def define_model(input_var, **kwargs):
""" Defines the model and returns (network, validation network output)
-Return layers.get_output(final_layer_name) if validation network output and
train network output are the same
-For example, return layers.get_output(final_layer_name, deterministic = true)
if there is a dropout layer
-Use **kwargs to pass model specific parameters
"""
image_size = 32
conv_filter_count = 100
conv_filter_size = 5
pool_size = 2
n_dense_units = 3000
input = layers.InputLayer(shape=(None, 3, image_size, image_size),
input_var=input_var)
greyscale_input = our_layers.GreyscaleLayer(
incoming=input,
random_greyscale=True,
)
conv1 = layers.Conv2DLayer(
incoming=greyscale_input,
num_filters=conv_filter_count,
filter_size=conv_filter_size,
stride=1,
nonlinearity=lasagne.nonlinearities.sigmoid,
)
pool1 = layers.MaxPool2DLayer(
incoming=conv1,
pool_size=pool_size,
stride=pool_size,
)
dense1 = layers.DenseLayer(
incoming=pool1,
num_units=n_dense_units,
nonlinearity=lasagne.nonlinearities.rectify,
)
pre_unpool1 = layers.DenseLayer(
incoming=dense1,
num_units=conv_filter_count * (image_size + conv_filter_size - 1)**2 /
(pool_size * pool_size),
nonlinearity=lasagne.nonlinearities.linear,
)
pre_unpool1 = layers.ReshapeLayer(
incoming=pre_unpool1,
shape=(input_var.shape[0], conv_filter_count) +
((image_size + conv_filter_size - 1) / 2,
(image_size + conv_filter_size - 1) / 2),
)
unpool1 = our_layers.Unpool2DLayer(
incoming=pre_unpool1,
kernel_size=2,
)
deconv1 = layers.Conv2DLayer(
incoming=unpool1,
num_filters=3,
filter_size=conv_filter_size,
stride=1,
nonlinearity=lasagne.nonlinearities.sigmoid,
)
output = layers.ReshapeLayer(incoming=deconv1, shape=input_var.shape)
return (output, layers.get_output(output))
def define_model(input_var, **kwargs):
""" Defines the model and returns (network, validation network output)
-Return layers.get_output(final_layer_name) if validation network output and
train network output are the same
-For example, return layers.get_output(final_layer_name, deterministic = true)
if there is a dropout layer
-Use **kwargs to pass model specific parameters
"""
conv1_filter_count = 100
conv1_filter_size = 5
pool1_size = 2
conv2_filter_count = 100
conv2_filter_size = 5
pool2_size = 2
n_dense_units = 3000
batch_size = input_var.shape[0]
image_size = 32
# note: ignore_border is True by defualt, so rounding down
after_pool1 = image_size // pool1_size
after_pool2 = after_pool1 // pool2_size
input = layers.InputLayer(
shape = (None, 3, image_size, image_size),
input_var = input_var
)
greyscale_input = our_layers.GreyscaleLayer(
incoming = input,
random_greyscale = True,
)
conv1 = layers.Conv2DLayer(
incoming = greyscale_input,
num_filters = conv1_filter_count,
filter_size = conv1_filter_size,
stride = 1,
pad = 'same',
nonlinearity = lasagne.nonlinearities.tanh,
)
pool1 = layers.MaxPool2DLayer(
incoming = conv1,
pool_size = pool1_size,
stride = pool1_size,
)
conv2 = layers.Conv2DLayer(
incoming = pool1,
num_filters = conv2_filter_count,
filter_size = conv2_filter_size,
stride = 1,
pad = 'same',
nonlinearity = lasagne.nonlinearities.tanh,
)
pool2 = layers.MaxPool2DLayer(
incoming = conv2,
pool_size = pool2_size,
stride = pool2_size,
)
dense1 = layers.DenseLayer(
incoming = pool2,
num_units = n_dense_units,
nonlinearity = lasagne.nonlinearities.tanh,
)
pre_unpool2 = layers.DenseLayer(
incoming = dense1,
num_units = conv2_filter_count * (after_pool2 ** 2),
nonlinearity = lasagne.nonlinearities.tanh,
)
pre_unpool2 = layers.ReshapeLayer(
incoming = pre_unpool2,
shape = (batch_size, conv2_filter_count) + (after_pool2, after_pool2),
)
unpool2 = our_layers.Unpool2DLayer(
incoming = pre_unpool2,
kernel_size = pool2_size,
)
deconv2 = layers.Conv2DLayer(
incoming = unpool2,
num_filters = conv1_filter_count,
filter_size = conv2_filter_size,
stride = 1,
pad = 'same',
nonlinearity = lasagne.nonlinearities.tanh,
)
unpool1 = our_layers.Unpool2DLayer(
incoming = deconv2,
kernel_size = pool1_size,
)
deconv1 = layers.Conv2DLayer(
incoming = unpool1,
num_filters = 3,
filter_size = conv1_filter_size,
stride = 1,
pad = 'same',
nonlinearity = lasagne.nonlinearities.tanh,
)
output = layers.ReshapeLayer(
incoming = deconv1,
shape = input_var.shape
)
return (output, layers.get_output(output))
