=9216,
n_out=4096,
verbose = self.verbose
)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output,
n_in=4096,
n_out=4096,
verbose = self.verbose)
fc_layer7 = FCLayer(input=dropout_layer6.output,
n_in=4096,
n_out=4096,
verbose = self.verbose
)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output,
n_in=4096,
n_out=4096,
verbose = self.verbose)
softmax_layer8 = SoftmaxLayer(input=dropout_layer7.output,
n_in=4096,
n_out=n_softmax_out,
verbose = self.verbose)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.p_y_given_x = softmax_layer8.p_y_given_x
self.y_pred = softmax_layer8.y_pred
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
if n_softmax_out < 5:
self.errors_top_5 = softmax_layer8.errors_top_x(y, n_softmax_out)
else:
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
# inputs
self.x = x
self.y = y
self.rand = rand
self.lr = lr
self.shared_x = theano.shared(np.zeros((3, config['input_width'],
config['input_height'],
config['file_batch_size']), # for loading large batch
dtype=theano.config.floatX),
borrow=True)
self.shared_y = theano.shared(np.zeros((config['file_batch_size'],),
dtype=int), borrow=True)
self.shared_lr = theano.shared(np.float32(config['learning_rate']))
# training related
self.base_lr = np.float32(config['learning_rate'])
self.step_idx = 0
self.mu = config['momentum'] # def: 0.9 # momentum
self.eta = config['weight_decay'] #0.0002 # weight decay
self.weight_types = weight_types
self.batch_size = batch_size
self.grads = T.grad(self.cost,self.params)
# shared variable for storing momentum before exchanging momentum(delta w)
self.vels = [theano.shared(param_i.get_value() * 0.)
for param_i in self.params]
# shared variable for accepting momentum during exchanging momentum(delta w)
self.vels2 = [theano.shared(param_i.get_value() * 0.)
for param_i in self.params]
self.train = None
self.get_vel = None
self.descent_vel = None
self.val = None
self.inference = None
def __init__(self, config):
self.config = config
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4('x')
y = T.lvector('y')
rand = T.fvector('rand')
print '... building the model'
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x, image_shape=(batch_size, 3, 256, 256),
cropsize=227, rand=rand, mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(input=layer1_input,
image_shape=(batch_size, 3, 227, 227),
filter_shape=(96, 3, 11, 11),
convstride=4, padsize=0, group=1,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=True,
)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(batch_size, 96, 27, 27),
filter_shape=(256, 96, 5, 5),
#convstride=1, padsize=2, group=1,
convstride=1, padsize=2, group=2,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=True,
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(batch_size, 256, 13, 13),
filter_shape=(384, 256, 3, 3),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=0,
bias_init=0.0, lrn=False,
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(batch_size, 384, 13, 13),
filter_shape=(384, 384, 3, 3),
#convstride=1, padsize=1, group=1,
convstride=1, padsize=1, group=2,
poolsize=1, poolstride=0,
bias_init=0.1, lrn=False,
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(batch_size, 384, 13, 13),
filter_shape=(256, 384, 3, 3),
#convstride=1, padsize=1, group=1,
convstride=1, padsize=1, group=2,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=False,
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(convpool_layer5.output, 2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096)
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096)
softmax_layer8 = SoftmaxLayer(
input=dropout_layer7.output, n_in=4096, n_out=1000)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
self.x = x
self.y = y
self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
def __init__(self, config):
self.config = config
batch_size = config['batch_size']
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
layers = []
params = []
weight_types = []
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x1 = T.ftensor4('x1')
x2 = T.ftensor4('x2')
y = T.lvector('y') # The ground truth to be compared with will go here
rand1 = T.fvector('rand1')
rand2 = T.fvector('rand2')
print '... building the model'
if flag_datalayer:
data_layerA = DataLayer(input=x1,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config['rand_crop'])
layer1A_input = data_layerA.output
else:
layer1A_input = x1
if flag_datalayer:
data_layerB = DataLayer(input=x2,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config['rand_crop'])
layer1B_input = data_layerB.output
else:
layer1B_input = x2
fc_layer2_input = T.concatenate(
(T.flatten(layer1A_input.dimshuffle(3, 0, 1, 2),
2), T.flatten(layer1B_input.dimshuffle(3, 0, 1, 2), 2)),
axis=1)
fc_layer2 = FCLayer(input=fc_layer2_input, n_in=154587 * 2, n_out=4096)
layers.append(fc_layer2)
params += fc_layer2.params
weight_types += fc_layer2.weight_type
dropout_layer2 = DropoutLayer(fc_layer2.output, n_in=4096, n_out=4096)
fc_layer3 = FCLayer(input=dropout_layer2.output, n_in=4096, n_out=4096)
layers.append(fc_layer3)
params += fc_layer3.params
weight_types += fc_layer3.weight_type
dropout_layer3 = DropoutLayer(fc_layer3.output, n_in=4096, n_out=4096)
# Final softmax layer
softmax_layer3 = SoftmaxLayer(
input=dropout_layer3.output, n_in=4096,
n_out=2) # Only a single binary output is required!
