def main():
# read in the data
base_path = os.path.dirname(__file__)
file_path = os.path.join(base_path, "data", "patches.mat")
data = loadmat(file_path)['X']
# preprocess the data
data -= data.mean(axis=0)
data = whiten(data)
data = np.float32(data)
# define the model variables
weight_dims = (100, 256) # network architecture
w = init_weights(weight_dims) # random weights
x = t.fmatrix() # symbolic variable for data
# build model and define training functions
model = SparseFilter(w, x)
train_fn = training_functions(data, model, weight_dims)
# train the model using L-BFGS
weights = minimize(train_fn, model.w.eval().flatten(),
method='L-BFGS-B', jac=True,
options={'maxiter': 100, 'disp': True})
# grab the weights and visualize them
weights = weights.x.reshape(weight_dims[0], weight_dims[1])
visualize.drawplots(weights.T, 'y', 'n', 1)
#################### MAIN SCRIPT #########################
# load data, normalize, and convert to float32
basepath = os.path.dirname(__file__)
filename = 'patches.mat'
filepath = os.path.join(basepath, "data", filename)
data = loadmat(filepath)['data']
data -= data.mean(axis=0)
data = np.float32(data)
# construct the network
gSize = 2
step = 1
wDims = [[225, 256]]
model = sparse_filtering.network(model_type='groupSF', wDims=wDims, p=None,
gMat=None, gSize=gSize, step=step, lr=0.01)
train = model.training_functions(data)
# train the sparse filtering network
maxIter = 100
cost_master = []
for i in range(maxIter):
cost, w = train[0]()
cost_master.append(cost)
print("The cost at iteration %i: %f" %(i, cost))
plotCost(cost_master)
drawplots(w.T)
filename = 'patches.mat'
filepath = os.path.join(basepath, "data", filename)
data = loadmat(filepath)['data']
data -= data.mean(axis=0)
data = np.float32(data)
# construct the network
gSize = 2
step = 1
wDims = [[225, 256]]
model = sparse_filtering.network(model_type='groupSF',
wDims=wDims,
p=None,
gMat=None,
gSize=gSize,
step=step,
lr=0.01)
train = model.training_functions(data)
# train the sparse filtering network
maxIter = 100
cost_master = []
for i in range(maxIter):
cost, w = train[0]()
cost_master.append(cost)
print("The cost at iteration %i: %f" % (i, cost))
plotCost(cost_master)
drawplots(w.T)
