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Updated python lr impl to use sequence for more efficient management of sequences

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This commit is contained in:
Alejandro Saucedo
2020-11-08 15:56:43 +00:00
parent 93e03ae463
commit 358f496549
1 changed files with 9 additions and 3 deletions
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12
python/test/test_kompute.py
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@@ -189,14 +189,20 @@ def test_logistic_regression_pyshader():
mgr.eval_tensor_create_def(params)
# Record commands for efficient evaluation
sq = mgr.create_sequence()
sq.begin()
sq.record_tensor_sync_device([tensor_w_in, tensor_b_in])
sq.record_algo_data(params, compute_shader.to_spirv())
sq.record_tensor_sync_local([tensor_w_out_i, tensor_w_out_j, tensor_b_out, tensor_l_out])
sq.end()
ITERATIONS = 100
learning_rate = 0.1
# Perform machine learning training and inference across all input X and Y
for i_iter in range(ITERATIONS):
mgr.eval_tensor_sync_device_def([tensor_w_in, tensor_b_in])
mgr.eval_algo_data_def(params, compute_shader.to_spirv())
mgr.eval_tensor_sync_local_def([tensor_w_out_i, tensor_w_out_j, tensor_b_out, tensor_l_out])
sq.eval()
# Calculate the parameters based on the respective derivatives calculated
w_in_i_val = tensor_w_in.data()[0]