layers.append(softmax_layer3)
params += softmax_layer3.params
weight_types += softmax_layer3.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer3.negative_log_likelihood(y)
self.errors = softmax_layer3.errors(y)
self.errors_top_5 = softmax_layer3.errors_top_x(y, 5)
self.x1 = x1
self.x2 = x2
self.y = y
self.rand1 = rand1
self.rand2 = rand2
self.layers = layers
self.params = params
self.weight_types = weight_types
self.batch_size = batch_size
def __init__(self, config):
self.config = config
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4('x')
y = T.ivector('y')
rand = T.fvector('rand')
print '... building the model'
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x, image_shape=(3, 256, 256,
batch_size),
cropsize=227, rand=rand, mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(input=layer1_input,
image_shape=(3, 227, 227, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4, padsize=0, group=1,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=True,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(96, 27, 27, batch_size),
filter_shape=(96, 5, 5, 256),
convstride=1, padsize=2, group=2,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=True,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(256, 13, 13, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=0,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1, padsize=1, group=2,
poolsize=1, poolstride=0,
bias_init=0.1, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1, padsize=1, group=2,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096)
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096)
softmax_layer8 = SoftmaxLayer(
input=dropout_layer7.output, n_in=4096, n_out=1000)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
self.x = x
self.y = y
self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
def __init__(self, config):
self.config = config
batch_size = config.batch_size
lib_conv = config.lib_conv
group = (2 if config.grouping else 1)
LRN = (True if config.LRN else False)
print 'LRN, group', LRN, group
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
x = T.ftensor4('x')
y = T.lvector('y')
print '... building the model with ConvLib %s, LRN %s, grouping %i ' \
% (lib_conv, LRN, group)
self.layers = []
params = []
weight_types = []
layer1_input = x
convpool_layer1 = ConvPoolLayer(
input=layer1_input,
image_shape=((3, 224, 224, batch_size) if lib_conv == 'cudaconvnet'
else (batch_size, 3, 227, 227)),
filter_shape=((3, 11, 11, 96) if lib_conv == 'cudaconvnet'
else (96, 3, 11, 11)),
convstride=4,
padsize=(0 if lib_conv == 'cudaconvnet' else 3),
group=1,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=LRN,
lib_conv=lib_conv)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(
input=convpool_layer1.output,
image_shape=((96, 27, 27, batch_size) if lib_conv == 'cudaconvnet'
else (batch_size, 96, 27, 27)),
filter_shape=((96, 5, 5, 256) if lib_conv == 'cudaconvnet'
else (256, 96, 5, 5)),
convstride=1, padsize=2, group=group,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=LRN,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(
input=convpool_layer2.output,
image_shape=((256, 13, 13, batch_size) if lib_conv == 'cudaconvnet'
else (batch_size, 256, 13, 13)),
filter_shape=((256, 3, 3, 384) if lib_conv == 'cudaconvnet'
else (384, 256, 3, 3)),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=0,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(
input=convpool_layer3.output,
image_shape=((384, 13, 13, batch_size) if lib_conv == 'cudaconvnet'
else (batch_size, 384, 13, 13)),
filter_shape=((384, 3, 3, 384) if lib_conv == 'cudaconvnet'
else (384, 384, 3, 3)),
convstride=1, padsize=1, group=group,
poolsize=1, poolstride=0,
bias_init=0.1, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(
input=convpool_layer4.output,
image_shape=((384, 13, 13, batch_size) if lib_conv == 'cudaconvnet'
else (batch_size, 384, 13, 13)),
filter_shape=((384, 3, 3, 256) if lib_conv == 'cudaconvnet'
else (256, 384, 3, 3)),
convstride=1, padsize=1, group=group,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
if lib_conv == 'cudaconvnet':
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
else:
fc_layer6_input = convpool_layer5.output.flatten(2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output)
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output)
softmax_layer8 = SoftmaxLayer(
input=dropout_layer7.output, n_in=4096, n_out=1000)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
self.x = x
self.y = y
# self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
def __init__(self, config):
self.config = config
self.name = "alexnet"
batch_size = config["batch_size"]
flag_datalayer = config["use_data_layer"]
lib_conv = config["lib_conv"]
n_softmax_out = config["n_softmax_out"]
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4("x")
