public static History FitLoop(Model model, Func <NDArrayList, float[]> fit_function, NDArrayList fit_inputs, string[] out_labels = null, int?batch_size = null, int epochs = 100,
int verbose = 1, CallbackList callbacks = null, Func <NDArrayList, float[]> val_function = null, NDArrayList val_inputs = null, bool shuffle = true,
string[] callback_metrics = null, int initial_epoch = 0, int?steps_per_epoch = null, int?validation_steps = null)
{
NDArrayList ins_batch = null;
float[] val_outs = null;
float[] outs = null;
Dictionary <string, float> batch_logs;
Model callback_model;
string count_mode;
NDArray index_array = null;
var do_validation = false;
if (val_function != null && val_inputs != null)
{
do_validation = true;
if (verbose > 0 && fit_inputs != null)
{
Console.WriteLine(String.Format("Train on %d samples, validate on %d samples", fit_inputs[0].Shape[0], val_inputs[0].Shape[0]));
}
}
if (validation_steps != null)
{
do_validation = true;
if (steps_per_epoch == null)
{
throw new Exception("Can only use `validation_steps` when doing step-wise training, i.e. `steps_per_epoch` must be set.");
}
}
else if (do_validation)
{
if (!steps_per_epoch.HasValue)
{
throw new Exception("Must specify `validation_steps` to perform validation when doing step-wise training.");
}
}
var num_train_samples = TrainingUtils.CheckNumSamples(fit_inputs, batch_size: batch_size, steps: steps_per_epoch, steps_name: "steps_per_epoch");
if (num_train_samples > 0)
{
index_array = nd.Arange(num_train_samples);
}
model.history = new History();
var _callbacks = new List <Callback> {
new BaseLogger(stateful_metrics: model.stateful_metric_names.ToArray())
};
if (verbose > 0)
{
if (steps_per_epoch != null)
{
count_mode = "steps";
}
else
{
count_mode = "samples";
}
_callbacks.Add(new ProgbarLogger(count_mode, stateful_metrics: model.stateful_metric_names.ToArray()));
}
_callbacks.Add(model.history);
callbacks = new CallbackList(_callbacks.ToArray());
out_labels = out_labels ?? new string[0];
// it's possible to callback a different model than itself
// (used by Sequential models)
callback_model = model;
callbacks.SetModel(callback_model);
callbacks.SetParams(new Dictionary <string, object> {
{
"batch_size",
batch_size
},
{
"epochs",
epochs
},
{
"steps",
steps_per_epoch
},
{
"samples",
num_train_samples
},
{
"verbose",
verbose
},
{
"do_validation",
do_validation
},
{
"metrics",
callback_metrics ?? new string[0]
}
});
callbacks.OnTrainBegin();
callback_model.stop_training = false;
foreach (var cbk in callbacks.callbacks)
{
cbk.validation_data = val_inputs;
}
// To prevent a slowdown,
// we find beforehand the arrays that need conversion.
List <KerasSymbol> feed = new List <KerasSymbol>();
feed.AddRange(model._feed_inputs);
feed.AddRange(model._feed_targets);
feed.AddRange(model._feed_sample_weights);
var indices_for_conversion_to_dense = new List <int>();
foreach (var i in Enumerable.Range(0, feed.Count))
{
if (!K.IsSparse(feed[i]))
{
indices_for_conversion_to_dense.Add(i);
}
}
foreach (var epoch in Enumerable.Range(initial_epoch, epochs - initial_epoch))
{
// Reset stateful metrics
//ToDo: Recheck code
//foreach (var m in model.stateful_metric_functions)
//{
// m.reset_states();
//}
callbacks.OnEpochBegin(epoch);
var epoch_logs = new Dictionary <string, float>
{
};
if (steps_per_epoch != null)
{
foreach (var step_index in Enumerable.Range(0, steps_per_epoch.Value))
{
batch_logs = new Dictionary <string, float>
{
};
batch_logs["batch"] = step_index;
batch_logs["size"] = 1;
callbacks.OnBatchBegin(step_index, batch_logs);
outs = fit_function(fit_inputs);
for (int i = 0; i < out_labels.Length; i++)
{
if (batch_logs.ContainsKey(out_labels[i]))
{
batch_logs.Add(out_labels[i], outs[i]);
}
else
{
batch_logs[out_labels[i]] = outs[i];
}
}
callbacks.OnBatchEnd(step_index, batch_logs);
if (callback_model.stop_training)
{
break;
}
}
if (do_validation)
{
val_outs = TestLoop(model, val_function, val_inputs, steps: validation_steps, verbose: 0);
// Same labels assumed.
for (int i = 0; i < out_labels.Length; i++)
{
var l = "val_" + out_labels[i];
if (epoch_logs.ContainsKey(l))
{
epoch_logs.Add(l, val_outs[i]);
}
else
{
epoch_logs[l] = val_outs[i];
}
}
}
}
else
{
if (shuffle && batch_size.HasValue)
{
index_array = TrainingUtils.BatchShuffle(index_array, batch_size.Value);
}
else if (shuffle)
{
nd.Shuffle(index_array);
}
var batches = TrainingUtils.MakeBatches(num_train_samples, batch_size.Value);
foreach (var _tup_3 in batches.Select((_p_1, _p_2) => Tuple.Create(_p_2, _p_1)))
{
var batch_index = _tup_3.Item1;
var(batch_start, batch_end) = _tup_3.Item2;
var batch_ids = index_array[$"{batch_start}:{batch_end}"];
try
{
ins_batch = GenericUtils.SliceArrays(fit_inputs, batch_ids.ArrayData.Cast <int>().ToArray());
}
catch (Exception ex)
{
throw new Exception("TypeError while preparing batch. If using HDF5 input data, pass shuffle=\"batch\".");
}
batch_logs = new Dictionary <string, float>();
batch_logs["batch"] = batch_index;
batch_logs["size"] = batch_ids.Shape[0];
callbacks.OnBatchBegin(batch_index, batch_logs);
foreach (var i in indices_for_conversion_to_dense)
{
ins_batch[i] = ins_batch[i];
}
outs = fit_function(ins_batch);
for (int i = 0; i < out_labels.Length; i++)
{
if (batch_logs.ContainsKey(out_labels[i]))
{
batch_logs.Add(out_labels[i], outs[i]);
}
else
{
batch_logs[out_labels[i]] = outs[i];
}
}
callbacks.OnBatchEnd(batch_index, batch_logs);
if (callback_model.stop_training)
{
break;
}
if (batch_index == batches.Length - 1)
{
// Last batch.
if (do_validation)
{
val_outs = TestLoop(model, val_function, val_inputs, batch_size: batch_size.Value, verbose: 0);
// Same labels assumed.
for (int i = 0; i < out_labels.Length; i++)
{
var l = "val_" + out_labels[i];
if (epoch_logs.ContainsKey(l))
{
epoch_logs.Add(l, val_outs[i]);
}
else
{
epoch_logs[l] = val_outs[i];
}
}
}
}
}
}
callbacks.OnEpochEnd(epoch, epoch_logs);
if (callback_model.stop_training)
{
break;
}
}
callbacks.OnTrainEnd();
return(model.history);
}
