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lostmsulostmsu
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use preview 6.1
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‎BasicMath/BasicMath.csproj‎

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6.1" />
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</ItemGroup>
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</Project>

‎CharRNN/CharRNN.csproj‎

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<ItemGroup>
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<PackageReference Include="CommandLineParser" Version="2.3.0" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6.1" />
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<PackageReference Include="Newtonsoft.Json" Version="12.0.1" />
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</ItemGroup>
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‎FSharp/BasicMathF/BasicMathF.fsproj‎

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<ItemGroup>
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<PackageReference Include="FSharp.Interop.Dynamic" Version="4.0.3.130" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6.1" />
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</ItemGroup>
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</Project>

‎FSharp/FashionMnistF/FashionMnistF.fsproj‎

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<ItemGroup>
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<PackageReference Include="FSharp.Interop.Dynamic" Version="4.0.3.130" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6.1" />
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</ItemGroup>
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</Project>

‎FashionMnistClassification/FashionMnistClassification.csproj‎

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6.1" />
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</ItemGroup>
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</Project>

‎GPT-2/GPT-2.csproj‎

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<ItemGroup>
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<PackageReference Include="CsvHelper" Version="12.1.2" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6.1" />
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<PackageReference Include="ManyConsole.CommandLineUtils" Version="1.0.3-alpha" />
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<PackageReference Include="morelinq" Version="3.1.0" />
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<PackageReference Include="Newtonsoft.Json" Version="12.0.1" />

‎GPT-2/Gpt2Model.cs‎

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}
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static Tensor GeLU(Tensor input) =>
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((dynamic)input * 0.5) * (tf.tanh_dyn((input + tf.pow(input, 3) * 0.044715) * Math.Sqrt(2 / Math.PI)) + 1);
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((dynamic)input * 0.5) * (tf.tanh((input + tf.pow(input, 3) * 0.044715) * Math.Sqrt(2 / Math.PI)) + 1);
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/// <summary>
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/// Normalize to mean = 0, std = 1, then do a diagonal affine transform.

‎GPT-2/Gpt2Sampler.cs‎

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using numpy;
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using tensorflow;
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using tensorflow.contrib.training;
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using tensorflow.python.framework.dtypes;
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using tensorflow.python.ops.variable_scope;
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public static class Gpt2Sampler
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Tensor[] Body(object past, dynamic prev, object output)
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{
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var nextOutputs = Step(hParams, prev[Range.All, tf.newaxis], past: past);
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Tensor logits = nextOutputs["logits"][Range.All, -1, Range.All] / tf.to_float(temperature);
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Tensor logits = nextOutputs["logits"][Range.All, -1, Range.All] / tf.constant(temperature, dtypes.float32_ref);
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logits = TopLogits(logits, topK: topK);
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var samples = tf.multinomial_dyn(logits, num_samples: 1, output_dtype: tf.int32);
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return new Tensor[]

‎LinearSVM/LinearSVM.csproj‎

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</PropertyGroup>
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6" />
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<PackageReference Include="Gradient" Version="0.1.10-tech-preview6.1" />
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<PackageReference Include="ManyConsole.CommandLineUtils" Version="1.0.3-alpha" />
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</ItemGroup>
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‎LinearSVM/LinearSvmProgram.cs‎

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dynamic Loss(dynamic W, dynamic b, dynamic inputData, dynamic targetData) {
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var logits = tf.subtract(tf.matmul(inputData, W), b);
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var normTerm = tf.divide(tf.reduce_sum(tf.multiply(tf.transpose(W), W)), 2);
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var classificationLoss = tf.reduce_mean(tf.maximum(0.0, tf.subtract(this.flags.Delta, tf.multiply(logits, targetData))));
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var classificationLoss = tf.reduce_mean(tf.maximum(tf.constant(0.0), tf.subtract(this.flags.Delta, tf.multiply(logits, targetData))));
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var totalLoss = tf.add_dyn(tf.multiply(this.flags.C, classificationLoss), tf.multiply(this.flags.Reg, normTerm));
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return totalLoss;
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}

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