public static void PrintSummary(Model model, int?line_length = null, double[] positions = null, Action <string> print_fn = null)
{
int trainable_count = 0;
List <string> to_display = new List <string>();
bool sequential_like = false;
var relevant_nodes = new List <Node>();
if (print_fn == null)
{
print_fn = Console.WriteLine;
}
if (model.GetType().Name == "Sequential")
{
sequential_like = true;
}
else if (!model._is_graph_network)
{
// We treat subclassed models as a simple sequence of layers,
// for logging purposes.
sequential_like = true;
}
else
{
sequential_like = true;
var nodes_by_depth = model._nodes_by_depth.Values;
var nodes = new List <Node>();
foreach (var v in nodes_by_depth)
{
if (v.Count > 1 || v.Count == 1 && v[0].inbound_layers.Length > 1)
{
// if the model has multiple nodes
// or if the nodes have multiple inbound_layers
// the model is no longer sequential
sequential_like = false;
break;
}
nodes.AddRange(v);
}
if (sequential_like)
{
// search for shared layers
foreach (var layer in model.Layers)
{
var flag = false;
foreach (var node in layer._inbound_nodes)
{
if (nodes.Contains(node))
{
if (flag)
{
sequential_like = false;
break;
}
else
{
flag = true;
}
}
}
if (!sequential_like)
{
break;
}
}
}
}
if (sequential_like)
{
line_length = line_length ?? 65;
positions = positions ?? new double[] {
0.45,
0.85,
1.0
};
if (positions.Last() <= 1)
{
positions = (from p in positions
select line_length.Value * p).ToArray();
}
// header names for the different log elements
to_display = new List <string> {
"Layer (type)",
"Output Shape",
"Param #"
};
}
else
{
line_length = line_length ?? 98;
positions = positions ?? new double[] {
0.33,
0.55,
0.67,
1.0
};
if (positions.Last() <= 1)
{
positions = (from p in positions
select line_length.Value * p).ToArray();
}
// header names for the different log elements
to_display = new List <string> {
"Layer (type)",
"Output Shape",
"Param #",
"Connected to"
};
foreach (var v in model._nodes_by_depth.Values)
{
relevant_nodes.AddRange(v);
}
}
Action <string[], double[]> print_row = (fields, _positions) => {
var line = "";
foreach (var i in Enumerable.Range(0, fields.Length))
{
if (i > 0)
{
line = line.Substring(0, line.Length - 1) + " ";
}
line += fields[i].ToString();
line = line.Substring(0, Convert.ToInt32(_positions[i]));
var lineCharCount = Convert.ToInt32(_positions[i] - line.Length);
foreach (var item in Enumerable.Range(0, lineCharCount))
{
line += " ";
}
}
print_fn(line);
};
string msg = "";
foreach (var item in Enumerable.Range(0, line_length.Value))
{
msg += "_";
}
print_fn(msg);
print_row(to_display.ToArray(), positions);
msg = "";
foreach (var item in Enumerable.Range(0, line_length.Value))
{
msg += "=";
}
print_fn(msg);
Action <Layer> print_layer_summary = layer => {
string output_shape;
try
{
output_shape = layer.OutputShape.ToString();
}
catch (Exception)
{
output_shape = "multiple";
}
var name = layer.name;
var cls_name = layer.GetType().Name;
var fields = new List <string> {
name + " (" + cls_name + ")",
output_shape.ToString(),
layer.CountParams().ToString()
};
print_row(fields.ToArray(), positions);
};
Action <Layer> print_layer_summary_with_connections = layer => {
string first_connection;
string output_shape;
try
{
output_shape = layer.OutputShape.ToString();
}
catch (Exception)
{
output_shape = "multiple";
}
var connections = new List <string>();
foreach (var node in layer._inbound_nodes)
{
if (relevant_nodes.Count > 0 && !relevant_nodes.Contains(node))
{
// node is not part of the current network
continue;
}
foreach (var i in Enumerable.Range(0, node.inbound_layers.Length))
{
var inbound_layer = node.inbound_layers[i].name;
var inbound_node_index = node.node_indices[i];
var inbound_tensor_index = node.tensor_indices[i];
connections.Add(inbound_layer + "[" + inbound_node_index.ToString() + "][" + inbound_tensor_index.ToString() + "]");
}
}
var name = layer.name;
var cls_name = layer.GetType().Name;
if (connections.Count == 0)
{
first_connection = "";
}
else
{
first_connection = connections[0];
}
var fields = new List <string> {
name + " (" + cls_name + ")",
output_shape,
layer.CountParams().ToString(),
first_connection
};
print_row(fields.ToArray(), positions);
if (connections.Count > 1)
{
foreach (var i in Enumerable.Range(1, connections.Count - 1))
{
fields = new List <string> {
"",
"",
"",
connections[i]
};
print_row(fields.ToArray(), positions);
}
}
};
var layers = model.Layers;
foreach (var i in Enumerable.Range(0, layers.Length))
{
if (sequential_like)
{
print_layer_summary(layers[i]);
}
else
{
print_layer_summary_with_connections(layers[i]);
}
if (i == layers.Length - 1)
{
msg = "";
foreach (var item in Enumerable.Range(0, line_length.Value))
{
msg += "=";
}
print_fn(msg);
}
else
{
msg = "";
foreach (var item in Enumerable.Range(0, line_length.Value))
{
msg += "=";
}
print_fn(msg);
}
}
if (model._collected_trainable_weights != null)
{
trainable_count = model._collected_trainable_weights.Select(x => K.CountParams(x)).Sum();
}
else
{
trainable_count = model.TrainableWeights.Select(x => K.CountParams(x)).Sum();
}
var non_trainable_count = model.NonTrainableWeights.Select(x => K.CountParams(x)).Sum();
print_fn($"Total params: {trainable_count + non_trainable_count}");
print_fn($"Trainable params: {trainable_count}");
print_fn($"Non-trainable params: {non_trainable_count}");
msg = "";
foreach (var item in Enumerable.Range(0, line_length.Value))
{
msg += "_";
}
print_fn(msg);
}
public static int CountParams(KerasSymbol[] weights)
{
return(weights.Select(x => K.CountParams(x)).ToList().Sum());
}