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Updated tests and rebased

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This commit is contained in:
Alejandro Saucedo 2021-03-07 10:39:30 +00:00
parent bb64b2b37c
commit a2ee928f4c
9 changed files with 151 additions and 100 deletions
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27
python/test/test_logistic_regression.py
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@ -1,4 +1,5 @@
import pyshader as ps
import numpy as np
import kp
def test_logistic_regression():
@ -46,21 +47,21 @@ def test_logistic_regression():
mgr = kp.Manager(0)
# First we create input and ouput tensors for shader
tensor_x_i = mgr.tensor([0.0, 1.0, 1.0, 1.0, 1.0])
tensor_x_j = mgr.tensor([0.0, 0.0, 0.0, 1.0, 1.0])
tensor_x_i = mgr.tensor(np.array([0.0, 1.0, 1.0, 1.0, 1.0]))
tensor_x_j = mgr.tensor(np.array([0.0, 0.0, 0.0, 1.0, 1.0]))
tensor_y = mgr.tensor([0.0, 0.0, 0.0, 1.0, 1.0])
tensor_y = mgr.tensor(np.array([0.0, 0.0, 0.0, 1.0, 1.0]))
tensor_w_in = mgr.tensor([0.001, 0.001])
tensor_w_out_i = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_w_out_j = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_w_in = mgr.tensor(np.array([0.001, 0.001]))
tensor_w_out_i = mgr.tensor(np.array([0.0, 0.0, 0.0, 0.0, 0.0]))
tensor_w_out_j = mgr.tensor(np.array([0.0, 0.0, 0.0, 0.0, 0.0]))
tensor_b_in = mgr.tensor([0.0])
tensor_b_out = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_b_in = mgr.tensor(np.array([0.0]))
tensor_b_out = mgr.tensor(np.array([0.0, 0.0, 0.0, 0.0, 0.0]))
tensor_l_out = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
tensor_l_out = mgr.tensor(np.array([0.0, 0.0, 0.0, 0.0, 0.0]))
tensor_m = mgr.tensor([ tensor_y.size() ])
tensor_m = mgr.tensor(np.array([ tensor_y.size() ]))
# We store them in an array for easier interaction
params = [tensor_x_i, tensor_x_j, tensor_y, tensor_w_in, tensor_w_out_i,
@ -91,9 +92,9 @@ def test_logistic_regression():
# Calculate the parameters based on the respective derivatives calculated
for j_iter in range(tensor_b_out.size()):
tensor_w_in[0] -= learning_rate * tensor_w_out_i.data()[j_iter]
tensor_w_in[1] -= learning_rate * tensor_w_out_j.data()[j_iter]
tensor_b_in[0] -= learning_rate * tensor_b_out.data()[j_iter]
tensor_w_in.data()[0] -= learning_rate * tensor_w_out_i.data()[j_iter]
tensor_w_in.data()[1] -= learning_rate * tensor_w_out_j.data()[j_iter]
tensor_b_in.data()[0] -= learning_rate * tensor_b_out.data()[j_iter]
assert tensor_w_in.data()[0] < 0.01
assert tensor_w_in.data()[0] > 0.0