private float ValAccuracy(uint batch_size, Symbol lenet)
{
uint val_num = val_data.GetShape()[0];
uint correct_count = 0;
uint all_count = 0;
uint start_index = 0;
while (start_index < val_num)
{
if (start_index + batch_size > val_num)
{
start_index = val_num - batch_size;
}
args_map["data"] =
val_data.Slice(start_index, start_index + batch_size).Clone(ctx_dev);
args_map["data_label"] =
val_label.Slice(start_index, start_index + batch_size).Clone(ctx_dev);
start_index += batch_size;
NDArray.WaitAll();
Executor exe = lenet.SimpleBind(ctx_dev, args_map, new XavierInitializer(2));
exe.Forward(false);
var outputs = exe.Outputs;
NDArray out_cpu = outputs[0].Clone(ctx_cpu);
NDArray label_cpu =
val_label.Slice(start_index - batch_size, start_index).Clone(ctx_cpu);
NDArray.WaitAll();
float *dptr_out = out_cpu.GetData();
float *dptr_label = label_cpu.GetData();
for (int i = 0; i < batch_size; ++i)
{
float label = dptr_label[i];
uint cat_num = out_cpu.GetShape()[1];
float p_label = 0, max_p = dptr_out[i * cat_num];
for (int j = 0; j < cat_num; ++j)
{
float p = dptr_out[i * cat_num + j];
if (max_p < p)
{
p_label = j;
max_p = p;
}
}
if (label == p_label)
{
correct_count++;
}
}
all_count += batch_size;
exe.Dispose();
}
return((float)(correct_count * 1.0 / all_count));
}
public unsafe EagerTensorV2(NDArray nd, string device_name = "")
{
if (nd.typecode == NPTypeCode.String)
{
throw new NotImplementedException("Support for NDArray of type string not implemented yet");
}
var arraySlice = nd.Unsafe.Storage.Shape.IsContiguous ? nd.GetData() : nd.CloneData();
_handle = c_api.TF_NewTensor(nd.dtype.as_dtype(),
nd.shape.Select(i => (long)i).ToArray(),
nd.ndim,
new IntPtr(arraySlice.Address),
nd.size * nd.dtypesize,
deallocator: (IntPtr dataPtr, long len, IntPtr args) =>
{
}, IntPtr.Zero);
EagerTensorHandle = c_api.TFE_NewTensorHandle(_handle, tf.status.Handle);
}
public void Exp_Sliced()
{
var a = np.arange(1.0, 81.0);
a = a.reshape(5, 4, 4);
var b = a[":, :, 0"];
var ret = np.exp(b);
for (int i = 0; i < ret.size; i++)
{
Console.WriteLine(ret.GetAtIndex(i));
}
var actual = ret.GetData <double>();
var expected = ret54.GetData <double>();
for (int i = 0; i < actual.Count; i++)
{
(System.Math.Abs((actual[i] / expected[i]) - 1.0) < 0.0000001).Should().BeTrue();
}
}
public String Predict(NDArray nd)
{
nd.CopyTo(_data);
NDArray.WaitAll();
_exe.Forward(false);
List <NDArray> outputs = _exe.Outputs;
NDArray outCpu = outputs[0].Clone(Context.Cpu());
NDArray.WaitAll();
float *p = outCpu.GetData();
int iMax = 0;
float max = p[0];
for (int i = 0; i < 10; i++)
{
if (p[i] > max)
{
max = p[i];
iMax = i;
}
}
return(iMax.ToString());
}