Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 e41a71ce 编写于 作者: F fengjiayi
浏览文件
操作

fix errors

上级 e0be63bf
显示空白变更内容
内联 并排
Showing with 66 addition and 65 deletion
python/paddle/v2/fluid/layers/nn.py
浏览文件 @ e41a71ce
......@@ -386,7 +386,8 @@ def square_error_cost(input, label, **kwargs):
square_out = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]})
type='square', inputs={'X': [minus_out]},
outputs={'Out': [square_out]})
return square_out
......
python/paddle/v2/fluid/tests/test_activation_op.py
浏览文件 @ e41a71ce
......@@ -10,13 +10,13 @@ class TestExp(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.exp(self.inputs['X'])}
self.outputs = {'Out': np.exp(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSigmoid(OpTest):
......@@ -25,13 +25,13 @@ class TestSigmoid(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
self.outputs = {'Out': 1 / (1 + np.exp(-self.inputs['X']))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
class TestLogSigmoid(OpTest):
......@@ -40,13 +40,13 @@ class TestLogSigmoid(OpTest):
self.inputs = {
'X': np.random.uniform(-1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.log(1 / (1 + np.exp(-self.inputs['X'])))}
self.outputs = {'Out': np.log(1 / (1 + np.exp(-self.inputs['X'])))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
class TestTanh(OpTest):
......@@ -55,13 +55,13 @@ class TestTanh(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.tanh(self.inputs['X'])}
self.outputs = {'Out': np.tanh(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestTanhShrink(OpTest):
......@@ -70,13 +70,13 @@ class TestTanhShrink(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [10, 17]).astype("float32")
}
self.outputs = {'Y': self.inputs['X'] - np.tanh(self.inputs['X'])}
self.outputs = {'Out': self.inputs['X'] - np.tanh(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
class TestHardShrink(OpTest):
......@@ -90,13 +90,13 @@ class TestHardShrink(OpTest):
t = np.copy(x)
t[(t >= -threshold) & (t <= threshold)] = 0
self.outputs = {'Y': t}
self.outputs = {'Out': t}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.005)
self.check_grad(['X'], 'Out', max_relative_error=0.005)
class TestSoftShrink(OpTest):
......@@ -110,13 +110,13 @@ class TestSoftShrink(OpTest):
y = np.copy(self.inputs['X'])
y = (y < -lambda_val) * (y + lambda_val) + (y > lambda_val) * (
y - lambda_val)
self.outputs = {'Y': y}
self.outputs = {'Out': y}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSqrt(OpTest):
......@@ -125,13 +125,13 @@ class TestSqrt(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.sqrt(self.inputs['X'])}
self.outputs = {'Out': np.sqrt(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestAbs(OpTest):
......@@ -144,13 +144,13 @@ class TestAbs(OpTest):
# we should avoid this
x[np.abs(x) < 0.005] = 0.02
self.inputs = {'X': x}
self.outputs = {'Y': np.abs(self.inputs['X'])}
self.outputs = {'Out': np.abs(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestCeil(OpTest):
......@@ -158,13 +158,13 @@ class TestCeil(OpTest):
self.op_type = "ceil"
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
self.inputs = {'X': x}
self.outputs = {'Y': np.ceil(self.inputs['X'])}
self.outputs = {'Out': np.ceil(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestFloor(OpTest):
......@@ -173,13 +173,13 @@ class TestFloor(OpTest):
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
self.inputs = {'X': x}
# numpy floor need +1
self.outputs = {'Y': np.floor(self.inputs['X']) + 1.0}
self.outputs = {'Out': np.floor(self.inputs['X']) + 1.0}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestRound(OpTest):
......@@ -187,13 +187,13 @@ class TestRound(OpTest):
self.op_type = "round"
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
self.inputs = {'X': x}
self.outputs = {'Y': np.round(self.inputs['X'])}
self.outputs = {'Out': np.round(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestRelu(OpTest):
......@@ -203,13 +203,13 @@ class TestRelu(OpTest):
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
self.inputs = {'X': x}
self.outputs = {'Y': np.maximum(self.inputs['X'], 0)}
self.outputs = {'Out': np.maximum(self.inputs['X'], 0)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestBRelu(OpTest):
......@@ -227,13 +227,13 @@ class TestBRelu(OpTest):
t = np.copy(x)
t[t < t_min] = t_min
t[t > t_max] = t_max
self.outputs = {'Y': t}
self.outputs = {'Out': t}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestRelu6(OpTest):
......@@ -248,14 +248,14 @@ class TestRelu6(OpTest):
self.inputs = {'X': x}
self.attrs = {'threshold': threshold}
self.outputs = {
'Y': np.minimum(np.maximum(self.inputs['X'], 0), threshold)
'Out': np.minimum(np.maximum(self.inputs['X'], 0), threshold)
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestSoftRelu(OpTest):
