def dropout(x, dropout_ratio=0.5):
x = as_variable(x)
if dezero.Config.train:
xp = cuda.get_array_module(x)
mask = xp.random.rand(*x.shape) > dropout_ratio
scale = xp.array(1.0 - dropout_ratio).astype(x.dtype)
y = x * mask / scale
return y
else:
return x
def backward(self, gy):
x, t = self.inputs
N, CLS_NUM = x.shape
gy *= 1 / N
y = softmax(x)
# Convert to one-hot
#xp = cuda.get_array_module(t.data)
xp = as_variable(x)
t_onehot = xp.eye(CLS_NUM, dtype=t.dtype)[t.data]
y = (y - t_onehot) * gy
return y
def pooling_simple(x, kernel_size, stride=1, pad=0):
x = as_variable(x)
N, C, H, W = x.shape
KH, KW = pair(kernel_size)
PH, PW = pair(pad)
SH, SW = pair(stride)
OH = get_conv_outsize(H, KH, SH, PH)
OW = get_conv_outsize(W, KW, SW, PW)
col = im2col(x, kernel_size, stride, pad, to_matrix=True)
col = col.reshape(-1, KH * KW)
y = col.max(axis=1)
y = y.reshape(N, OH, OW, C).transpose(0, 3, 1, 2)
return y
def pooling_simple(x, kernel_size, stride=1, pad=0):
x = as_variable(x)
n, c, h, w = x.shape
kh, kw = _pair(kernel_size)
ph, pw = _pair(pad)
sh, sw = _pair(stride)
out_h = (h + ph * 2 - kh) // sh + 1
out_w = (h + pw * 2 - kw) // sw + 1
col = im2col(x, kernel_size, stride, pad)
col = col.transpose((0, 4, 5, 1, 2, 3)).reshape((-1, kh * kw))
y = col.max(axis=1)
y = y.reshape((n, out_h, out_w, c))
y = y.transpose((0, 3, 1, 2))
return y
def sigmoid_simple(x):
x = as_variable(x)
y = 1 / (1 + exp(-x))
return y
def broadcast_to(x, shape):
if x.shape == shape:
return as_variable(x)
return BroadcastTo(shape)(x)
def sum_to(x, shape):
if x.shape == shape:
return as_variable(x)
return SumTo(shape)(x)
def reshape(x, shape):
if x.shape == shape:
return as_variable(x)
return Reshape(shape)(x)
def average(x, axis=None, keepdims=False):
x = as_variable(x)
y = sum(x, axis, keepdims)
return y * (y.data.size / x.data.size)
def expand_dims(x, axis):
x = as_variable(x)
shape = list(x.shape)
shape.insert(axis, 1)
return reshape(x, tuple(shape))
def forward(self, x):
x = as_variable(x)
y = 1 / (1 + exp(-x))
return y
def accuracy(y, t):
y, t = as_variable(y), as_variable(t)
pred = y.data.argmax(axis=1).reshape(t.shape)
result = (pred == t.data)
acc = result.mean()
return Variable(as_array(acc))
def test_forward(self):
a = as_variable(6.0)
b = as_variable(4.0)
c = div(a, b)
self.assertEqual(c.data, np.array(1.5))
def forward(self, x):
#xp = cuda.get_array_module(x)
xp = as_variable(x)
y = xp.clip(x, self.x_min, self.x_max)
return y
def forward(self, x):
x = as_variable(x)
y = x - x.max(axis=self.axis, keepdims=True)
y = x.exp(y)
y /= y.sum(axis=self.axis, keepdims=True)
return y
def softmax_simple(x, axis=1):
x = as_variable(x)
y = exp(x)
sum_y = sum(y, axis=axis, keepdims=True)
return y / sum_y
def mean_squared_error_simple(x0, x1):
x0, x1 = as_variable(x0), as_variable(x1)
diff = x0 - x1
y = sum(diff**2) / len(diff)
return y
def mean_squared_error_simple(x0, x1):
x0, x1 = as_variable(x0), as_variable(x1)
diff = x0 - x1
return sum(diff**2) / diff.size