y = T.lvector("y")
rand = T.fvector("rand")
print "... building the model"
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(
input=x,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config["rand_crop"],
)
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(
input=layer1_input,
image_shape=(3, 227, 227, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4,
padsize=0,
group=1,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=True,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(
input=convpool_layer1.output,
image_shape=(96, 27, 27, batch_size),
filter_shape=(96, 5, 5, 256),
convstride=1,
padsize=2,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.1,
lrn=True,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(
input=convpool_layer2.output,
image_shape=(256, 13, 13, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1,
padsize=1,
group=1,
poolsize=1,
poolstride=0,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(
input=convpool_layer3.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1,
padsize=1,
group=2,
poolsize=1,
poolstride=0,
bias_init=0.1,
lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(
input=convpool_layer4.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1,
padsize=1,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096)
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096)
softmax_layer8 = SoftmaxLayer(input=dropout_layer7.output, n_in=4096, n_out=n_softmax_out)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.p_y_given_x = softmax_layer8.p_y_given_x ###############ADDED##########
self.y_pred = softmax_layer8.y_pred ########ADDED##############
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
if n_softmax_out < 5:
self.errors_top_5 = softmax_layer8.errors_top_x(y, n_softmax_out) ##########MODIFIED##########
else:
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5) ##########MODIFIED##########
self.params = params
self.x = x
self.y = y
self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
def __init__(self, config):
self.config = config
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4('x')
y = T.lvector('y')
rand = T.fvector('rand')
print '... building the model'
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x, image_shape=(3, 256, 256,
batch_size),
cropsize=224, rand=rand, mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1_1 = ConvPoolLayer(input=layer1_input,
image_shape=(3, 224, 224, batch_size),
filter_shape=(3, 3, 3, 64),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer1_1)
params += convpool_layer1_1.params
weight_types += convpool_layer1_1.weight_type
convpool_layer1_2 = ConvPoolLayer(input=convpool_layer1_1.output,
image_shape=(64, 224, 224, batch_size),
filter_shape=(64, 3, 3, 64),
convstride=1, padsize=1, group=1,
poolsize=2, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer1_2)
params += convpool_layer1_2.params
weight_types += convpool_layer1_2.weight_type
convpool_layer2_1 = ConvPoolLayer(input=convpool_layer1_2.output,
image_shape=(64, 112, 112, batch_size),
filter_shape=(64, 3, 3, 128),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer2_1)
params += convpool_layer2_1.params
weight_types += convpool_layer2_1.weight_type
convpool_layer2_2 = ConvPoolLayer(input=convpool_layer2_1.output,
image_shape=(128, 112, 112, batch_size),
filter_shape=(128, 3, 3, 128),
convstride=1, padsize=1, group=1,
poolsize=2, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer2_2)
params += convpool_layer2_2.params
weight_types += convpool_layer2_2.weight_type
convpool_layer3_1 = ConvPoolLayer(input=convpool_layer2_2.output,
image_shape=(128, 56, 56, batch_size),
filter_shape=(128, 3, 3, 256),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3_1)
params += convpool_layer3_1.params
weight_types += convpool_layer3_1.weight_type
convpool_layer3_2 = ConvPoolLayer(input=convpool_layer3_1.output,
image_shape=(256, 56, 56, batch_size),
filter_shape=(256, 3, 3, 256),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3_2)
params += convpool_layer3_2.params
weight_types += convpool_layer3_2.weight_type
convpool_layer3_3 = ConvPoolLayer(input=convpool_layer3_2.output,
image_shape=(256, 56, 56, batch_size),
filter_shape=(256, 3, 3, 256),
convstride=1, padsize=1, group=1,
poolsize=2, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3_3)
params += convpool_layer3_3.params
weight_types += convpool_layer3_3.weight_type
convpool_layer4_1 = ConvPoolLayer(input=convpool_layer3_3.output,
image_shape=(256, 28, 28, batch_size),
filter_shape=(256, 3, 3, 512),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4_1)
params += convpool_layer4_1.params
weight_types += convpool_layer4_1.weight_type
convpool_layer4_2 = ConvPoolLayer(input=convpool_layer4_1.output,
image_shape=(512, 28, 28, batch_size),
filter_shape=(512, 3, 3, 512),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4_2)
