def test_SoftmaxLossLayer(self):
bot0 = Blob()
bot0.Reshape([2,6])
bot0.set_data([1.0,2.0,3.0,4.0,5.0,6.0,1.0,2.0,3.0,4.0,5.0,6.0])
bot0.Reshape([2,6])
bot1 = Blob()
bot1.Reshape([2,6])
bot1.set_data([1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0])
bot1.Reshape([2,6])
top0 = Blob()
top1 = Blob()
expect_bot0 = Blob()
expect_bot0.Reshape([2,6])
expect_bot0.set_diff([-0.49786511,0.00580323,0.01577482,0.0428804,0.116561,0.31684566,-0.49786511,0.00580323,0.01577482,0.0428804,0.116561,0.31684566])
expect_bot0.Reshape([2,6])
expect_top1 = Blob()
expect_top1.Reshape([2,6])
expect_top1.set_data([0.00426978, 0.01160646, 0.03154963, 0.08576079, 0.23312201,0.63369132,0.00426978, 0.01160646, 0.03154963, 0.08576079, 0.23312201,0.63369132])
expect_top1.Reshape([2,6])
layer = SoftmaxLossLayer()
layer.Setup([bot0, bot1], [top0,top1])
layer.Forward([bot0, bot1], [top0,top1])
np.testing.assert_array_almost_equal( top1.data(), expect_top1.data() )
top0.set_diff(1.0)
layer.Backward([top0,top1], [], [bot0, bot1])
np.testing.assert_array_almost_equal( bot0.diff(), expect_bot0.diff() )
def test_MaxPoolingLayer(self):
top0 = Blob()
bot0 = Blob()
bot0.Reshape((1,1,4,4))
bot0.set_data([5,3,1,2,1,2,3,2,4,2,2,5,3,6,1,1])
bot0.Reshape((1,1,4,4))
expect_top0 = Blob()
expect_top0.Reshape((1,1,2,2))
expect_top0.set_data([5,3,6,5])
expect_top0.Reshape((1,1,2,2))
expect_bot0 = Blob()
expect_bot0.Reshape((1,1,4,4))
expect_bot0.set_diff([1,0,0,0,0,0,0.8,0,0,0,0,0.6,0,0.4,0,0])
expect_bot0.Reshape((1,1,4,4))
layer = MaxPoolingLayer(2,2,2)
layer.Setup([bot0], [top0])
layer.Forward([bot0], [top0])
np.testing.assert_array_almost_equal( top0.data(), expect_top0.data() )
top0.set_diff([1.0,0.8,0.4,0.6])
top0.Reshape((1,1,2,2))
layer.Backward([top0], [], [bot0])
np.testing.assert_array_almost_equal( bot0.diff(), expect_bot0.diff() )
def test_ReLULayer(self):
# top0.data = ReLU(bot0.data)
# bot0.diff = ReLUGrad(top0.diff)
bot0 = Blob()
bot0.Reshape([2,6])
bot0.set_data([-1.0,2.0,-3.0,4.0,-5.0,6.0,-1.0,2.0,-3.0,4.0,-5.0,6.0])
bot0.Reshape([2,6])
top0 = Blob()
top0.Reshape([2,6])
top0.set_diff([1.0,-2.0,3.0,-4.0,5.0,-6.0,1.0,-2.0,3.0,-4.0,5.0,-6.0])
top0.Reshape([2,6])
expect_bot0 = Blob()
expect_bot0.Reshape([2,6])
expect_bot0.set_diff([0.0,-2.0,0.0,-4.0,0.0,-6.0,0.0,-2.0,0.0,-4.0,0.0,-6.0])
expect_bot0.Reshape([2,6])
expect_top0 = Blob()
expect_top0.Reshape([2,6])
expect_top0.set_data([0.0,2.0,0.0,4.0,0.0,6.0,0.0,2.0,0.0,4.0,0.0,6.0])
expect_top0.Reshape([2,6])
layer = ReLULayer()
layer.Setup([bot0], [top0])
layer.Forward([bot0], [top0])
np.testing.assert_array_equal( expect_top0.data(), top0.data() )
layer.Backward([top0], [], [bot0])
np.testing.assert_array_equal( expect_bot0.diff(), bot0.diff() )
def test_SoftMaxLayer(self):
bot0 = Blob()
bot0.Reshape((3,))
bot0.set_data([1,2,3])
bot0.Reshape((3,))
top0 = Blob()
top0.Reshape((3,))
expect_top0 = Blob()
expect_top0.Reshape((3,))
expect_top0.set_data([0.09003057, 0.24472847, 0.66524096])
expect_top0.Reshape((3,))
