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DigitRecognitionNN works, but the accuracy is only 92% after 10 epoch… · MSavameri/TensorFlow.NET@04c80f3 · GitHub

  1. MSavameri
  2. TensorFlow.NET

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Commit 04c80f3

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Oceania2018
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DigitRecognitionNN works, but the accuracy is only 92% after 10 epochs, it's supposed to be 97%.
1 parent 5ff096c commit 04c80f3

19 files changed

Lines changed: 171 additions & 37 deletions

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‎README.md‎

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@@ -142,13 +142,13 @@ Example runner will download all the required files like training data and model
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* [Logistic Regression](test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs)
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* [Nearest Neighbor](test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs)
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* [Naive Bayes Classification](test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs)
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* [Full Connected Neural Network](test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs)
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* [Image Recognition](test/TensorFlowNET.Examples/ImageProcess)
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* [K-means Clustering](test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs)
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* [NN XOR](test/TensorFlowNET.Examples/BasicModels/NeuralNetXor.cs)
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* [Object Detection](test/TensorFlowNET.Examples/ImageProcess/ObjectDetection.cs)
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* [Text Classification](test/TensorFlowNET.Examples/TextProcess/BinaryTextClassification.cs)
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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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‎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.TensorShape.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.TensorShape;
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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/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.TensorShape.is_fully_defined();
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}
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}
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}

‎src/TensorFlowNET.Core/Keras/Layers/Layer.cs‎

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@@ -161,7 +161,7 @@ protected void _maybe_build(Tensor input)
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if (_dtype == TF_DataType.DtInvalid)
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_dtype = input.dtype;
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var input_shapes = input.GetShape();
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var input_shapes = input.TensorShape;
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build(input_shapes);
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built = true;
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}

‎src/TensorFlowNET.Core/Operations/Losses/losses_impl.py.cs‎

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@@ -118,8 +118,8 @@ public Tensor sparse_softmax_cross_entropy(Tensor labels,
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if(weights > 0)
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{
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var weights_tensor = ops.convert_to_tensor(weights);
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var labels_rank = labels.GetShape().NDim;
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var weights_shape = weights_tensor.GetShape();
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var labels_rank = labels.TensorShape.NDim;
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var weights_shape = weights_tensor.TensorShape;
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var weights_rank = weights_shape.NDim;
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if (labels_rank > -1 && weights_rank > -1)

‎src/TensorFlowNET.Core/Operations/NnOps/_WithSpaceToBatch.cs‎

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@@ -18,7 +18,7 @@ public _WithSpaceToBatch(TensorShape input_shape,
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string data_format = null)
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{
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var dilation_rate_tensor = ops.convert_to_tensor(dilation_rate, TF_DataType.TF_INT32, name: "dilation_rate");
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var rate_shape = dilation_rate_tensor.GetShape();
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var rate_shape = dilation_rate_tensor.TensorShape;
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var num_spatial_dims = rate_shape.Dimensions[0];
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int starting_spatial_dim = -1;
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if (!string.IsNullOrEmpty(data_format) && data_format.StartsWith("NC"))

‎src/TensorFlowNET.Core/Operations/confusion_matrix.py.cs‎

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@@ -24,9 +24,9 @@ public static (Tensor, Tensor) remove_squeezable_dimensions(Tensor labels,
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{
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predictions = ops.convert_to_tensor(predictions);
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labels = ops.convert_to_tensor(labels);
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var predictions_shape = predictions.GetShape();
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var predictions_shape = predictions.TensorShape;
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var predictions_rank = predictions_shape.NDim;
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var labels_shape = labels.GetShape();
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var labels_shape = labels.TensorShape;
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var labels_rank = labels_shape.NDim;
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if(labels_rank > -1 && predictions_rank > -1)
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{

‎src/TensorFlowNET.Core/Operations/nn_ops.cs‎

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@@ -83,7 +83,7 @@ public static Tensor dropout_v2(Tensor x, Tensor rate, Tensor noise_shape = null
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// float to be selected, hence we use a >= comparison.
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var keep_mask = random_tensor >= rate;
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var ret = x * scale * math_ops.cast(keep_mask, x.dtype);
86-
ret.SetShape(x.GetShape());
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ret.SetShape(x.TensorShape);
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return ret;
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});
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}
@@ -131,14 +131,14 @@ public static Tensor sparse_softmax_cross_entropy_with_logits(Tensor labels = nu
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var precise_logits = logits.dtype == TF_DataType.TF_HALF ? math_ops.cast(logits, dtypes.float32) : logits;
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// Store label shape for result later.
134-
var labels_static_shape = labels.GetShape();
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var labels_static_shape = labels.TensorShape;
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var labels_shape = array_ops.shape(labels);
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/*bool static_shapes_fully_defined = (
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labels_static_shape.is_fully_defined() &&
138138
logits.get_shape()[:-1].is_fully_defined());*/
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// Check if no reshapes are required.
141-
if(logits.GetShape().NDim == 2)
141+
if(logits.TensorShape.NDim == 2)
142142
{
143143
var (cost, _) = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
144144
precise_logits, labels, name: name);
@@ -163,7 +163,7 @@ public static Tensor softmax_cross_entropy_with_logits_v2_helper(Tensor labels,
163163
{
164164
var precise_logits = logits;
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var input_rank = array_ops.rank(precise_logits);
166-
var shape = logits.GetShape();
166+
var shape = logits.TensorShape;
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168168
if (axis != -1)
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throw new NotImplementedException("softmax_cross_entropy_with_logits_v2_helper axis != -1");

‎src/TensorFlowNET.Core/Operations/weights_broadcast_ops.cs‎

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@@ -16,8 +16,8 @@ public static Tensor broadcast_weights(Tensor weights, Tensor values)
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weights, dtype: values.dtype.as_base_dtype(), name: "weights");
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// Try static check for exact match.
19-
var weights_shape = weights.GetShape();
20-
var values_shape = values.GetShape();
19+
var weights_shape = weights.TensorShape;
20+
var values_shape = values.TensorShape;
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if (weights_shape.is_fully_defined() &&
2222
values_shape.is_fully_defined())
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return weights;

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