def test_conv_and_max_pooling():
# 测试卷积和最大池化
z = np.random.randn(3, 3, 28, 28).astype(np.float)
K = np.random.randn(3, 4, 3, 3).astype(np.float) * 1e-3
b = np.zeros(4).astype(np.float)
next_z = conv_forward(z, K, b)
y_pred = max_pooling_forward_bak(next_z, pooling=(2, 2))
y_true = np.ones_like(y_pred)
from nn.losses import mean_squared_loss
for i in range(10000):
# 前向
next_z = conv_forward(z, K, b)
y_pred = max_pooling_forward_bak(next_z, pooling=(2, 2))
# 反向
loss, dy = mean_squared_loss(y_pred, y_true)
next_dz = max_pooling_backward_bak(dy, next_z, pooling=(2, 2))
dK, db, _ = conv_backward(next_dz, K, z)
K -= 0.001 * dK
b -= 0.001 * db
if i % 10 == 0:
print("i:{},loss:{},mindy:{},maxdy:{}".format(i, loss, np.mean(dy), np.max(dy)))
if np.allclose(y_true, y_pred):
print("yes")
break
def test_conv():
# 测试卷积
z = np.random.randn(3, 3, 28, 28).astype(np.float)
K = np.random.randn(3, 4, 3, 3).astype(np.float) * 1e-3
b = np.zeros(4).astype(np.float)
next_z = conv_forward(z, K, b)
y_true = np.ones_like(next_z)
from nn.losses import mean_squared_loss
for i in range(10000):
# 前向
next_z = conv_forward(z, K, b)
# 反向
loss, dy = mean_squared_loss(next_z, y_true)
dK, db, _ = conv_backward(dy, K, z)
K -= 0.001 * dK
b -= 0.001 * db
if i % 10 == 0:
print("i:{},loss:{},mindy:{},maxdy:{}".format(i, loss, np.mean(dy), np.max(dy)))
if np.allclose(y_true, next_z):
print("yes")
break