expect_bot0 = Blob()
expect_bot0.Reshape((3,))
expect_bot0.set_diff([0.0, 0.0, 0.0])
expect_bot0.Reshape((3,))
layer = SoftMaxLayer()
layer.Setup([bot0], [top0])
layer.Forward([bot0], [top0])
np.testing.assert_array_almost_equal( top0.data(), expect_top0.data() )
top0.set_diff(np.ones_like(top0.data()))
layer.Backward([top0], [], [bot0])
np.testing.assert_array_almost_equal( bot0.diff(), expect_bot0.diff() )
def test_ConvolutionLayer(self):
bot0 = Blob()
bot0.Reshape((1,1,4,4))
bot0.set_data([1,1,0,1,1,0,0,1,0,1,1,0,1,1,1,1])
bot0.Reshape((1,1,4,4))
top0 = Blob()
expect_top0 = Blob()
expect_top0.Reshape((1,1,3,3))
expect_top0.set_data([2,1,1,2,2,1,2,3,3])
expect_top0.Reshape((1,1,3,3))
expect_W = Blob()
expect_W.Reshape((1,1,2,2))
expect_W.set_diff([5,5,6,6])
expect_W.Reshape((1,1,2,2))
expect_bot0 = Blob()
expect_bot0.Reshape((1,1,4,4))
expect_bot0.set_diff([1,1,1,0,2,3,3,1,2,3,3,1,1,2,2,1])
expect_bot0.Reshape((1,1,4,4))
expect_b = Blob()
expect_b.Reshape((1,))
expect_b.set_diff([9])
expect_b.Reshape((1,))
layer = ConvolutionLayer(2,2,1,0,1)
layer.Setup([bot0], [top0])
layer.W.set_data([1,0,1,1])
layer.W.Reshape((1,1,2,2))
layer.Forward([bot0], [top0])
np.testing.assert_array_almost_equal( top0.data(), expect_top0.data() )
top0.set_diff(np.ones_like(top0.data()))
layer.Backward([top0], [], [bot0])
np.testing.assert_array_almost_equal( layer.W.diff(), expect_W.diff() )
np.testing.assert_array_almost_equal( layer.b.diff(), expect_b.diff() )
np.testing.assert_array_almost_equal( bot0.diff(), expect_bot0.diff() )
def test_InnerProductLayer(self):
bot0 = Blob()
bot0.Reshape((2,3))
bot0.set_data([1,2,3,4,5,6])
bot0.Reshape((2,3))
top0 = Blob()
expect_b = Blob()
expect_b.Reshape((1,2))
expect_b.set_diff([2,2])
expect_b.Reshape((1,2))
expect_W = Blob()
expect_W.Reshape((3,2))
expect_W.set_diff([5,5,7,7,9,9])
expect_W.Reshape((3,2))
expect_bot0 = Blob()
expect_bot0.Reshape((2,3))
expect_bot0.set_diff([3,7,11,3,7,11])
expect_bot0.Reshape((2,3))
layer = InnerProductLayer(3,2)
layer.Setup([bot0], [top0])
layer.W.set_data([1,2,3,4,5,6])
layer.W.Reshape((3,2))
expect_top0 = Blob()
expect_top0.Reshape((2,2))
expect_top0.set_data([22,28,49,64])
expect_top0.Reshape((2,2))
layer.Forward([bot0], [top0])
np.testing.assert_array_almost_equal( top0.data(), expect_top0.data() )
top0.set_diff(np.ones_like(top0.data()))
layer.Backward([top0], [], [bot0])
np.testing.assert_array_almost_equal( layer.b.diff(), expect_b.diff() )
np.testing.assert_array_almost_equal( layer.W.diff(), expect_W.diff() )
np.testing.assert_array_almost_equal( bot0.diff(), expect_bot0.diff() )
self.rand_blob_.data() * self.scale_))
def Backward_cpu(self, top, propagate_down, bottom):
bottom[0].set_diff(
numpy.multiply(top[0].diff(),
self.rand_blob_.data() * self.scale_))
if __name__ == '__main__':
layer = DropoutLayer(0.5)
bottom = Blob()
top = Blob()
bottom.Reshape((4, 4))
bottom.set_data(numpy.arange(16) * 1.0)
bottom.Reshape((4, 4))
layer.Setup([bottom], [top])
layer.Forward([bottom], [top])
