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Oceania2018Oceania2018
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Gradient function for Conv2D
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‎src/TensorFlowNET.Core/Gradients/nn_grad.cs‎

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@@ -97,6 +97,50 @@ public static Tensor[] _SparseSoftmaxCrossEntropyWithLogitsGrad(Operation op, Te
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};
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}
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/// <summary>
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/// Gradient function for Conv2D.
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/// </summary>
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/// <param name="op"></param>
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/// <param name="grads"></param>
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/// <returns></returns>
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[RegisterGradient("Conv2D")]
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public static Tensor[] _Conv2DGrad(Operation op, Tensor[] grads)
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{
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var dilations = op.get_attr("dilations");
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var strides = op.get_attr("strides");
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var padding = op.get_attr("padding");
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var explicit_paddings = op.get_attr("explicit_paddings");
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var use_cudnn_on_gpu = op.get_attr("use_cudnn_on_gpu");
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var data_format = op.get_attr("data_format");
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var shape = gen_array_ops.shape_n(new Tensor[] { op.inputs[0], op.inputs[1] });
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return new Tensor[]
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{
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gen_nn_ops.conv2d_backprop_input(new Conv2dParams
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{
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InputSizes = shape[0],
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Filter = op.inputs[1],
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Dilations = dilations == null ? null : dilations as int[],
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Strides = strides == null ? null : strides as int[],
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Padding = padding.ToString(),
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ExplicitPaddings = explicit_paddings == null ? null : explicit_paddings as int[],
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UseCudnnOnGpu = (bool)use_cudnn_on_gpu,
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DataFormat = data_format.ToString()
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}),
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gen_nn_ops.conv2d_backprop_filter(new Conv2dParams
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{
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Input = op.inputs[0],
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FilterSizes = shape[1],
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Dilations = dilations == null ? null : dilations as int[],
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Strides = strides == null ? null : strides as int[],
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Padding = padding.ToString(),
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ExplicitPaddings = explicit_paddings == null ? null : explicit_paddings as int[],
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UseCudnnOnGpu = (bool)use_cudnn_on_gpu,
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DataFormat = data_format.ToString()
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})
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};
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}
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private static bool IsZero(Tensor g)
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{
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if (new string[] { "ZerosLike", "Zeros" }.Contains(g.op.type))

‎src/TensorFlowNET.Core/Operations/NnOps/Conv2dParams.cs‎

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@@ -22,11 +22,27 @@ public class Conv2dParams
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/// </summary>
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public Tensor Input { get; set; }
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/// <summary>
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/// An integer vector representing the shape of `input`
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/// </summary>
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public Tensor InputSizes { get; set; }
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/// <summary>
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/// A 4-D tensor of shape
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/// </summary>
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public Tensor Filter { get; set; }
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/// <summary>
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/// An integer vector representing the tensor shape of `filter`
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/// </summary>
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public Tensor FilterSizes { get; set; }
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/// <summary>
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/// A `Tensor`. Must have the same type as `filter`.
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/// 4-D with shape `[batch, out_height, out_width, out_channels]`.
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/// </summary>
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public Tensor OutBackProp { get; set; }
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/// <summary>
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/// The stride of the sliding window for each
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/// dimension of `input`. The dimension order is determined by the value of

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

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@@ -43,6 +43,52 @@ public static Tensor conv2d(Conv2dParams parameters)
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});
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return _op.outputs[0];
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}
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/// <summary>
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/// Computes the gradients of convolution with respect to the filter.
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/// </summary>
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/// <param name="parameters"></param>
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/// <returns></returns>
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public static Tensor conv2d_backprop_filter(Conv2dParams parameters)
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{
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var _op = _op_def_lib._apply_op_helper("Conv2DBackpropFilter", name: parameters.Name, args: new
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{
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input = parameters.Input,
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filter_sizes = parameters.FilterSizes,
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out_backprop = parameters.OutBackProp,
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strides = parameters.Strides,
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padding = parameters.Padding,
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use_cudnn_on_gpu = parameters.UseCudnnOnGpu,
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explicit_paddings = parameters.ExplicitPaddings,
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data_format = parameters.DataFormat,
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dilations = parameters.Dilations
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});
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return _op.outputs[0];
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}
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/// <summary>
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/// Computes the gradients of convolution with respect to the input.
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/// </summary>
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/// <param name="parameters"></param>
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/// <returns></returns>
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public static Tensor conv2d_backprop_input(Conv2dParams parameters)
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{
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var _op = _op_def_lib._apply_op_helper("Conv2DBackpropInput", name: parameters.Name, args: new
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{
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input_sizes = parameters.InputSizes,
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filter = parameters.Filter,
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out_backprop = parameters.OutBackProp,
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strides = parameters.Strides,
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padding = parameters.Padding,
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use_cudnn_on_gpu = parameters.UseCudnnOnGpu,
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explicit_paddings = parameters.ExplicitPaddings,
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data_format = parameters.DataFormat,
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dilations = parameters.Dilations
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});
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return _op.outputs[0];
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}
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public static Tensor bias_add(Tensor value,

‎src/TensorFlowNET.Core/Operations/gen_array_ops.cs‎

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@@ -252,6 +252,19 @@ public static Tensor shape(Tensor input, TF_DataType out_type = TF_DataType.TF_I
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return _op.outputs[0];
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}
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/// <summary>
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/// Returns shape of tensors.
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/// </summary>
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/// <param name="input"></param>
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/// <param name="out_type"></param>
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/// <param name="name"></param>
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/// <returns></returns>
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public static Tensor[] shape_n(Tensor[] input, TF_DataType out_type = TF_DataType.TF_INT32, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("ShapeN", name, new { input, out_type });
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return _op.outputs;
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}
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public static Tensor size(Tensor input, TF_DataType out_type = TF_DataType.TF_INT32, string name = null)
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{
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var _op = _op_def_lib._apply_op_helper("Size", name, new { input, out_type });

‎src/TensorFlowNET.Core/TensorFlowNET.Core.csproj‎

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@@ -20,8 +20,9 @@ Docs: https://tensorflownet.readthedocs.io</Description>
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<AssemblyVersion>0.8.1.0</AssemblyVersion>
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<PackageReleaseNotes>Changes since v0.8:
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1. Removed global static graph instance.
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2. Provide custom gradient function.</PackageReleaseNotes>
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1. Remove global static graph instance.
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2. Provide custom gradient function.
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3. Add gradient function for Conv2D.</PackageReleaseNotes>
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<LangVersion>7.2</LangVersion>
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<FileVersion>0.8.1.0</FileVersion>
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</PropertyGroup>

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