diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/ELUArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/ELUArgs.cs index 23552316..e830e5bf 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/ELUArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/ELUArgs.cs @@ -1,9 +1,12 @@ -using System; +using Newtonsoft.Json; +using System; using System.Collections.Generic; using System.Text; namespace Tensorflow.Keras.ArgsDefinition { - public class ELUArgs : LayerArgs { - public float Alpha { get; set; } = 0.1f; - } + public class ELUArgs : AutoSerializeLayerArgs + { + [JsonProperty("alpha")] + public float Alpha { get; set; } = 0.1f; + } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/LeakyReLuArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/LeakyReLuArgs.cs index 6bdb294c..6d953134 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/LeakyReLuArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/LeakyReLuArgs.cs @@ -1,14 +1,16 @@ -using System; +using Newtonsoft.Json; +using System; using System.Collections.Generic; using System.Text; namespace Tensorflow.Keras.ArgsDefinition { - public class LeakyReLuArgs : LayerArgs + public class LeakyReLuArgs : AutoSerializeLayerArgs { /// /// Negative slope coefficient. /// + [JsonProperty("alpha")] public float Alpha { get; set; } = 0.3f; } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/SoftmaxArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/SoftmaxArgs.cs index a37973bc..1c1d147f 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/SoftmaxArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/SoftmaxArgs.cs @@ -4,15 +4,9 @@ using System.Collections.Generic; using System.Text; namespace Tensorflow.Keras.ArgsDefinition { - public class SoftmaxArgs : LayerArgs + public class SoftmaxArgs : AutoSerializeLayerArgs { [JsonProperty("axis")] public Axis axis { get; set; } = -1; - [JsonProperty("name")] - public override string Name { get => base.Name; set => base.Name = value; } - [JsonProperty("trainable")] - public override bool Trainable { get => base.Trainable; set => base.Trainable = value; } - [JsonProperty("dtype")] - public override TF_DataType DType { get => base.DType; set => base.DType = value; } } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/AttentionArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/AttentionArgs.cs index 73477c58..4cdfb46b 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/AttentionArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/AttentionArgs.cs @@ -1,3 +1,5 @@ +using Newtonsoft.Json; + namespace Tensorflow.Keras.ArgsDefinition { public class AttentionArgs : BaseDenseAttentionArgs @@ -6,6 +8,7 @@ namespace Tensorflow.Keras.ArgsDefinition /// /// If `true`, will create a scalar variable to scale the attention scores. /// + [JsonProperty("use_scale")] public bool use_scale { get; set; } = false; /// @@ -14,6 +17,7 @@ namespace Tensorflow.Keras.ArgsDefinition /// and key vectors. `"concat"` refers to the hyperbolic tangent of the /// concatenation of the query and key vectors. /// + [JsonProperty("score_mode")] public string score_mode { get; set; } = "dot"; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/BaseDenseAttentionArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/BaseDenseAttentionArgs.cs index b2a0c3a5..0ef01737 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/BaseDenseAttentionArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/BaseDenseAttentionArgs.cs @@ -1,6 +1,8 @@ +using Newtonsoft.Json; + namespace Tensorflow.Keras.ArgsDefinition { - public class BaseDenseAttentionArgs : LayerArgs + public class BaseDenseAttentionArgs : AutoSerializeLayerArgs { /// @@ -14,6 +16,7 @@ namespace Tensorflow.Keras.ArgsDefinition /// Float between 0 and 1. Fraction of the units to drop for the /// attention scores. /// + [JsonProperty("dropout")] public float dropout { get; set; } = 0f; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/MultiHeadAttentionArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/MultiHeadAttentionArgs.cs index 21b2d218..077dea89 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/MultiHeadAttentionArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/MultiHeadAttentionArgs.cs @@ -1,22 +1,40 @@ +using Newtonsoft.Json; using