def forward(self, x):
x = F.relu(self.conv1_1(x))
x = F.relu(self.conv1_2(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv2_1(x))
x = F.relu(self.conv2_2(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv3_1(x))
x = F.relu(self.conv3_2(x))
x = F.relu(self.conv3_3(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv4_1(x))
x = F.relu(self.conv4_2(x))
x = F.relu(self.conv4_3(x))
x = F.pooling(x, 2, 2)
x = F.relu(self.conv5_1(x))
x = F.relu(self.conv5_2(x))
x = F.relu(self.conv5_3(x))
x = F.pooling(x, 2, 2)
x = F.reshape(x, (x.shape[0], -1))
x = F.dropout(F.relu(self.fc6(x)))
x = F.dropout(F.relu(self.fc7(x)))
x = self.fc8(x)
return x
def forward(self, x):
x = F.relu(self.conv1_1(x)) # (OH, OW)=(28, 28)
x = F.relu(self.conv1_2(x)) # (OH, OW)=(28, 28)
x = F.pooling(x, 2, 2) # (OH, OW)=(14, 14)
x = F.relu(self.conv2_1(x)) # (OH, OW)=(14, 14)
x = F.relu(self.conv2_2(x)) # (OH, OW)=(14, 14)
x = F.pooling(x, 2, 2) # (OH, OW)=(7, 7)
x = F.reshape(x, (x.shape[0], -1)) # (7, 7)->(49, )
x = F.dropout(F.relu(self.fc3(x)))
x = self.fc4(x)
return x
def test_forward2(self):
n, c, h, w = 1, 5, 15, 15
ksize, stride, pad = 2, 2, 0
x = np.random.randn(n, c, h, w).astype('f')
y = F.pooling(x, ksize, stride, pad)
expected = CF.max_pooling_2d(x, ksize, stride, pad, cover_all=False)
self.assertTrue(np.array_equal(expected.data, y.data))
def test_forward1(self):
n, c, h, w = 1, 5, 16, 16
ksize, stride, pad = 2, 2, 0
x = np.random.randn(n, c, h, w).astype('f')
y = F.pooling(x, ksize, stride, pad)
expected = CF.max_pooling_2d(x, ksize, stride, pad)
self.assertTrue(array_allclose(expected.data, y.data))
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.pooling(x, kernel_size=3, stride=2)
x = self.res2(x)
x = self.res3(x)
x = self.res4(x)
x = self.res5(x)
x = _global_average_pooling_2d(x)
x = self.fc6(x)
return x
def forward(self, x):
x = F.relu(self.conv1(x)) # (OH, OW)=(28, 28)
x = F.pooling(x, 2, 2) # (OH, OW)=(14, 14)
#x = F.relu(self.conv2(x))
#x = F.pooling(x, 2, 2)
x = F.reshape(x, (x.shape[0], -1)) # (14, 14)->(196, )
x = F.dropout(F.relu(self.fc3(x)))
#x = F.dropout(F.relu(self.fc4(x)))
x = self.fc5(x)
return x
def test_backward1(self):
n, c, h, w = 1, 5, 16, 16
ksize, stride, pad = 2, 2, 0
x = np.random.randn(n, c, h, w).astype('f') * 1000
f = lambda x: F.pooling(x, ksize, stride, pad)
self.assertTrue(gradient_check(f, x))
