public override List <Tensor> Call(List <Array> inputs)
{
var feed_dict = new Dictionary <Tensor, Array>();
foreach (var(tensor, value) in Enumerable.Zip(this.inputs, inputs, (a, b) => (a, b)))
{
// if (is_sparse(tensor))
// {
// sparse_coo = value.tocoo()
// indices = np.concatenate((np.expand_dims(sparse_coo.row, 1),
// np.expand_dims(sparse_coo.col, 1)), 1)
// value = (indices, sparse_coo.data, sparse_coo.shape)
// }
feed_dict[tensor] = value;
}
var session = K._SESSION;
var init = tf.GetGlobalVariablesInitializer();
if (init.Length > 0)
{
Console.WriteLine("Initializing variables:");
foreach (var op in init)
{
Console.WriteLine(" - " + op.Name);
session.Run(new TFOutput[0], new TFTensor[0], new TFOutput[0], new[] { op });
}
Console.WriteLine("Operations:");
foreach (var op in tf.GetEnumerator())
{
Console.WriteLine(" - " + op.Name);
}
Console.WriteLine();
}
//Console.WriteLine("Before:");
//PrintVariables(feed_dict, session);
// Console.ReadKey();
var runner = session.GetRunner();
foreach (var o in this.outputs)
{
runner.Fetch(K.In(o).output);
}
foreach (var op in this.updates_op)
{
runner.AddTarget(op);
}
foreach (KeyValuePair <Tensor, Array> pair in feed_dict)
{
TensorFlowTensor t = K.In(pair.Key);
runner.AddInput(t.output, pair.Value);
}
var updated = runner.Run();
//Console.WriteLine();
//foreach (var v in updated)
//{
// object obj = v.GetValue();
// if (obj is float[,])
// Console.WriteLine((obj as float[,]).ToCSharp());
// else if (obj is float[])
// Console.WriteLine((obj as float[]).ToCSharp());
// else
// Console.WriteLine(obj);
//}
//Console.WriteLine();
//Console.WriteLine();
//Console.WriteLine("After:");
//PrintVariables(feed_dict, session);
return(updated.Get(0, this.outputs.Count).Select(t => K.Out(t)).ToList());
// Console.ReadKey();
}
private void PrintVariables(Dictionary <Tensor, Array> feed_dict, TFSession session)
{
string[] ops =
{
//"SGD/grad/dense_1/dense_1/kernel/var",
//"SGD/grad/dense_2/dense_2/kernel/var",
//"SGD/grad/dense_2/dense_2/bias/var",
//"loss/dense_1_loss/y_true",
//"loss/dense_1_loss/y_pred",
//"loss/dense_1_loss/weights",
//"iterations/var",
//"lr/var",
//"lr_t",
//"p_t",
//"metrics/binary_accuracy/Round0",
//"metrics/binary_accuracy/Cast0",
//"metrics/binary_accuracy/Mean0",
//"metrics/binary_accuracy/Equal0",
//"metrics/binary_accuracy/value",
//"metrics/score_array/mean"
//"beta_1/var",
//"beta_2/var",
//"decay/var",
//"adam/grad/dense_1/dense_1/kernel/var",
//"dense_1/variance_scaling/1/scaled",
//"dense_1/dense_1/kernel/var",
//"dense_1/call/dot",
//"dense_1/call/Sigmoid0",
};
foreach (var op in ops)
{
try
{
var debugRunner = session.GetRunner();
foreach (KeyValuePair <Tensor, Array> pair in feed_dict)
{
TensorFlowTensor t = K.In(pair.Key);
debugRunner.AddInput(t.output, pair.Value);
}
Console.WriteLine(op);
debugRunner.Fetch(op);
var v = debugRunner.Run();
object obj = v[0].GetValue();
if (obj is float[, ])
{
Console.WriteLine((obj as float[, ]).ToCSharp());
}
else if (obj is float[])
{
Console.WriteLine((obj as float[]).ToCSharp());
}
else if (obj is bool[, ])
{
Console.WriteLine((obj as bool[, ]).ToCSharp());
}
else if (obj is bool[])
{
Console.WriteLine((obj as bool[]).ToCSharp());
}
else if (obj is sbyte[, ])
{
Console.WriteLine((obj as sbyte[, ]).ToCSharp());
}
else if (obj is sbyte[])
{
Console.WriteLine((obj as sbyte[]).ToCSharp());
}
else
{
Console.WriteLine(obj);
}
}
catch
{
}
}
}