top.set_diff(numpy.arange(16) * 1.0)
top.Reshape((4, 4))
layer.Backward([top], [], [bottom])
print bottom.data()
print layer.rand_blob_.data()
print layer.scale_
print top.data()
print bottom.diff()
layer2 = ExpLayer()
layer2.Setup([top], [top2])
layer2.Forward([top], [top2])
layer2.Backward([top2], [], [top])
layer1.Backward([top], [], [bottom])
print 'bottom'
print bottom.data()
print 'top1'
print top.data()
print 'top2'
print top2.data()
print 'top2.diff'
print top2.diff()
print 'top1.diff'
print top.diff()
print 'bottom.diff'
print bottom.diff()
bottom.set_data([-2, -1, 0, 1, 2, 3])
bottom.Reshape((6, ))
top.Reshape((5, ))
top.set_diff([-2, -1, 0, 1, 2])
top.Reshape((5, ))
layer = InnerProductLayer()
layer.Setup([bottom], [top])
layer.Forward([bottom], [top])
loss = dot / bottom[0].shape()[0] / 2
top[0].set_data(loss)
def Backward_cpu(self, top, propagate_down, bottom):
print top[0].diff()
print bottom[0].shape()[0]
print self.diff_.data()
bottom[0].set_diff(top[0].diff() / bottom[0].shape()[0] *
self.diff_.data())
if __name__ == '__main__':
bottom_0 = Blob(numpy.float, [6])
bottom_1 = Blob(numpy.float, [6])
bottom_0.set_data([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
bottom_1.set_data([1.0, 0.0, 0.0, 0.0, 0.0, 0.0])
bottom_0.Reshape([1, 6])
bottom_1.Reshape([1, 6])
top = Blob(numpy.float, 0)
top.set_diff(10.0)
layer = EuclideanLossLayer()
layer.Setup([bottom_0, bottom_1], [top])
layer.Forward([bottom_0, bottom_1], [top])
layer.Backward([top], [], [bottom_0, bottom_1])
print top.data()
print bottom_0.diff()
class InnerProductLayer(Layer):
def __init__(self, K, N):
Layer.__init__(self)
# input number of neuron
self.K_ = K
# output number of neuron
self.N_ = N
self.bias_term_ = None
self.bias_multiplier_ = None
self.transpose_ = False
self.W = Blob()
self.b = Blob()
self.blobs_.append(self.W)
self.blobs_.append(self.b)
def LayerSetup(self, bottom, top):
self.M_ = bottom[0].data().shape[0]
W_shape = (self.K_, self.N_)
b_shape = (1, self.N_)
self.W.Reshape(W_shape)
self.b.Reshape(b_shape)
# Xavier
fan_in = self.W.count() / self.W.shape()[0]
fan_out = self.W.count() / self.W.shape()[1]
n = (fan_in + fan_out) / 2
scale = numpy.sqrt(3.0 / n)
#self.W.set_data(numpy.random.uniform(-scale, scale, self.W.count()) )
self.W.set_data(
numpy.random.normal(loc=0.0, scale=0.1, size=self.W.count()))
self.W.Reshape(W_shape)
def Reshape(self, bottom, top):
top_shape = (self.M_, self.N_)
top[0].Reshape(top_shape)
def type(self):
return 'InnerProduct'
def ExactNumBottomBlobs(self):
return 1
def ExactNumTopBlobs(self):
return 1
def Forward_cpu(self, bottom, top):
top[0].set_data(
numpy.matmul(bottom[0].data(), self.W.data()) + self.b.data())
def Backward_cpu(self, top, propagate_down, bottom):
self.W.set_diff(
self.W.diff() +
numpy.matmul(bottom[0].data().transpose(), top[0].diff()))
self.b.set_diff(self.b.diff() + numpy.sum(top[0].diff(), axis=0))
bottom[0].set_diff(
numpy.matmul(top[0].diff(),
self.W.data().transpose()))
