mirror of
https://github.com/lordmathis/CUDANet.git
synced 2025-11-06 09:44:28 +00:00
Add dense sigmoid test
This commit is contained in:
@@ -17,10 +17,13 @@ class DenseLayerTest : public CublasTestFixture {
|
|||||||
std::vector<std::vector<float>>& weights,
|
std::vector<std::vector<float>>& weights,
|
||||||
std::vector<float>& biases,
|
std::vector<float>& biases,
|
||||||
float*& d_input,
|
float*& d_input,
|
||||||
float*& d_output
|
float*& d_output,
|
||||||
|
std::string activation
|
||||||
) {
|
) {
|
||||||
// Create Dense layer
|
// Create Dense layer
|
||||||
Layers::Dense denseLayer(inputSize, outputSize, "linear", cublasHandle);
|
Layers::Dense denseLayer(
|
||||||
|
inputSize, outputSize, activation, cublasHandle
|
||||||
|
);
|
||||||
|
|
||||||
// Set weights and biases
|
// Set weights and biases
|
||||||
denseLayer.setWeights(weights);
|
denseLayer.setWeights(weights);
|
||||||
@@ -61,7 +64,7 @@ TEST_F(DenseLayerTest, Init) {
|
|||||||
// std::cout << "Dense layer: input size = " << inputSize << ",
|
// std::cout << "Dense layer: input size = " << inputSize << ",
|
||||||
// output size = " << outputSize << std::endl;
|
// output size = " << outputSize << std::endl;
|
||||||
Layers::Dense denseLayer(
|
Layers::Dense denseLayer(
|
||||||
inputSize, outputSize, "linear", cublasHandle
|
inputSize, outputSize, "sigmoid", cublasHandle
|
||||||
);
|
);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
@@ -79,12 +82,12 @@ TEST_F(DenseLayerTest, setWeights) {
|
|||||||
{1.3f, 0.5f, 0.0f, 1.7f}
|
{1.3f, 0.5f, 0.0f, 1.7f}
|
||||||
};
|
};
|
||||||
|
|
||||||
Layers::Dense denseLayer(inputSize, outputSize, "linear", cublasHandle);
|
Layers::Dense denseLayer(inputSize, outputSize, "sigmoid", cublasHandle);
|
||||||
|
|
||||||
denseLayer.setWeights(weights);
|
denseLayer.setWeights(weights);
|
||||||
}
|
}
|
||||||
|
|
||||||
TEST_F(DenseLayerTest, ForwardUnitWeightMatrix) {
|
TEST_F(DenseLayerTest, ForwardUnitWeightMatrixLinear) {
|
||||||
int inputSize = 3;
|
int inputSize = 3;
|
||||||
int outputSize = 3;
|
int outputSize = 3;
|
||||||
|
|
||||||
@@ -106,7 +109,8 @@ TEST_F(DenseLayerTest, ForwardUnitWeightMatrix) {
|
|||||||
float* d_output;
|
float* d_output;
|
||||||
|
|
||||||
Layers::Dense denseLayer = commonTestSetup(
|
Layers::Dense denseLayer = commonTestSetup(
|
||||||
inputSize, outputSize, input, weights, biases, d_input, d_output
|
inputSize, outputSize, input, weights, biases, d_input, d_output,
|
||||||
|
"linear"
|
||||||
);
|
);
|
||||||
denseLayer.forward(d_input, d_output);
|
denseLayer.forward(d_input, d_output);
|
||||||
|
|
||||||
@@ -124,7 +128,7 @@ TEST_F(DenseLayerTest, ForwardUnitWeightMatrix) {
|
|||||||
commonTestTeardown(d_input, d_output);
|
commonTestTeardown(d_input, d_output);
|
||||||
}
|
}
|
||||||
|
|
||||||
TEST_F(DenseLayerTest, ForwardRandomWeightMatrix) {
|
TEST_F(DenseLayerTest, ForwardRandomWeightMatrixRelu) {
|
||||||
int inputSize = 5;
|
int inputSize = 5;
|
||||||
int outputSize = 4;
|
int outputSize = 4;
|
||||||
|
|
||||||
