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Partial implementation of tf.keras. SciSharp#355 
1 parent 34efa15 commit 5f1f598

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Lines changed: 329 additions & 196 deletions

‎TensorFlow.NET.sln‎

Lines changed: 1 addition & 53 deletions
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@@ -9,11 +9,7 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Benchmark", "src
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.UnitTest", "test\TensorFlowNET.UnitTest\Tensorflow.UnitTest.csproj", "{23C28035-2FCE-41F3-9A12-E73CE8A5AE32}"
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12+
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Global
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‎src/TensorFlowNET.Core/APIs/tf.layers.cs‎

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@@ -14,9 +14,11 @@ You may obtain a copy of the License at
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limitations under the License.
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******************************************************************************/
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using NumSharp;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Operations.Activation;
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using static Tensorflow.Binding;
@@ -173,14 +175,19 @@ public Tensor dense(Tensor inputs,
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if (bias_initializer == null)
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bias_initializer = tf.zeros_initializer;
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var layer = new Dense(units, activation,
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use_bias: use_bias,
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bias_initializer: bias_initializer,
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kernel_initializer: kernel_initializer,
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trainable: trainable,
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name: name);
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return layer.apply(inputs).Item1;
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var layer = new Dense(new DenseArgs
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{
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Units = units,
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Activation = activation,
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UseBias = use_bias,
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BiasInitializer = bias_initializer,
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KernelInitializer = kernel_initializer,
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Trainable = trainable,
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Name = name
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});
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throw new NotImplementedException("");
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//return layer.apply(inputs).Item1;
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}
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/// <summary>

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

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@@ -515,6 +515,9 @@ public Tensor sigmoid<T>(T x, string name = null)
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public Tensor sum(Tensor input, int axis, bool keep_dims = false, string name = null)
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=> gen_math_ops._sum(input, axis, keep_dims: keep_dims, name: name);
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public Tensor reduce_mean(Tensor input_tensors, int axis, bool keepdims = false, string name = null)
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=> math_ops.reduce_mean(input_tensors, axis: new[] { axis }, keepdims: keepdims, name: 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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=> math_ops.reduce_mean(input_tensor, axis: axis, keepdims: keepdims, name: name, reduction_indices: reduction_indices);
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‎src/TensorFlowNET.Core/Data/DatasetManager.cs‎

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@@ -7,7 +7,7 @@ namespace Tensorflow
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{
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public class DatasetManager
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{
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public IDatasetV2 from_tensor_slices(NDArray features, NDArray labels)
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public IDatasetV2 from_tensor_slices(Tensor features, Tensor labels)
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=> new TensorSliceDataset(features, labels);
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}
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}

‎src/TensorFlowNET.Core/Data/TensorSliceDataset.cs‎

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@@ -11,9 +11,9 @@ namespace Tensorflow
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{
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public class TensorSliceDataset : DatasetSource
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{
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public TensorSliceDataset(NDArray features, NDArray labels)
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public TensorSliceDataset(Tensor features, Tensor labels)
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{
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_tensors = new[] { tf.convert_to_tensor(features), tf.convert_to_tensor(labels) };
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_tensors = new[] { features, labels };
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var batched_spec = _tensors.Select(x => x.ToTensorSpec()).ToArray();
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structure = batched_spec.Select(x => x._unbatch()).ToArray();
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‎src/TensorFlowNET.Core/Exceptions/InvalidArgumentError.cs‎

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@@ -0,0 +1,17 @@
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using System;
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namespace Tensorflow
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{
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public class InvalidArgumentError : TensorflowException
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{
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public InvalidArgumentError() : base()
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{
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}
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public InvalidArgumentError(string message) : base(message)
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{
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}
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}
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}

‎src/TensorFlowNET.Core/Gradients/GradientTape.cs‎

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@@ -119,7 +119,7 @@ public Tensor gradient(Tensor target, ResourceVariable source)
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return (results[0], results[1]);
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}
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public Tensor[] gradient(Tensor target, ResourceVariable[] sources)
122+
public Tensor[] gradient(Tensor target, IEnumerable<IVariableV1> sources)
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{
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if (_recording)
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{

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/DenseArgs.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow.Operations.Activation;
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using static Tensorflow.Binding;
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class DenseArgs : LayerArgs
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{
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/// <summary>
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/// Positive integer, dimensionality of the output space.
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/// </summary>
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public int Units { get; set; }
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/// <summary>
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/// Activation function to use.
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/// </summary>
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public IActivation Activation { get; set; }
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/// <summary>
22+
/// Whether the layer uses a bias vector.
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/// </summary>
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public bool UseBias { get; set; } = true;
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/// <summary>
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/// Initializer for the `kernel` weights matrix.
28+
/// </summary>
29+
public IInitializer KernelInitializer { get; set; } = tf.glorot_uniform_initializer;
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/// <summary>
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/// Initializer for the bias vector.
33+
/// </summary>
34+
public IInitializer BiasInitializer { get; set; } = tf.zeros_initializer;
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/// <summary>
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/// Regularizer function applied to the `kernel` weights matrix.
38+
/// </summary>
39+
public IInitializer KernelRegularizer { get; set; }
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41+
/// <summary>
42+
/// Regularizer function applied to the bias vector.
43+
/// </summary>
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public IInitializer BiasRegularizer { get; set; }
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/// <summary>
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/// Constraint function applied to the `kernel` weights matrix.
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/// </summary>
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public Action KernelConstraint { get; set; }
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/// <summary>
52+
/// Constraint function applied to the bias vector.
53+
/// </summary>
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public Action BiasConstraint { get; set; }
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/LayerArgs.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.ArgsDefinition
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{
7+
public class LayerArgs
8+
{
9+
/// <summary>
10+
/// Indicates whether the layer's weights are updated during training
11+
/// and whether the layer's updates are run during training.
12+
/// </summary>
13+
public bool Trainable { get; set; } = true;
14+
15+
public string Name { get; set; }
16+
17+
/// <summary>
18+
/// Only applicable to input layers.
19+
/// </summary>
20+
public TF_DataType DType { get; set; }
21+
22+
/// <summary>
23+
/// Whether the `call` method can be used to build a TF graph without issues.
24+
/// This attribute has no effect if the model is created using the Functional
25+
/// API. Instead, `model.dynamic` is determined based on the internal layers.
26+
/// </summary>
27+
public bool Dynamic { get; set; } = false;
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29+
/// <summary>
30+
/// Only applicable to input layers.
31+
/// </summary>
32+
public TensorShape InputShape { get; set; }
33+
34+
/// <summary>
35+
/// Only applicable to input layers.
36+
/// </summary>
37+
public TensorShape BatchInputShape { get; set; }
38+
39+
/// <summary>
40+
/// Initial weight values.
41+
/// </summary>
42+
public float[] Weights { get; set; }
43+
44+
/// <summary>
45+
/// Regularizer function applied to the output of the layer(its "activation").
46+
/// </summary>
47+
public IInitializer ActivityRegularizer { get; set; }
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public bool Autocast { get; set; }
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/ModelArgs.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.ArgsDefinition
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{
7+
public class ModelArgs : LayerArgs
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{
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}
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}

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