params += convpool_layer4_2.params
weight_types += convpool_layer4_2.weight_type
convpool_layer4_3 = ConvPoolLayer(input=convpool_layer4_2.output,
image_shape=(512, 28, 28, batch_size),
filter_shape=(512, 3, 3, 512),
convstride=1, padsize=1, group=1,
poolsize=2, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4_3)
params += convpool_layer4_3.params
weight_types += convpool_layer4_3.weight_type
convpool_layer5_1 = ConvPoolLayer(input=convpool_layer4_3.output,
image_shape=(512, 14, 14, batch_size),
filter_shape=(512, 3, 3, 512),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5_1)
params += convpool_layer5_1.params
weight_types += convpool_layer5_1.weight_type
convpool_layer5_2 = ConvPoolLayer(input=convpool_layer5_1.output,
image_shape=(512, 14, 14, batch_size),
filter_shape=(512, 3, 3, 512),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=1,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5_2)
params += convpool_layer5_2.params
weight_types += convpool_layer5_2.weight_type
convpool_layer5_3 = ConvPoolLayer(input=convpool_layer5_2.output,
image_shape=(512, 14, 14, batch_size),
filter_shape=(512, 3, 3, 512),
convstride=1, padsize=1, group=1,
poolsize=2, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5_3)
params += convpool_layer5_3.params
weight_types += convpool_layer5_3.weight_type
fc_layer6_input = T.flatten(convpool_layer5_3.output.dimshuffle(3, 0, 1, 2), 2)
# dimshuffle is changing the batch dimension to the first dimension
# flatten crash the output to (batch, evethingelse) 2-D array
# fc_layer6 = FCLayer(input=fc_layer6_input, n_in=224*224*64, n_out=4096)
# fc_layer6 = FCLayer(input=fc_layer6_input, n_in=401408, n_out=4096)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=25088, n_out=4096)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096)
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096)
softmax_layer8 = SoftmaxLayer(
input=dropout_layer7.output, n_in=4096, n_out=1000)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
self.x = x
self.y = y
self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
def __init__(self, config):
self.config = config
batch_size = config.batch_size
lib_conv = config.lib_conv
group = (2 if config.grouping else 1)
LRN = (True if config.LRN else False)
print 'LRN, group', LRN, group
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
x = T.ftensor4('x')
y = T.lvector('y')
print '... building the model with ConvLib %s, LRN %s, grouping %i ' \
% (lib_conv, LRN, group)
self.layers = []
params = []
weight_types = []
layer1_input = x
convpool_layer1 = ConvPoolLayer(
input=layer1_input,
image_shape=((3, 224, 224,
batch_size) if lib_conv == 'cudaconvnet' else
(batch_size, 3, 227, 227)),
filter_shape=((3, 11, 11, 96) if lib_conv == 'cudaconvnet' else
(96, 3, 11, 11)),
convstride=4,
padsize=(0 if lib_conv == 'cudaconvnet' else 3),
group=1,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=LRN,
lib_conv=lib_conv)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(
input=convpool_layer1.output,
image_shape=((96, 27, 27,
batch_size) if lib_conv == 'cudaconvnet' else
(batch_size, 96, 27, 27)),
filter_shape=((96, 5, 5, 256) if lib_conv == 'cudaconvnet' else
(256, 96, 5, 5)),
convstride=1,
padsize=2,
group=group,
poolsize=3,
poolstride=2,
bias_init=0.1,
lrn=LRN,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(
input=convpool_layer2.output,
image_shape=((256, 13, 13,
batch_size) if lib_conv == 'cudaconvnet' else
(batch_size, 256, 13, 13)),
filter_shape=((256, 3, 3, 384) if lib_conv == 'cudaconvnet' else
(384, 256, 3, 3)),
convstride=1,
padsize=1,
group=1,
poolsize=1,
poolstride=0,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(
input=convpool_layer3.output,
image_shape=((384, 13, 13,
batch_size) if lib_conv == 'cudaconvnet' else
(batch_size, 384, 13, 13)),
filter_shape=((384, 3, 3, 384) if lib_conv == 'cudaconvnet' else
(384, 384, 3, 3)),
convstride=1,
padsize=1,
group=group,
poolsize=1,
poolstride=0,
bias_init=0.1,
lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(
input=convpool_layer4.output,
image_shape=((384, 13, 13,
batch_size) if lib_conv == 'cudaconvnet' else
(batch_size, 384, 13, 13)),
filter_shape=((384, 3, 3, 256) if lib_conv == 'cudaconvnet' else
(256, 384, 3, 3)),
convstride=1,
padsize=1,
group=group,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
if lib_conv == 'cudaconvnet':
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
else:
fc_layer6_input = convpool_layer5.output.flatten(2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output)
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output)
softmax_layer8 = SoftmaxLayer(input=dropout_layer7.output,
n_in=4096,
n_out=1000)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
self.x = x
self.y = y
# self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
