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(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 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
if "__file__" in globals():
import os, sys
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
import numpy as np
from dezero import test_mode
import dezero.functions as F
x = np.ones(5)
print(x)
# When training
y = F.dropout(x)
print(y)
# When testing (predicting)
with test_mode():
y = F.dropout(x)
print(y)
def test_forward1(self):
x = np.random.randn(100, 100)
y = F.dropout(Variable(x), dropout_ratio=0.0)
res = np.array_equal(y.data, x.data)
self.assertTrue(res)
def f(x):
np.random.seed(0)
return F.dropout(x, 0.0)
def test_forward2(self):
x = np.random.randn(100, 100)
with dezero.test_mode():
y = F.dropout(x)
res = array_equal(y.data, x)
self.assertTrue(res)
def test_backward_on_train(self):
np.random.seed(2)
x = Variable(np.array([[0., 1.], [2., 3.]]))
y, = F.dropout(x)
y.backward()
assert_equal(x.grad.data, [[0., 0.], [2., 0.]])
def test_forward_on_infer(self):
np.random.seed(2)
x = np.array([[0., 1.], [2., 3.]])
with dezero.config.test_mode():
y, = F.dropout(x)
assert_equal(y.data, x)
def test_forward_on_train(self):
np.random.seed(2)
x = np.array([[0., 1.], [2., 3.]])
mask = np.array([[False, False], [True, False]])
y, = F.dropout(x)
assert_equal(y.data, x * mask / 0.5)