System; using static Tensorflow.Binding; namespace Tensorflow.Keras.ArgsDefinition { - public class MultiHeadAttentionArgs : LayerArgs + public class MultiHeadAttentionArgs : AutoSerializeLayerArgs { + [JsonProperty("num_heads")] public int NumHeads { get; set; } + [JsonProperty("key_dim")] public int KeyDim { get; set; } + [JsonProperty("value_dim")] public int? ValueDim { get; set; } = null; + [JsonProperty("dropout")] public float Dropout { get; set; } = 0f; + [JsonProperty("use_bias")] public bool UseBias { get; set; } = true; + [JsonProperty("output_shape")] public Shape OutputShape { get; set; } = null; + [JsonProperty("attention_axes")] public Shape AttentionAxis { get; set; } = null; + [JsonProperty("kernel_initializer")] public IInitializer KernelInitializer { get; set; } = tf.glorot_uniform_initializer; + [JsonProperty("bias_initializer")] public IInitializer BiasInitializer { get; set; } = tf.zeros_initializer; + [JsonProperty("kernel_regularizer")] public IRegularizer KernelRegularizer { get; set; } = null; + [JsonProperty("bias_regularizer")] public IRegularizer BiasRegularizer { get; set; } = null; + [JsonProperty("kernel_constraint")] public Action KernelConstraint { get; set; } = null; + [JsonProperty("bias_constraint")] public Action BiasConstraint { get; set; } = null; + [JsonProperty("activity_regularizer")] + public override IRegularizer ActivityRegularizer { get => base.ActivityRegularizer; set => base.ActivityRegularizer = value; } + + // TODO: Add `key_shape`, `value_shape`, `query_shape`. } } \ No newline at end of file diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/AutoSerializeLayerArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/AutoSerializeLayerArgs.cs index 66b34a1a..1a97b013 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/AutoSerializeLayerArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/AutoSerializeLayerArgs.cs @@ -5,6 +5,12 @@ using System.Text; namespace Tensorflow.Keras.ArgsDefinition { + /// + /// This class has nothing but the attributes different from `LayerArgs`. + /// It's used to serialize the model to `tf` format. + /// If the `get_config` of a `Layer` in python code of tensorflow contains `super().get_config`, + /// then the Arg definition should inherit `utoSerializeLayerArgs` instead of `LayerArgs`. + /// public class AutoSerializeLayerArgs: LayerArgs { [JsonProperty("name")] diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Convolution/ConvolutionalArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Convolution/ConvolutionalArgs.cs index 4f050228..08d563c1 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Convolution/ConvolutionalArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Convolution/ConvolutionalArgs.cs @@ -1,31 +1,65 @@ -using System; +using Newtonsoft.Json; +using System; using static Tensorflow.Binding; namespace Tensorflow.Keras.ArgsDefinition { - public class ConvolutionalArgs : LayerArgs + public class ConvolutionalArgs : AutoSerializeLayerArgs { public int Rank { get; set; } = 2; + [JsonProperty("filters")] public int Filters { get; set; } public int NumSpatialDims { get; set; } = Unknown; + [JsonProperty("kernel_size")] public Shape KernelSize { get; set; } = 5; /// /// specifying the stride length of the convolution. /// + [JsonProperty("strides")] public Shape Strides { get; set; } = (1, 1); - + [JsonProperty("padding")] public string Padding { get; set; } = "valid"; + [JsonProperty("data_format")] public string DataFormat { get; set; } + [JsonProperty("dilation_rate")] public Shape DilationRate { get; set; } = (1, 1); + [JsonProperty("groups")] public int Groups { get; set; } = 1; public Activation Activation { get; set; } + private string _activationName; + [JsonProperty("activation")] + public string ActivationName + { + get + { + if (string.IsNullOrEmpty(_activationName)) + { + return Activation.Method.Name; + } + else + { + return _activationName; + } + } + set + { + _activationName = value; + } + } + [JsonProperty("use_bias")] public bool UseBias { get; set; } + [JsonProperty("kernel_initializer")] public IInitializer KernelInitializer { get; set; } = tf.glorot_uniform_initializer; + [JsonProperty("bias_initializer")] public IInitializer BiasInitializer { get; set; } = tf.zeros_initializer; + [JsonProperty("kernel_regularizer")] public IRegularizer KernelRegularizer { get; set; } + [JsonProperty("bias_regularizer")] public IRegularizer BiasRegularizer { get; set; } + [JsonProperty("kernel_constraint")] public Action KernelConstraint { get; set; } + [JsonProperty("bias_constraint")] public Action BiasConstraint { get; set; } } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/EinsumDenseArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/EinsumDenseArgs.cs similarity index 65% rename from src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/EinsumDenseArgs.cs rename to src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/EinsumDenseArgs.cs index 3a8642ff..9817e9c6 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Attention/EinsumDenseArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/EinsumDenseArgs.cs @@ -1,9 +1,10 @@ +using Newtonsoft.Json; using System; using static Tensorflow.Binding; -namespace Tensorflow.Keras.ArgsDefinition +namespace Tensorflow.Keras.ArgsDefinition.Core { - public class EinsumDenseArgs : LayerArgs + public class EinsumDenseArgs : AutoSerializeLayerArgs { /// /// An equation describing the einsum to perform. This equation must @@ -11,6 +12,7 @@ namespace Tensorflow.Keras.ArgsDefinition /// `ab...,bc->ac...` where 'ab', 'bc', and 'ac' can be any valid einsum axis /// expression sequence. /// + [JsonProperty("equation")] public string Equation { get; set; } /// @@ -19,6 +21,7 @@ namespace Tensorflow.Keras.ArgsDefinition /// None for any dimension that is unknown or can be inferred from the input /// shape. /// + [JsonProperty("output_shape")] public Shape OutputShape { get; set; } /// @@ -26,41 +29,70 @@ namespace Tensorflow.Keras.ArgsDefinition /// Each character in the `bias_axes` string should correspond to a character /// in the output portion of the `equation` string. /// + [JsonProperty("bias_axes")] public string BiasAxes { get; set; } = null; /// /// Activation function to use. /// public Activation Activation { get; set; } + private string _activationName; + [JsonProperty("activation")] + public string ActivationName + { + get + { + if (string.IsNullOrEmpty(_activationName)) + { + return Activation.Method.Name; + } + else + { + return _activationName; + } + } + set + { + _activationName = value; + } + } /// /// Initializer for the `kernel` weights matrix. /// + [JsonProperty("kernel_initializer")] public IInitializer KernelInitializer { get; set; } = tf.glorot_uniform_initializer; /// /// Initializer for the bias vector. /// + [JsonProperty("bias_initializer")] public IInitializer BiasInitializer { get; set; } = tf.zeros_initializer; /// /// Regularizer function applied to the `kernel` weights matrix. /// + [JsonProperty("kernel_regularizer")] public IRegularizer KernelRegularizer { get; set; } /// /// Regularizer function applied to the bias vector. /// + [JsonProperty("bias_regularizer")] public IRegularizer BiasRegularizer { get; set; } /// /// Constraint function applied to the `kernel` weights matrix. /// + [JsonProperty("kernel_constraint")] public Action KernelConstraint { get; set; } /// /// Constraint function applied to the bias vector. /// + [JsonProperty("bias_constraint")] public Action BiasConstraint { get; set; } + [JsonProperty("activity_regularizer")] + public override IRegularizer ActivityRegularizer { get => base.ActivityRegularizer; set => base.ActivityRegularizer = value; } } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/EmbeddingArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/EmbeddingArgs.cs index b1f4fddd..c462961b 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/EmbeddingArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Core/EmbeddingArgs.cs @@ -1,11 +1,22 @@ -namespace Tensorflow.Keras.ArgsDefinition +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition { - public class EmbeddingArgs : LayerArgs + public class EmbeddingArgs : AutoSerializeLayerArgs { + [JsonProperty("input_dim")] public int InputDim { get; set; } + [JsonProperty("output_dim")] public int OutputDim { get; set; } + [JsonProperty("mask_zero")] public bool MaskZero { get; set; } + [JsonProperty("input_length")] public int InputLength { get; set; } = -1; + [JsonProperty("embeddings_initializer")] public IInitializer EmbeddingsInitializer { get; set; } + [JsonProperty("activity_regularizer")] + public override IRegularizer ActivityRegularizer { get => base.ActivityRegularizer; set => base.ActivityRegularizer = value; } + + // TODO: `embeddings_regularizer`, `embeddings_constraint`. } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping2DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping2DArgs.cs deleted file mode 100644 index 16705063..