def cuda_pooling_layer(layer, last_layer):
name = layer.name
param = layer.pooling_param
ds = (param.kernel_size, param.kernel_size
) #caffe only does square kernels
stride = (param.stride, param.stride)
pool = cuda_convnet.MaxPool2DCCLayer(last_layer,
pool_size=ds,
stride=stride,
name=name)
return pool
def build_model(input_width, input_height, output_dim,
batch_size=BATCH_SIZE, dimshuffle=True):
l_in = lasagne.layers.InputLayer(
shape=(batch_size, 1, input_width, input_height),
)
if not dimshuffle:
l_in = cuda_convnet.bc01_to_c01b(l_in)
l_conv1 = cuda_convnet.Conv2DCCLayer(
l_in,
num_filters=32,
filter_size=(5, 5),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.Uniform(),
dimshuffle=dimshuffle,
)
l_pool1 = cuda_convnet.MaxPool2DCCLayer(
l_conv1,
ds=(2, 2),
dimshuffle=dimshuffle,
)
l_conv2 = cuda_convnet.Conv2DCCLayer(
l_pool1,
num_filters=32,
filter_size=(5, 5),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.Uniform(),
dimshuffle=dimshuffle,
)
l_pool2 = cuda_convnet.MaxPool2DCCLayer(
l_conv2,
ds=(2, 2),
dimshuffle=dimshuffle,
)
if not dimshuffle:
l_pool2 = cuda_convnet.c01b_to_bc01(l_pool2)
l_hidden1 = lasagne.layers.DenseLayer(
l_pool2,
num_units=256,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.Uniform(),
)
l_hidden1_dropout = lasagne.layers.DropoutLayer(l_hidden1, p=0.5)
# l_hidden2 = lasagne.layers.DenseLayer(
# l_hidden1_dropout,
# num_units=256,
# nonlinearity=lasagne.nonlinearities.rectify,
# )
# l_hidden2_dropout = lasagne.layers.DropoutLayer(l_hidden2, p=0.5)
l_out = lasagne.layers.DenseLayer(
l_hidden1_dropout,
num_units=output_dim,
nonlinearity=lasagne.nonlinearities.softmax,
W=lasagne.init.Uniform(),
)
return l_out
def build_model(input_width=96,
input_height=96,
output_dim=1,
batch_size=64,
dimshuffle=True):
l_in = lasagne.layers.InputLayer(shape=(batch_size, 3, input_width,
input_height), )
if not dimshuffle:
l_in = cuda_convnet.bc01_to_c01b(l_in)
l_conv1 = cuda_convnet.Conv2DCCLayer(
l_in,
num_filters=32,
filter_size=(3, 3),
nonlinearity=lasagne.nonlinearities.rectify,
dimshuffle=dimshuffle,
)
l_pool1 = cuda_convnet.MaxPool2DCCLayer(
l_conv1,
pool_size=(2, 2),
dimshuffle=dimshuffle,
)
l_conv2 = cuda_convnet.Conv2DCCLayer(
l_pool1,
num_filters=64,
filter_size=(2, 2),
nonlinearity=lasagne.nonlinearities.rectify,
dimshuffle=dimshuffle,
)
l_pool2 = cuda_convnet.MaxPool2DCCLayer(
l_conv2,
pool_size=(2, 2),
dimshuffle=dimshuffle,
)
l_conv3 = cuda_convnet.Conv2DCCLayer(
l_pool2,
num_filters=128,
filter_size=(2, 2),
nonlinearity=lasagne.nonlinearities.rectify,
dimshuffle=dimshuffle,
)
l_pool3 = cuda_convnet.MaxPool2DCCLayer(
l_conv3,
pool_size=(2, 2),
dimshuffle=dimshuffle,
)
if not dimshuffle:
l_poo3 = cuda_convnet.c01b_to_bc01(l_pool3)
l_hidden1 = lasagne.layers.DenseLayer(
l_pool3,
num_units=512,
nonlinearity=lasagne.nonlinearities.rectify,
)
l_hidden1_dropout = lasagne.layers.DropoutLayer(l_hidden1, p=0.5)
l_hidden2 = lasagne.layers.DenseLayer(
l_hidden1_dropout,
num_units=512,
nonlinearity=lasagne.nonlinearities.rectify,
)
l_hidden2_dropout = lasagne.layers.DropoutLayer(l_hidden2, p=0.5)
l_out = lasagne.layers.DenseLayer(
l_hidden2_dropout,
num_units=output_dim,
nonlinearity=lasagne.nonlinearities.linear,
)
return l_out