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Merge branch 'master' into v0.20-tensorflow2.0
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‎.gitignore‎

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@@ -336,3 +336,4 @@ test/TensorFlowNET.Examples/mnist
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# training model resources
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.resources
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/redist

‎README.md‎

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@@ -29,10 +29,10 @@ In comparison to other projects, like for instance TensorFlowSharp which only pr
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| TensorFlow | tf 1.13 | tf 1.14 | tf 1.15 | tf 2.0 |
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| ----------- | ------- | ------- | ------- | ------ |
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| tf.net 0.12 | | x | | |
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| tf.net 0.14 | | x | x | |
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| tf.net 0.13 | | x | x | |
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| tf.net 0.12 | x | x | | |
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| tf.net 0.11 | x | x | | |
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| tf.net 0.10 | x | x | | |
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| tf.net 0.9 | x | | | |
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Install TF.NET and TensorFlow binary through NuGet.
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```sh

‎TensorFlow.NET.sln‎

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@@ -3,16 +3,24 @@ Microsoft Visual Studio Solution File, Format Version 12.00
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6+
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EndProject
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16+
Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Hub", "src\TensorFlowNET.Hub\Tensorflow.Hub.csproj", "{95B077C1-E21B-486F-8BDD-1C902FE687AB}"
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE

‎docs/source/ImageRecognition.md‎

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2/18/2019 3:56:25 AM Completed InceptionArchGoogLeNet
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```
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You can find the full source code from [github](https://github.com/SciSharp/TensorFlow.NET/tree/master/test/TensorFlowNET.Examples/ImageProcess).
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You can find the full source code from [github](https://github.com/SciSharp/TensorFlow.NET-Examples/tree/master/src/TensorFlowNET.Examples/ImageProcessing).
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‎docs/source/MnistInRnn.md‎

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# Chapter. MNIST In RNN
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### Recurrent Neural Networks
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Recurrent Neural Networks (RNNs) are popular models that have shown great promise in sequential data classification task. The traditional neural network model cannot make the next prediction input based on the knowledge that has been learned before.

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

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/// <param name="data"></param>
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/// <returns></returns>
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public static Tensor dynamic_stitch(Tensor[] indices, Tensor[] data, string name = null)
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public Tensor dynamic_stitch(Tensor[] indices, Tensor[] data, string name = null)
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=> gen_data_flow_ops.dynamic_stitch(indices, data, name: name);
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/// <summary>
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/// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
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/// </summary>
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/// <param name="data"></param>
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/// <param name="partitions"></param>
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/// <param name="num_partitions">The number of partitions to output.</param>
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/// <param name="name"></param>
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/// <returns></returns>
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public Tensor[] dynamic_partition(Tensor data, Tensor partitions, int num_partitions,
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string name = null)
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=> gen_data_flow_ops.dynamic_partition(data, partitions, num_partitions, name: name);
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}
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}

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

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public partial class tensorflow
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{
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public GraphTransformer graph_transforms => new GraphTransformer();
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public GraphKeys GraphKeys { get; } = new GraphKeys();
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public void reset_default_graph()

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

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{
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public GFile gfile = new GFile();
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public Tensor read_file(string filename, string name = null) => gen_io_ops.read_file(filename, name);
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public Tensor read_file(Tensor filename, string name = null) => gen_io_ops.read_file(filename, name);
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public void import_graph_def(GraphDef graph_def,
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public ITensorOrOperation[] import_graph_def(GraphDef graph_def,
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‎src/TensorFlowNET.Core/APIs/tf.layers.cs‎

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trainable: trainable);
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trainable: trainable,
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name: name);
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}

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

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Original file line numberDiff line numberDiff line change
@@ -251,10 +251,11 @@ public Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip_value_
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/// greater than <c>clip_value_max</c> are set to <c>clip_value_max</c>.
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/// </remarks>
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public Tensor clip_by_value (Tensor t, Tensor clip_value_min, Tensor clip_value_max, string name = "ClipByValue")
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=> gen_ops.clip_by_value(t, clip_value_min, clip_value_max, name);
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=> clip_ops.clip_by_value(t, clip_value_min, clip_value_max, name);
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public Tensor sub<Tx, Ty>(Tx a, Ty b, string name = null)
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=> gen_math_ops.sub(a, b, name: name);
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public Tensor sub(Tensor a, Tensor b)
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=> gen_math_ops.sub(a, b);
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public Tensor divide(Tensor a, Tensor b)
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@@ -474,7 +475,7 @@ public Tensor sum(Tensor input, int axis, bool keep_dims = false, string name =
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public Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
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public Tensor reduce_mean(Tensor[] input_tensors, int axis, bool keepdims = false, string name = null)
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public Tensor reduce_mean(Tensor[] input_tensors, int? axis = null, bool keepdims = false, string name = null)
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=> math_ops.reduce_mean(input_tensors, axis: axis, keepdims: keepdims, name: name);
479480

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public Tensor round(Tensor x, string name = null)

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