@@ -142,7 +146,7 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrix) {
|
|||||||
float* d_output;
|
float* d_output;
|
||||||
|
|
||||||
Layers::Dense denseLayer = commonTestSetup(
|
Layers::Dense denseLayer = commonTestSetup(
|
||||||
inputSize, outputSize, input, weights, biases, d_input, d_output
|
inputSize, outputSize, input, weights, biases, d_input, d_output, "relu"
|
||||||
);
|
);
|
||||||
|
|
||||||
denseLayer.forward(d_input, d_output);
|
denseLayer.forward(d_input, d_output);
|
||||||
@@ -153,7 +157,10 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrix) {
|
|||||||
);
|
);
|
||||||
EXPECT_EQ(cublasStatus, CUBLAS_STATUS_SUCCESS);
|
EXPECT_EQ(cublasStatus, CUBLAS_STATUS_SUCCESS);
|
||||||
|
|
||||||
std::vector<float> expectedOutput = {10.4f, 13.0f, 8.9f, 9.3f};
|
// weights * inputs = 13.1, 17.5, 8.3, 14.8
|
||||||
|
// + biases = 13.3, 18, 9, 15.9
|
||||||
|
|
||||||
|
std::vector<float> expectedOutput = {13.3f, 18.0f, 9.0f, 15.9f};
|
||||||
for (int i = 0; i < outputSize; ++i) {
|
for (int i = 0; i < outputSize; ++i) {
|
||||||
EXPECT_NEAR(
|
EXPECT_NEAR(
|
||||||
output[i], expectedOutput[i], 1e-4
|
output[i], expectedOutput[i], 1e-4
|
||||||
@@ -162,3 +169,50 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrix) {
|
|||||||
|
|
||||||
commonTestTeardown(d_input, d_output);
|
commonTestTeardown(d_input, d_output);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
TEST_F(DenseLayerTest, ForwardRandomWeightMatrixSigmoid) {
|
||||||
|
int inputSize = 5;
|
||||||
|
int outputSize = 4;
|
||||||
|
|
||||||
|
std::vector<float> input = {0.1f, 0.2f, 0.3f, 0.4f, 0.5f};
|
||||||
|
|
||||||
|
std::vector<std::vector<float>> weights = {
|
||||||
|
{0.8f, 0.7f, 0.7f, 0.3f, 0.8f},
|
||||||
|
{0.1f, 0.4f, 0.8f, 0.0f, 0.2f},
|
||||||
|
{0.2f, 0.5f, 0.7f, 0.3f, 0.0f},
|
||||||
|
{0.1f, 0.7f, 0.6f, 1.0f, 0.4f}
|
||||||
|
};
|
||||||
|
std::vector<float> biases = {0.1f, 0.2f, 0.3f, 0.4f};
|
||||||
|
|
||||||
|
float* d_input;
|
||||||
|
float* d_output;
|
||||||
|
|
||||||
|
Layers::Dense denseLayer = commonTestSetup(
|
||||||
|
inputSize, outputSize, input, weights, biases, d_input, d_output,
|
||||||
|
"sigmoid"
|
||||||
|
);
|
||||||
|
|
||||||
|
denseLayer.forward(d_input, d_output);
|
||||||
|
|
||||||
|
std::vector<float> output(outputSize);
|
||||||
|
cublasStatus = cublasGetVector(
|
||||||
|
outputSize, sizeof(float), d_output, 1, output.data(), 1
|
||||||
|
);
|
||||||
|
EXPECT_EQ(cublasStatus, CUBLAS_STATUS_SUCCESS);
|
||||||
|
|
||||||
|
// weights * input = 0.95, 0.43, 0.45, 0.93
|
||||||
|
// + biases = 1.05, 0.63, 0.75, 1.33
|
||||||
|
// sigmoid = 0.740775, 0.652489, 0.679179, 0.790841
|
||||||
|
|
||||||
|
std::vector<float> expectedOutput = {
|
||||||
|
0.740775f, 0.652489f, 0.679179f, 0.790841f
|
||||||
|
};
|
||||||
|
|
||||||
|
for (int i = 0; i < outputSize; ++i) {
|
||||||
|
EXPECT_NEAR(
|
||||||
|
output[i], expectedOutput[i], 1e-5
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
commonTestTeardown(d_input, d_output);
|
||||||
|
}
|
||||||
|
|||||||
Reference in New Issue
Block a user