def build_model(input_width, input_height, output_dim,
batch_size=BATCH_SIZE):
l_in = lasagne.layers.InputLayer(
shape=(batch_size, 1, input_width, input_height),
)
l_conv1 = dnn.Conv2DDNNLayer(
l_in,
num_filters=32,
filter_size=(5, 5),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),
)
l_pool1 = dnn.MaxPool2DDNNLayer(l_conv1, pool_size=(2, 2))
l_conv2 = dnn.Conv2DDNNLayer(
l_pool1,
num_filters=32,
filter_size=(5, 5),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),
)
l_pool2 = dnn.MaxPool2DDNNLayer(l_conv2, pool_size=(2, 2))
l_hidden1 = lasagne.layers.DenseLayer(
l_pool2,
num_units=256,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),
)
l_hidden1_dropout = lasagne.layers.DropoutLayer(l_hidden1, p=0.5)
l_out = lasagne.layers.DenseLayer(
l_hidden1_dropout,
num_units=output_dim,
nonlinearity=lasagne.nonlinearities.softmax,
W=lasagne.init.GlorotUniform(),
)
return l_out
def build_densenet(l_in,
input_var=None,
first_output=64,
growth_rate=32,
num_blocks=4,
dropout=0):
"""
Creates a DenseNet model in Lasagne.
Parameters
----------
input_shape : tuple
The shape of the input layer, as ``(batchsize, channels, rows, cols)``.
Any entry except ``channels`` can be ``None`` to indicate free size.
input_var : Theano expression or None
Symbolic input variable. Will be created automatically if not given.
classes : int
The number of classes of the softmax output.
first_output : int
Number of channels of initial convolution before entering the first
dense block, should be of comparable size to `growth_rate`.
growth_rate : int
Number of feature maps added per layer.
num_blocks : int
Number of dense blocks (defaults to 3, as in the original paper).
dropout : float
The dropout rate. Set to zero (the default) to disable dropout.
batchsize : int or None
The batch size to build the model for, or ``None`` (the default) to
allow any batch size.
inputsize : int, tuple of int or None
Returns
-------
network : Layer instance
Lasagne Layer instance for the output layer.
References
----------
.. [1] Gao Huang et al. (2016):
Densely Connected Convolutional Networks.
https://arxiv.org/abs/1608.06993
"""
nb_layers = [6, 12, 32, 32] # For DenseNet-169
nb_layers = [6, 12, 24, 16] # For DenseNet-121
# initial convolution
network = Conv2DLayer(l_in,
first_output,
filter_size=7,
stride=2,
pad='same',
W=lasagne.init.HeNormal(gain='relu'),
b=None,
nonlinearity=None,
name='pre_conv')
network = BatchNormLayer(network, name='pre_bn', beta=None, gamma=None)
network = ScaleLayer(network, name='pre_scale')
network = BiasLayer(network, name='pre_shift')
network = dnn.MaxPool2DDNNLayer(network, pool_size=3, stride=2)
# note: The authors' implementation does *not* have a dropout after the
# initial convolution. This was missing in the paper, but important.
# if dropout:
# network = DropoutLayer(network, dropout)
# dense blocks with transitions in between
for b in range(num_blocks):
network = dense_block(network,
nb_layers[b],
growth_rate,
dropout,
name_prefix='block%d' % (b + 1))
if b < num_blocks - 1:
network = transition(network,
dropout,
name_prefix='block%d_trs' % (b + 1))
# post processing until prediction
network = ScaleLayer(network, name='post_scale')
network = BiasLayer(network, name='post_shift')
network = NonlinearityLayer(network,
nonlinearity=rectify,
name='post_relu')
return network