def __init__(self, model_type='SparseFilter', weight_dims=(100, 256), layer_input=None,
p=None, group_size=None, step=None, lr=0.01, c='n', weights=None):
"""
Builds a layer for the network by constructing a model.
Parameters:
----------
model_type : str
The model type to build into a given layer.
weight_dims : list of tuples
The dimensions of the weight matrices for each layer.
fully connected: [neurons x input_dim ^ 2]
convolutional: [filters x dim x dim]
layer_input : ndarray (symbolic Theano variable)
The input to a given layer.
p : int
The pooling size (assumed to be square).
group_size : int
The group size for group sparse filtering.
step : int
The step size for group sparse filtering.
lr : int
The learning rate for gradient descent.
"""
# assign network inputs to layer
self.m = model_type
self.weight_dims = weight_dims
self.x = layer_input
self.p = p
self.lr = lr
self.c = c
if weights is None:
self.w = init_weights(weight_dims)
elif weights is not None:
self.w = weights
# build model based on model_type
self.model = None
if model_type == 'SparseFilter':
self.model = SparseFilter(self.w, self.x)
elif model_type == 'ConvolutionalSF':
self.model = ConvolutionalSF(self.w, self.x)
elif model_type == 'GroupSF':
self.g_mat = connections.gMatToroidal(self.weight_dims[0], group_size, step, centered='n')
self.model = GroupSF(self.w, self.x, self.g_mat)
elif model_type == 'MultiGroupSF':
self.g_mat1 = connections.groupMat(self.weight_dims[0], group_size, step)
self.g_mat2 = connections.groupMat(
np.square(np.sqrt(self.weight_dims[0]) / 2),
group_size, step,
)
self.model = MultiGroupSF(self.w, self.x, self.g_mat1, self.g_mat2)
elif model_type == 'GroupConvolutionalSF':
self.g_mat = connections.gMatToroidal(self.weight_dims[0], group_size, step, centered='n')
self.model = GroupConvolutionalSF(self.w, self.x, self.g_mat)
assert self.model is not None
def __init__(self, model_type='SparseFilter', weight_dims=(100, 256), layer_input=None,
p=None, group_size=None, step=None, lr=0.01, c = 'n'):
"""
Builds a layer for the network by constructing a model.
Parameters:
----------
model_type : str
The model type to build into a given layer.
weight_dims : list of tuples
The dimensions of the weight matrices for each layer.
fully connected: [neurons x input_dim ^ 2]
convolutional: [filters x dim x dim]
layer_input : ndarray (symbolic Theano variable)
The input to a given layer.
p : int
The pooling size (assumed to be square).
group_size : int
The group size for group sparse filtering.
step : int
The step size for group sparse filtering.
lr : int
The learning rate for gradient descent.
"""
# assign network inputs to layer
self.m = model_type
self.weight_dims = weight_dims
self.w = init_weights(weight_dims) # TODO: constrain L2-norm of weights to sum to unity
self.x = layer_input
self.p = p
self.lr = lr
self.c = c
# build model based on model_type
self.model = None
if model_type == 'SparseFilter':
self.model = SparseFilter(self.w, self.x)
elif model_type == 'ConvolutionalSF':
self.model = ConvolutionalSF(self.w, self.x)
elif model_type == 'GroupSF':
self.g_mat = connections.gMatToroidal(self.weight_dims[0], group_size, step, centered='n')
self.model = GroupSF(self.w, self.x, self.g_mat)
elif model_type == 'GroupConvolutionalSF':
self.g_mat = connections.gMatToroidal(self.weight_dims[0], group_size, step, centered='n')
self.model = GroupConvolutionalSF(self.w, self.x, self.g_mat)
assert self.model is not None
# visualize the receptive fields of the first layer
visualize.drawplots(weights['layer0'].T,
color='gray',
convolution=convolution,
pad=0,
examples=None,
channels=channels)
# get activations of first layer and save in dictionary
f_hat, _, _, _, _, _ = outputs[0](data)
f_hats[model_type[0]] = f_hat
# project activations of both networks up using local connections
group_matrix = connections.gMatToroidal(n_filters,
topographic_parameters[0],
topographic_parameters[1],
centered='n')
gf_hats = {}
for model in model_type_meta:
model = model[0]
gf_hats[model] = np.dot(f_hats[model].T, group_matrix)
# evaluate the sparseness of the distributions
pl.figure(1)
bins = np.linspace(0, 1, 100)
pl.hist(gf_hats['SparseFilter'].flatten(),
bins=bins,
alpha=0.5,
label='Sparse Filtering')
pl.hist(gf_hats['GroupSF'].flatten(),
bins=bins,