def test_maxpool_layer_1_forward(self):
print('\n==================================')
print(' Test maxpool layer forward ')
print('==================================')
np.random.seed(123)
in_dim, out_dim = 3, 3
kernel_size, stride = 3, 1
x = np.random.randn(1, in_dim, 5, 5)
maxpool_layer = MaxPoolingLayer(kernel_size, stride)
maxpool_out = maxpool_layer.forward(x)
correct_out = [[[[1.65143654, 1.49138963, 1.49138963],
[2.20593008, 2.20593008, 2.18678609],
[2.20593008, 2.20593008, 2.18678609]],
[[0.9071052 , 0.9071052 , 0.92746243],
[0.68822271, 0.68822271, 0.92746243],
[2.39236527, 2.39236527, 2.23814334]],
[[1.75488618, 1.75488618, 1.75488618],
[1.75488618, 1.75488618, 1.75488618],
[1.16220405, 1.16220405, 1.41729905]]]]
e = rel_error(correct_out, maxpool_out)
print('Relative difference:', e)
self.assertTrue(e <= 5e-8)
def test_maxpool_layer_2_backward(self):
print('\n==================================')
print(' Test maxpool layer backward ')
print('==================================')
np.random.seed(123)
in_dim, out_dim = 3, 3
kernel_size, stride = 3, 1
x = np.random.randn(1, in_dim, 5, 5)
maxpool_layer = MaxPoolingLayer(kernel_size, stride)
maxpool_out = maxpool_layer.forward(x)
d_prev = np.random.randn(*maxpool_out.shape)
dx = maxpool_layer.backward(d_prev, 0.01)
correct_dx = [[[[0., 0., 0., 0., 0.], [1.03972709, 0., 0., 0., 0.],
[0., 0., -0.52939562, 0., 0.],
[0., -1.47139598, 2.06254556, 0., 0.],
[0., 0., 0., 0., 0.]],
[[0., -1.40066055, 0., 0., 0.],
[0., 0., 0., 0., -0.56802076],
[0., 0., 0.87459023, 0., 0.], [0., 0., 0., 0., 0.],
[0., -0.11714651, 0., 0., 0.18103513]],
[[0., 0., 0., 0., 0.], [0., 0., -0.19467321, 0., 0.],
[0., 0., 0., 0., -1.97788793], [0., 0., 0., 0., 0.],
[0., 0., 0.26287825, 0., 0.]]]]
dx_e = rel_error(correct_dx, dx)
print('Relative difference dx:', dx_e)
self.assertTrue(dx_e <= 5e-8)
print('Convolution Layer Backward Check')
print('dx Difference : ', (conv_dx - correct_conv_dx).sum())
print('dW Difference : ', (conv_dW - correct_conv_dW).sum())
print('db Difference : ', (conv_db - correct_conv_db).sum())
print()
# ===========================================================================
print(
'============================ 4. Pooling Layer ============================='
)
kernel_size = 2
max_pool = MaxPoolingLayer(kernel_size, stride=kernel_size)
x = np.array([[5, 1, 3, 10], [-1, 0, 4, -1], [-10, -1, 11, 1], [0, 1, -5, 3]],
dtype=np.float32).reshape(1, 1, 4, 4)
pooled = max_pool.forward(x)
d_pool = max_pool.backward(np.ones_like(pooled))
print('Difference pool out: ', (correct_pooled - pooled).sum())
print('Difference d_pool: ', (correct_d_pool - d_pool).sum())
print()
# ===========================================================================
print(
'=============================== 5. FC Layer ==============================='
)
fc_layer = FCLayer(2, 2)
fc_layer.W = np.array([[-2.0, 1.25], [0.01, -1.0]])
fc_layer.b = np.array([-1.0, 1.0])
temp6 = np.array([[-1, 3], [0.0, 1.0]])