class DummyDataset:
def __init__(self,X):
self.X = X.transpose(0,3,1,2).reshape(X.shape[0],np.prod(X.shape[1:]))
def get_design_matrix(self):
return self.X
def set_design_matrix(self,X):
self.X = X
X = model.get_input_space().make_batch_theano()
Y = model.fprop(X)
f = theano.function([X],Y)
X = theano.tensor.tensor3()
pool = theano.function([X],pooling.max_pool(X))
Xs = []
y = []
for i, (x,y) in enumerate(zip(dataset.raw.get_design_matrix(),dataset.raw.get_targets())):
print i, dataset.raw.get_design_matrix().shape
# Xs.append(f(np.cast['float32'](row[None,:].reshape(1,3,96,96).transpose(0,2,3,1))))
Xs.append(f(np.cast['float32'](x[None,:].reshape(1,96,96,3))))
y.append(y)
print Xs[-1]
if i==5:
break
Xs = np.array(Xs)
y = np.array(y)
def test_max_pooling():
x = T.tensor3()
xval = numpy.random.rand(4, 3, 2)
fn = theano.function([x], pooling.max_pool(x))
fn(xval)
def test_max_pooling():
x = T.tensor3()
xval = numpy.random.rand(4,3,2)
fn = theano.function([x], pooling.max_pool(x))
fn(xval)
