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manually construct graph for retain layer. · feelsyt/TensorFlow.NET@84440b2 · GitHub

/ TensorFlow.NET Public
forked from SciSharp/TensorFlow.NET

Commit 84440b2

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Oceania2018
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manually construct graph for retain layer.
1 parent 5751c20 commit 84440b2

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Lines changed: 1125 additions & 137 deletions

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‎README.md‎

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@@ -140,6 +140,7 @@ Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflow
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* [CNN Text Classification](test/TensorFlowNET.Examples/TextProcess/cnn_models/VdCnn.cs)
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* [Named Entity Recognition](test/TensorFlowNET.Examples/TextProcess/NER)
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* [Transfer Learning for Image Classification in InceptionV3](test/TensorFlowNET.Examples/ImageProcess/RetrainImageClassifier.cs)
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### Contribute:
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‎src/KerasNET.Core/Layers/Dense.cs‎

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@@ -40,7 +40,7 @@ public Tensor __call__(Tensor x)
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var dot = tf.matmul(x, W);
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if (this.activation != null)
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dot = activation.Activate(dot);
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Console.WriteLine("Calling Layer \"" + name + "(" + np.array(dot.getShape().Dimensions).ToString() + ")\" ...");
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Console.WriteLine("Calling Layer \"" + name + "(" + np.array(dot.GetShape().Dimensions).ToString() + ")\" ...");
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return dot;
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}
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public TensorShape __shape__()

‎src/KerasNET.Core/Model.cs‎

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@@ -65,7 +65,7 @@ public Tensor getFlow()
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#endregion
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#region Model Graph Form Layer Stack
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var flow_shape = features.getShape();
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var flow_shape = features.GetShape();
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Flow = features;
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for (int i = 0; i < layer_stack.Count; i++)
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{

‎src/TensorFlowNET.Core/APIs/tf.array.cs‎

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@@ -49,6 +49,20 @@ public static Tensor one_hot(Tensor indices, int depth,
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Tensor off_value = null,
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TF_DataType dtype = TF_DataType.DtInvalid,
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int axis = -1,
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string name = null) => array_ops.one_hot(indices, depth, dtype: dtype, axis: axis, name: name);
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string name = null) => array_ops.one_hot(indices, depth, dtype: dtype, axis: axis, name: name);
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/// <summary>
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/// A placeholder op that passes through `input` when its output is not fed.
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/// </summary>
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/// <typeparam name="T"></typeparam>
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/// <param name="input">A `Tensor`. The default value to produce when output is not fed.</param>
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/// <param name="shape">
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/// A `tf.TensorShape` or list of `int`s. The (possibly partial) shape of
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/// the tensor.
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/// </param>
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/// <param name="name">A name for the operation (optional).</param>
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/// <returns>A `Tensor`. Has the same type as `input`.</returns>
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public static Tensor placeholder_with_default<T>(T input, int[] shape, string name = null)
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=> gen_array_ops.placeholder_with_default(input, shape, name: name);
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}
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}

‎src/TensorFlowNET.Core/APIs/tf.math.cs‎

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@@ -277,9 +277,21 @@ public static Tensor reduce_sum(Tensor input, int? axis = null, int? reduction_i
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}
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public static Tensor reduce_sum(Tensor input, int axis, int? reduction_indices = null)
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{
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return math_ops.reduce_sum(input, axis);
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}
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=> math_ops.reduce_sum(input, axis);
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/// <summary>
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/// Computes the maximum of elements across dimensions of a tensor.
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/// </summary>
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/// <param name="input_tensor"></param>
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/// <param name="axis"></param>
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/// <param name="keepdims"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public static Tensor reduce_max(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
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=> math_ops.reduce_max(input_tensor, axis, keepdims, name);
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public static Tensor reduce_min(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
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=> math_ops.reduce_min(input_tensor, axis, keepdims, name);
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public static Tensor sigmoid<T>(T x, string name = null)
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=> math_ops.sigmoid(x, name: name);

‎src/TensorFlowNET.Core/Framework/common_shapes.py.cs‎

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@@ -37,7 +37,7 @@ public static Tensor _broadcast_shape_helper(Tensor shape_x, Tensor shape_y)
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public static bool has_fully_defined_shape(Tensor tensor)
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{
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return tensor.getShape().is_fully_defined();
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return tensor.GetShape().is_fully_defined();
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}
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}
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}