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping2DArgs.cs +++ /dev/null @@ -1,16 +0,0 @@ -using Tensorflow.NumPy; - -namespace Tensorflow.Keras.ArgsDefinition { - public class Cropping2DArgs : LayerArgs { - /// - /// channel last: (b, h, w, c) - /// channels_first: (b, c, h, w) - /// - public enum DataFormat { channels_first = 0, channels_last = 1 } - /// - /// Accept: int[1][2], int[1][1], int[2][2] - /// - public NDArray cropping { get; set; } - public DataFormat data_format { get; set; } = DataFormat.channels_last; - } -} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping3DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping3DArgs.cs deleted file mode 100644 index 9da2adc7..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping3DArgs.cs +++ /dev/null @@ -1,16 +0,0 @@ -using Tensorflow.NumPy; - -namespace Tensorflow.Keras.ArgsDefinition { - public class Cropping3DArgs : LayerArgs { - /// - /// channel last: (b, h, w, c) - /// channels_first: (b, c, h, w) - /// - public enum DataFormat { channels_first = 0, channels_last = 1 } - /// - /// Accept: int[1][3], int[1][1], int[3][2] - /// - public NDArray cropping { get; set; } - public DataFormat data_format { get; set; } = DataFormat.channels_last; - } -} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/CroppingArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/CroppingArgs.cs deleted file mode 100644 index 9d23acd4..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/CroppingArgs.cs +++ /dev/null @@ -1,10 +0,0 @@ -using Tensorflow.NumPy; - -namespace Tensorflow.Keras.ArgsDefinition { - public class CroppingArgs : LayerArgs { - /// - /// Accept length 1 or 2 - /// - public NDArray cropping { get; set; } - } -} \ No newline at end of file diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMCellArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMCellArgs.cs deleted file mode 100644 index fb0868dc..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMCellArgs.cs +++ /dev/null @@ -1,6 +0,0 @@ -namespace Tensorflow.Keras.ArgsDefinition.Lstm -{ - public class LSTMCellArgs : LayerArgs - { - } -} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Merging/MergeArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Merging/MergeArgs.cs index 3e6791e3..0140b3dd 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Merging/MergeArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Merging/MergeArgs.cs @@ -4,6 +4,7 @@ using System.Text; namespace Tensorflow.Keras.ArgsDefinition { + // TODO: complete the implementation public class MergeArgs : LayerArgs { public Tensors Inputs { get; set; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/BatchNormalizationArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/BatchNormalizationArgs.cs index 954ede57..6ee91e80 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/BatchNormalizationArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/BatchNormalizationArgs.cs @@ -1,21 +1,37 @@ -using static Tensorflow.Binding; +using Newtonsoft.Json; +using static Tensorflow.Binding; namespace Tensorflow.Keras.ArgsDefinition { - public class BatchNormalizationArgs : LayerArgs + public class BatchNormalizationArgs : AutoSerializeLayerArgs { + [JsonProperty("axis")] public Shape Axis { get; set; } = -1; + [JsonProperty("momentum")] public float Momentum { get; set; } = 0.99f; + [JsonProperty("epsilon")] public float Epsilon { get; set; } = 1e-3f; + [JsonProperty("center")] public bool Center { get; set; } = true; + [JsonProperty("scale")] public bool Scale { get; set; } = true; + [JsonProperty("beta_initializer")] public IInitializer BetaInitializer { get; set; } = tf.zeros_initializer; + [JsonProperty("gamma_initializer")] public IInitializer GammaInitializer { get; set; } = tf.ones_initializer; + [JsonProperty("moving_mean_initializer")] public