public void cntk_partial_shape_test()
{
// Note: Keras/TensorFlow represent unknown dimensions
// as None, whereas TensorFlowSharp represents as -1:
/*
* import keras
*
* from keras.models import Sequential
* from keras.layers import Dense
* from keras import backend as K
* import numpy as np
*
* a = K.placeholder(shape = (None, 2))
* b = K.variable(np.matrix([[1, 2, 3], [4, 5, 6]]))
* ab = K.dot(a, b)
*
* shape_a = K.int_shape(a)
* shape_b = K.int_shape(b)
* shape_ab = K.int_shape(ab)
*
* print(shape_a)
* print(shape_b)
* print(shape_ab)
*
* >>> Using TensorFlow backend.
* (None, 2)
* (2, 3)
* (None, 3)
*/
using (var K = new CNTKBackend())
{
Tensor a = K.placeholder(shape: new int?[] { null, 2 });
Tensor b = K.variable(array: new float[, ] {
{ 1, 2, 3 },
{ 4, 5, 6 }
});
var ab = K.dot(a, b);
int?[] shape_a = K.int_shape(a);
int?[] shape_b = K.int_shape(b);
int?[] shape_ab = K.int_shape(ab);
NDShape tf_shape_a = K.In(a).CNTK_Shape;
NDShape tf_shape_b = K.In(b).CNTK_Shape;
NDShape tf_shape_ab = K.In(ab).CNTK_Shape;
AssertEx.AreEqual(new int?[] { null, 2 }, shape_a);
AssertEx.AreEqual(new int?[] { 2, 3 }, shape_b);
AssertEx.AreEqual(new int?[] { null, 3 }, shape_ab);
Assert.AreEqual(NDShape.CreateNDShape(new[] { NDShape.FreeDimension, 2 }), tf_shape_a);
Assert.AreEqual(NDShape.CreateNDShape(new[] { 2, 3 }), tf_shape_b);
Assert.AreEqual(NDShape.CreateNDShape(new[] { NDShape.FreeDimension, 3 }), tf_shape_ab);
}
}
public void cntk_toString()
{
using (var K = new CNTKBackend())
{
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);
string a = input.ToString();
string b = kvar.ToString();
Assert.IsTrue(Regex.Match(a, "KerasSharp\\.Engine.Topology\\.Tensor 'CompositeFunction\\d+' shape=\\[2, 4, 5\\] dtype=Float").Success);
Assert.IsTrue(Regex.Match(b, "KerasSharp\\.Engine.Topology\\.Tensor 'CompositeFunction\\d+' shape=\\[2, 2\\] dtype=Double").Success);
}
}
public void cntk_int_shape()
{
using (var K = new CNTKBackend())
{
#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 cntk_ndim_test()
{
// https://github.com/fchollet/keras/blob/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras/backend/tensorflow_backend.py#L508
using (var K = new CNTKBackend())
{
#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);
}
}