......@@ -271,13 +271,13 @@ class TestSoftRelu(OpTest):
t = np.copy(x)
t[t < -threshold] = -threshold
t[t > threshold] = threshold
self.outputs = {'Y': np.log((np.exp(t) + 1))}
self.outputs = {'Out': np.log((np.exp(t) + 1))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestELU(OpTest):
......@@ -290,27 +290,27 @@ class TestELU(OpTest):
self.inputs = {'X': x}
self.attrs = {'alpha': alpha}
self.outputs = {
'Y': np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1))
'Out': np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1))
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestReciprocal(OpTest):
def setUp(self):
self.op_type = "reciprocal"
self.inputs = {'X': np.random.uniform(1, 2, [11, 17]).astype("float32")}
self.outputs = {'Y': np.reciprocal(self.inputs['X'])}
self.outputs = {'Out': np.reciprocal(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.01)
self.check_grad(['X'], 'Out', max_relative_error=0.01)
class TestLog(OpTest):
......@@ -319,13 +319,13 @@ class TestLog(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.log(self.inputs['X'])}
self.outputs = {'Out': np.log(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSquare(OpTest):
......@@ -334,13 +334,13 @@ class TestSquare(OpTest):
self.inputs = {
'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
}
self.outputs = {'Y': np.square(self.inputs['X'])}
self.outputs = {'Out': np.square(self.inputs['X'])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestPow(OpTest):
......@@ -348,13 +348,13 @@ class TestPow(OpTest):
self.op_type = "pow"
self.inputs = {'X': np.random.uniform(1, 2, [11, 17]).astype("float32")}
self.attrs = {'factor': 3.0}
self.outputs = {'Y': np.power(self.inputs['X'], 3)}
self.outputs = {'Out': np.power(self.inputs['X'], 3)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.02)
self.check_grad(['X'], 'Out', max_relative_error=0.02)
class TestSTanh(OpTest):
......@@ -366,13 +366,13 @@ class TestSTanh(OpTest):
scale_a = 2.0 / 3.0
scale_b = 1.7159
self.attrs = {'scale_a': scale_a, 'scale_b': scale_b}
self.outputs = {'Y': scale_b * np.tanh(self.inputs['X'] * scale_a)}
self.outputs = {'Out': scale_b * np.tanh(self.inputs['X'] * scale_a)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSoftplus(OpTest):
......@@ -381,13 +381,13 @@ class TestSoftplus(OpTest):
self.inputs = {
'X': np.random.uniform(-1, 1, [11, 17]).astype("float64")
}
self.outputs = {'Y': np.log(1 + np.exp(self.inputs['X']))}
self.outputs = {'Out': np.log(1 + np.exp(self.inputs['X']))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestSoftsign(OpTest):
......@@ -397,14 +397,14 @@ class TestSoftsign(OpTest):
'X': np.random.uniform(-1, 1, [11, 17]).astype("float32")
}
self.outputs = {
'Y': np.divide(self.inputs['X'], 1 + np.abs(self.inputs['X']))
'Out': np.divide(self.inputs['X'], 1 + np.abs(self.inputs['X']))
}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.007)
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestThresholdedRelu(OpTest):
......@@ -419,13 +419,13 @@ class TestThresholdedRelu(OpTest):
self.inputs = {'X': X}
self.attrs = {'threshold': threshold}
self.outputs = {'Y': (X > threshold) * X}
self.outputs = {'Out': (X > threshold) * X}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=self.relative_error)
self.check_grad(['X'], 'Out', max_relative_error=self.relative_error)
class TestHardSigmoid(OpTest):
......@@ -447,13 +447,13 @@ class TestHardSigmoid(OpTest):
upper_threshold - 0.2
temp = X * slope + offset
self.outputs = {'Y': np.maximum(0.0, np.minimum(1.0, temp))}
self.outputs = {'Out': np.maximum(0.0, np.minimum(1.0, temp))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.002)
self.check_grad(['X'], 'Out', max_relative_error=0.002)
class TestSwish(OpTest):
......@@ -462,13 +462,13 @@ class TestSwish(OpTest):
X = np.random.uniform(0.1, 1, [11, 17]).astype("float32")
self.inputs = {'X': X}
self.attrs = {'beta': 2.3}
self.outputs = {'Y': X * expit(self.attrs['beta'] * X)}
self.outputs = {'Out': X * expit(self.attrs['beta'] * X)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y', max_relative_error=0.008)
self.check_grad(['X'], 'Out', max_relative_error=0.008)
if __name__ == "__main__":
......
python/paddle/v2/fluid/tests/test_net.py
浏览文件 @ e41a71ce
......@@ -7,7 +7,7 @@ def fc(X, W, Y):
ret_v = core.Net.create()
ret_v.append_op(Operator("mul", X="X", Y="W", Out="pre_activation"))
ret_v.append_op(Operator("sigmoid", X="pre_activation", Y=Y))
ret_v.append_op(Operator("sigmoid", X="pre_activation", Out=Y))
ret_v.complete_add_op(True)
return ret_v
......@@ -30,7 +30,7 @@ Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{Y[fc.out]}.
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{Out[fc.out]}.
'''
self.assertEqual(expected, "\n" + str(net))
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或