IInitializer MovingMeanInitializer { get; set; } = tf.zeros_initializer; + [JsonProperty("moving_variance_initializer")] public IInitializer MovingVarianceInitializer { get; set; } = tf.ones_initializer; + [JsonProperty("beta_regularizer")] public IRegularizer BetaRegularizer { get; set; } + [JsonProperty("gamma_regularizer")] public IRegularizer GammaRegularizer { get; set; } + // TODO: `beta_constraint` and `gamma_constraint`. + [JsonProperty("renorm")] public bool Renorm { get; set; } + // TODO: `renorm_clipping` and `virtual_batch_size`. + [JsonProperty("renorm_momentum")] public float RenormMomentum { get; set; } = 0.99f; } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/LayerNormalizationArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/LayerNormalizationArgs.cs index 13fd98b4..1ac661b3 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/LayerNormalizationArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Normalization/LayerNormalizationArgs.cs @@ -1,16 +1,27 @@ -using static Tensorflow.Binding; +using Newtonsoft.Json; +using static Tensorflow.Binding; namespace Tensorflow.Keras.ArgsDefinition { - public class LayerNormalizationArgs : LayerArgs + public class LayerNormalizationArgs : AutoSerializeLayerArgs { + [JsonProperty("axis")] public Axis Axis { get; set; } = -1; + [JsonProperty("epsilon")] public float Epsilon { get; set; } = 1e-3f; + [JsonProperty("center")] public bool Center { get; set; } = true; + [JsonProperty("scale")] public bool Scale { get; set; } = true; + [JsonProperty("beta_initializer")] public IInitializer BetaInitializer { get; set; } = tf.zeros_initializer; + [JsonProperty("gamma_initializer")] public IInitializer GammaInitializer { get; set; } = tf.ones_initializer; + [JsonProperty("beta_regularizer")] public IRegularizer BetaRegularizer { get; set; } + [JsonProperty("gamma_regularizer")] public IRegularizer GammaRegularizer { get; set; } + + // TODO: `beta_constraint` and `gamma_constraint`. } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling1DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling1DArgs.cs index 9742203d..c5fdca67 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling1DArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling1DArgs.cs @@ -1,6 +1,8 @@ -namespace Tensorflow.Keras.ArgsDefinition +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition { - public class Pooling1DArgs : LayerArgs + public class Pooling1DArgs : AutoSerializeLayerArgs { /// /// The pooling function to apply, e.g. `tf.nn.max_pool2d`. @@ -10,11 +12,13 @@ /// /// specifying the size of the pooling window. /// + [JsonProperty("pool_size")] public int PoolSize { get; set; } /// /// specifying the strides of the pooling operation. /// + [JsonProperty("strides")] public int Strides { get { return _strides.HasValue ? _strides.Value : PoolSize; } set { _strides = value; } @@ -24,11 +28,13 @@ /// /// The padding method, either 'valid' or 'same'. /// + [JsonProperty("padding")] public string Padding { get; set; } = "valid"; /// /// one of `channels_last` (default) or `channels_first`. /// + [JsonProperty("data_format")] public string DataFormat { get; set; } } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling2DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling2DArgs.cs index 1260af4c..91a372ef 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling2DArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling/Pooling2DArgs.cs @@ -1,6 +1,8 @@ -namespace Tensorflow.Keras.ArgsDefinition +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition { - public class Pooling2DArgs : LayerArgs + public class Pooling2DArgs : AutoSerializeLayerArgs { /// /// The pooling function to apply, e.g. `tf.nn.max_pool2d`. @@ -10,21 +12,25 @@ /// /// specifying the size of the pooling window. /// + [JsonProperty("pool_size")] public Shape PoolSize { get; set; } /// /// specifying the strides of the pooling operation. /// + [JsonProperty("strides")] public Shape Strides { get; set; } /// /// The padding method, either 'valid' or 'same'. /// + [JsonProperty("padding")] public string Padding { get; set; } = "valid"; /// /// one of `channels_last` (default) or `channels_first`. /// + [JsonProperty("data_format")] public string DataFormat { get; set; } } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/PreprocessingLayerArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/PreprocessingLayerArgs.cs index 28ccf9f7..97cb364d 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/PreprocessingLayerArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/PreprocessingLayerArgs.cs @@ -4,7 +4,7 @@ using System.Text; namespace Tensorflow.Keras.ArgsDefinition { - public class PreprocessingLayerArgs : LayerArgs + public class PreprocessingLayerArgs : AutoSerializeLayerArgs { } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/RescalingArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/RescalingArgs.cs new file mode 100644 index 00000000..154bd8c8 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/RescalingArgs.cs @@ -0,0 +1,12 @@ +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition +{ + public class RescalingArgs : AutoSerializeLayerArgs + { + [JsonProperty("scale")] + public float Scale { get; set; } + [JsonProperty("offset")] + public float Offset { get; set; } + } +} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/ResizingArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/ResizingArgs.cs index cf11595e..39fa5221 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/ResizingArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/ResizingArgs.cs @@ -1,5 +1,6 @@ namespace Tensorflow.Keras.ArgsDefinition { + // TODO: no corresponding class found in keras python, maybe obselete? public class ResizingArgs : PreprocessingLayerArgs { public int Height { get; set; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/TextVectorizationArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/TextVectorizationArgs.cs index ddeadc00..1a7149f5 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/TextVectorizationArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Preprocessing/TextVectorizationArgs.cs @@ -1,4 +1,5 @@ -using System; +using Newtonsoft.Json; +using System; using System.Collections.Generic; using System.Text; @@ -6,11 +7,19 @@ namespace Tensorflow.Keras.ArgsDefinition { public class TextVectorizationArgs : PreprocessingLayerArgs { + [JsonProperty("standardize")] public Func Standardize { get; set; } + [JsonProperty("split")] public string Split { get; set; } = "standardize"; + [JsonProperty("max_tokens")] public int MaxTokens { get; set; } = -1; + [JsonProperty("output_mode")] public string OutputMode { get; set; } = "int"; + [JsonProperty("output_sequence_length")] public int OutputSequenceLength { get; set; } = -1; + [JsonProperty("vocabulary")] public string[] Vocabulary { get; set; } + + // TODO: Add `ngrams`, `sparse`, `ragged`, `idf_weights`, `encoding` } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Regularization/DropoutArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Regularization/DropoutArgs.cs index c41c6fe8..1c85d493 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Regularization/DropoutArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Regularization/DropoutArgs.cs @@ -1,21 +1,26 @@ -namespace Tensorflow.Keras.ArgsDefinition +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition { - public class DropoutArgs : LayerArgs + public class DropoutArgs : AutoSerializeLayerArgs { /// /// Float between 0 and 1. Fraction of the input units to drop. /// + [JsonProperty("rate")] public float Rate { get; set; } /// /// 1D integer tensor representing the shape of the /// binary dropout mask that will be multiplied with the input. /// + [JsonProperty("noise_shape")] public Shape NoiseShape { get; set; } /// /// random seed. /// + [JsonProperty("seed")] public int? Seed { get; set; } public bool SupportsMasking { get; set; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rescaling/RescalingArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rescaling/RescalingArgs.cs deleted file mode 100644 index ec9b5315..