public void tf_int_shape()
{
using (var K = new TensorFlowBackend())
{
#region doc_int_shape
var input = K.placeholder(shape: new int?[] { 2, 4, 5 });
var a = K.int_shape(input); // (2, 4, 5)
var val = new[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: val);
var b = K.int_shape(kvar); //(2, 2)
#endregion
Assert.AreEqual(new[] { 2, 4, 5 }, a);
Assert.AreEqual(new[] { 2, 2 }, b);
}
}
public void tf_ndim_test()
{
// https://github.com/fchollet/keras/blob/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras/backend/tensorflow_backend.py#L508
using (var K = new TensorFlowBackend())
{
#region doc_ndim
var input = K.placeholder(shape: new int?[] { 2, 4, 5 });
double[,] val = new double[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: (Array)val);
int?a = K.ndim(input); // 3
int?b = K.ndim(kvar); // 2
#endregion
Assert.AreEqual(3, a);
Assert.AreEqual(2, b);
}
}
public void tf_variable_test()
{
using (var K = new TensorFlowBackend())
{
#region doc_variable
double[,] val = new double[, ] {
{ 1, 2 }, { 3, 4 }
};
var kvar = K.variable(array: (Array)val, dtype: DataType.Double, name: "example_var");
var a = K.dtype(kvar); // 'float64'
var b = kvar.eval(); // { { 1, 2 }, { 3, 4 } }
#endregion
Assert.AreEqual(DataType.Double, a);
Assert.AreEqual(new double[, ] {
{ 1, 2 }, { 3, 4 }
}, b);
}
}
public void tf_random_uniform()
{
using (var K = new TensorFlowBackend())
{
#region doc_random_uniform
var kvar = K.random_uniform(new int[] { 100, 2000 }, minval: -4, maxval: 2, dtype: DataType.Double, seed: 1337, name: "uni");
var a = K.dtype(kvar); // float64 (Double)
var b = kvar.eval();
#endregion
double[,] actual = (double[, ])b;
Assert.AreEqual(100, actual.Rows());
Assert.AreEqual(2000, actual.Columns());
var u = UniformContinuousDistribution.Estimate(actual.Reshape());
Assert.AreEqual(-4, u.Minimum, 1e-3);
Assert.AreEqual(+2, u.Maximum, 1e-3);
Assert.AreEqual(-1, u.Mean, 1e-2);
}
}
public void tf_softmax_test()
{
using (var K = new TensorFlowBackend())
{
double[,] a = new double[, ] {
{ -4, 2 }, { 0.02, 0.3 }
};
var ta = K.variable(array: (Array)a, name: "example_var", dtype: DataType.Double);
var tr = K.softmax(ta);
double[,] r = (double[, ])tr.eval();
AssertEx.AreEqual(new double[, ] {
{ 0.0024726231566347748, 0.99752737684336534 },
{ 0.430453776060771, 0.56954622393922893 }
}, r, 1e-8);
AssertEx.AreEqual(r.GetRow(0), Accord.Math.Special.Softmax(a.GetRow(0)), 1e-8);
AssertEx.AreEqual(r.GetRow(1), Accord.Math.Special.Softmax(a.GetRow(1)), 1e-8);
}
}
public void tf_zeros()
{
using (var K = new TensorFlowBackend())
{
#region doc_zeros
var kvar = K.zeros(new int[] { 3, 4 });
var a = K.eval(kvar); // new[,] {{ 0., 0., 0., 0.},
// { 0., 0., 0., 0.},
// { 0., 0., 0., 0.}}
#endregion
float[,] actual = (float[, ])a;
Assert.AreEqual(3, actual.Rows());
Assert.AreEqual(4, actual.Columns());
double[,] expected = new[, ] {
{ 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0 }
};
Assert.AreEqual(expected, actual);
}
}
