You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
90 lines
2.4 KiB
90 lines
2.4 KiB
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
|
|
|
|
class TestRmspropOp1(OpTest):
|
|
''' Test RMSProp with explicit inputs
|
|
'''
|
|
|
|
def setUp(self):
|
|
self.op_type = "rmsprop"
|
|
|
|
param = np.random.random((123, 321)).astype("float32")
|
|
mean_square = np.random.random((123, 321)).astype("float32")
|
|
learning_rate = np.array([0.01]).astype("float32")
|
|
grad = np.random.random((123, 321)).astype("float32")
|
|
moment = np.zeros((123, 321)).astype("float32")
|
|
|
|
epsilon = 1e-6
|
|
decay = 0.9
|
|
momentum = 0.0
|
|
|
|
self.inputs = {
|
|
'Param': param,
|
|
'MeanSquare': mean_square,
|
|
'LearningRate': learning_rate,
|
|
'Grad': grad,
|
|
'Moment': moment,
|
|
}
|
|
|
|
self.attrs = {'epsilon': epsilon, 'decay': decay, 'momentum': momentum}
|
|
|
|
ms_out = decay * mean_square + (1 - decay) * grad * grad
|
|
moment_out = momentum * moment + \
|
|
learning_rate * grad / np.sqrt(ms_out + epsilon)
|
|
param_out = param - moment_out
|
|
|
|
self.outputs = {
|
|
'ParamOut': param_out,
|
|
'MomentOut': moment_out,
|
|
'MeanSquareOut': ms_out
|
|
}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
class TestRmspropOp2(OpTest):
|
|
'''Test RMSProp with defaukt values for attributes
|
|
'''
|
|
|
|
def setUp(self):
|
|
self.op_type = "rmsprop"
|
|
|
|
param = np.random.random((123, 321)).astype("float32")
|
|
mean_square = np.random.random((123, 321)).astype("float32")
|
|
learning_rate = np.array([0.01]).astype("float32")
|
|
grad = np.random.random((123, 321)).astype("float32")
|
|
moment = np.zeros((123, 321)).astype("float32")
|
|
|
|
epsilon = 1.0e-10
|
|
decay = 0.9
|
|
momentum = 0.0
|
|
|
|
self.inputs = {
|
|
'Param': param,
|
|
'MeanSquare': mean_square,
|
|
'LearningRate': learning_rate,
|
|
'Grad': grad,
|
|
'Moment': moment,
|
|
}
|
|
|
|
ms_out = decay * mean_square + (1 - decay) * grad * grad
|
|
moment_out = momentum * moment + \
|
|
learning_rate * grad / np.sqrt(ms_out + epsilon)
|
|
param_out = param - moment_out
|
|
|
|
self.outputs = {
|
|
'ParamOut': param_out,
|
|
'MomentOut': moment_out,
|
|
'MeanSquareOut': ms_out
|
|
}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|