‎src/TensorFlowNET.Core/Framework/meta_graph.py.cs‎

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@@ -19,7 +19,7 @@ public static MetaGraphDef read_meta_graph_file(string filename)
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return meta_graph_def;
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}
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22-
public static (Dictionary<string, RefVariable>, ITensorOrOperation[]) import_scoped_meta_graph_with_return_elements(MetaGraphDef meta_graph_or_file,
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public static (Dictionary<string, VariableV1>, ITensorOrOperation[]) import_scoped_meta_graph_with_return_elements(MetaGraphDef meta_graph_or_file,
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bool clear_devices = false,
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string import_scope = "",
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Dictionary<string, Tensor> input_map = null,
@@ -61,7 +61,7 @@ public static (Dictionary<string, RefVariable>, ITensorOrOperation[]) import_sco
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return_elements: return_elements);
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6363
// Restores all the other collections.
64-
var variable_objects = new Dictionary<ByteString, RefVariable>();
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var variable_objects = new Dictionary<ByteString, VariableV1>();
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foreach(var col in meta_graph_def.CollectionDef.OrderBy(x => x.Key))
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{
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// Don't add unbound_inputs to the new graph.
@@ -83,11 +83,14 @@ public static (Dictionary<string, RefVariable>, ITensorOrOperation[]) import_sco
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{
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foreach (var value in col.Value.BytesList.Value)
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{
86-
RefVariable variable = null;
86+
VariableV1 variable = null;
8787
if (!variable_objects.ContainsKey(value))
8888
{
8989
var proto = VariableDef.Parser.ParseFrom(value);
90-
variable = new RefVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
90+
if (proto.IsResource)
91+
variable = new ResourceVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
92+
else
93+
variable = new RefVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
9194
variable_objects[value] = variable;
9295
}
9396
variable = variable_objects[value];
@@ -126,9 +129,9 @@ public static (Dictionary<string, RefVariable>, ITensorOrOperation[]) import_sco
126129
}
127130
}
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129-
var variables = graph.get_collection<RefVariable>(ops.GraphKeys.GLOBAL_VARIABLES,
132+
var variables = graph.get_collection<VariableV1>(ops.GraphKeys.GLOBAL_VARIABLES,
130133
scope: scope_to_prepend_to_names);
131-
var var_list = new Dictionary<string, RefVariable>();
134+
var var_list = new Dictionary<string, VariableV1>();
132135
variables.ForEach(v => var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v);
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134137
return (var_list, imported_return_elements);

‎src/TensorFlowNET.Core/Gradients/math_grad.cs‎

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@@ -32,6 +32,27 @@ public static Tensor[] _AddGrad(Operation op, Tensor[] grads)
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return new Tensor[] { r1, r2 };
3333
}
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35+
public static Tensor[] _DivNoNanGrad(Operation op, Tensor[] grads)
36+
{
37+
var grad = grads[0];
38+
var x = op.inputs[0];
39+
var y = op.inputs[1];
40+
var sx = array_ops.shape(x);
41+
var sy = array_ops.shape(y);
42+
var (rx, ry) = gen_array_ops.broadcast_gradient_args(sx, sy);
43+
x = math_ops.conj(x);
44+
y = math_ops.conj(y);
45+
46+
var reduce_sum1 = math_ops.reduce_sum(math_ops.div_no_nan(grad, y), rx);
47+
var reduce_sum2 = math_ops.reduce_sum(grad * math_ops.div_no_nan(math_ops.div_no_nan(-x, y), y), ry);
48+
49+
return new Tensor[]
50+
{
51+
array_ops.reshape(reduce_sum1, sx),
52+
array_ops.reshape(reduce_sum2, sy)
53+
};
54+
}
55+
3556
/// <summary>
3657
/// Returns grad * exp(x).
3758
/// </summary>

‎src/TensorFlowNET.Core/Gradients/nn_grad.cs‎

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@@ -74,6 +74,23 @@ public static Tensor[] _SoftmaxCrossEntropyWithLogitsGrad(Operation op, Tensor[]
7474
};
7575
}
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77+
public static Tensor[] _SparseSoftmaxCrossEntropyWithLogitsGrad(Operation op, Tensor[] grads)
78+
{
79+
var sparse_softmax_grad_without_gradient = array_ops.prevent_gradient(
80+
op.outputs[1],
81+
message: "Currently there is no way to take the second " +
82+
"derivative of sparse_softmax_cross_entropy_with_logits due to the fused " +
83+
"implementation's interaction with tf.gradients()");
84+
85+
var grad_0 = grads[0];
86+
87+
return new Tensor[]
88+
{
89+
_BroadcastMul(grad_0, sparse_softmax_grad_without_gradient),
90+
null
91+
};
92+
}
93+
7794
private static bool IsZero(Tensor g)
7895
{
7996
if (new string[] { "ZerosLike", "Zeros" }.Contains(g.op.type))

‎src/TensorFlowNET.Core/Gradients/ops.gradient_function_mapping.cs‎

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@@ -24,6 +24,8 @@ public static Func<Operation, Tensor[], Tensor[]> get_gradient_function(Operatio
2424
return nn_grad._BiasAddGrad(oper, out_grads);
2525
case "ConcatV2":
2626
return array_grad._ConcatGradV2(oper, out_grads);
27+
case "DivNoNan":
28+
return math_grad._DivNoNanGrad(oper, out_grads);
2729
case "Exp":
2830
return math_grad._ExpGrad(oper, out_grads);
2931
case "Identity":
@@ -62,6 +64,8 @@ public static Func<Operation, Tensor[], Tensor[]> get_gradient_function(Operatio
6264
return nn_grad._SoftmaxGrad(oper, out_grads);
6365
case "SoftmaxCrossEntropyWithLogits":
6466
return nn_grad._SoftmaxCrossEntropyWithLogitsGrad(oper, out_grads);
67+
case "SparseSoftmaxCrossEntropyWithLogits":
68+
return nn_grad._SparseSoftmaxCrossEntropyWithLogitsGrad(oper, out_grads);
6569
case "Transpose":
6670
return array_grad._TransposeGrad(oper, out_grads);
6771
case "TopK":

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