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rescaling/RescalingArgs.cs +++ /dev/null @@ -1,8 +0,0 @@ -namespace Tensorflow.Keras.ArgsDefinition -{ - public class RescalingArgs : LayerArgs - { - public float Scale { get; set; } - public float Offset { get; set; } - } -} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/Cropping2DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/Cropping2DArgs.cs new file mode 100644 index 00000000..8c262639 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/Cropping2DArgs.cs @@ -0,0 +1,18 @@ +using Tensorflow.NumPy; + +namespace Tensorflow.Keras.ArgsDefinition.Reshaping +{ + public class Cropping2DArgs : LayerArgs + { + /// + /// channel last: (b, h, w, c) + /// channels_first: (b, c, h, w) + /// + public enum DataFormat { channels_first = 0, channels_last = 1 } + /// + /// Accept: int[1][2], int[1][1], int[2][2] + /// + public NDArray cropping { get; set; } + public DataFormat data_format { get; set; } = DataFormat.channels_last; + } +} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/Cropping3DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/Cropping3DArgs.cs new file mode 100644 index 00000000..2d98e55d --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/Cropping3DArgs.cs @@ -0,0 +1,18 @@ +using Tensorflow.NumPy; + +namespace Tensorflow.Keras.ArgsDefinition.Reshaping +{ + public class Cropping3DArgs : LayerArgs + { + /// + /// channel last: (b, h, w, c) + /// channels_first: (b, c, h, w) + /// + public enum DataFormat { channels_first = 0, channels_last = 1 } + /// + /// Accept: int[1][3], int[1][1], int[3][2] + /// + public NDArray cropping { get; set; } + public DataFormat data_format { get; set; } = DataFormat.channels_last; + } +} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/CroppingArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/CroppingArgs.cs new file mode 100644 index 00000000..21b85966 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/CroppingArgs.cs @@ -0,0 +1,12 @@ +using Tensorflow.NumPy; + +namespace Tensorflow.Keras.ArgsDefinition.Reshaping +{ + public class Cropping1DArgs : LayerArgs + { + /// + /// Accept length 1 or 2 + /// + public NDArray cropping { get; set; } + } +} \ No newline at end of file diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/PermuteArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/PermuteArgs.cs index 2686f6cd..92be10ab 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/PermuteArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/PermuteArgs.cs @@ -1,5 +1,9 @@ -namespace Tensorflow.Keras.ArgsDefinition { - public class PermuteArgs : LayerArgs { - public int[] dims { get; set; } - } +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition { + public class PermuteArgs : AutoSerializeLayerArgs + { + [JsonProperty("dims")] + public int[] dims { get; set; } + } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ReshapeArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ReshapeArgs.cs index 77bca8ad..4d1123c8 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ReshapeArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ReshapeArgs.cs @@ -1,7 +1,10 @@ -namespace Tensorflow.Keras.ArgsDefinition +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition { - public class ReshapeArgs : LayerArgs + public class ReshapeArgs : AutoSerializeLayerArgs { + [JsonProperty("target_shape")] public Shape TargetShape { get; set; } public object[] TargetShapeObjects { get; set; } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/UpSampling2DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/UpSampling2DArgs.cs index 7fdda32d..b35e0e4b 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/UpSampling2DArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/UpSampling2DArgs.cs @@ -1,12 +1,17 @@ -namespace Tensorflow.Keras.ArgsDefinition +using Newtonsoft.Json; + +namespace Tensorflow.Keras.ArgsDefinition { - public class UpSampling2DArgs : LayerArgs + public class UpSampling2DArgs : AutoSerializeLayerArgs { + [JsonProperty("size")] public Shape Size { get; set; } + [JsonProperty("data_format")] public string DataFormat { get; set; } /// /// 'nearest', 'bilinear' /// + [JsonProperty("interpolation")] public string Interpolation { get; set; } = "nearest"; } } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ZeroPadding2DArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ZeroPadding2DArgs.cs index ed6e7cc9..4831e435 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ZeroPadding2DArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ZeroPadding2DArgs.cs @@ -2,6 +2,7 @@ namespace Tensorflow.Keras.ArgsDefinition { + // TODO: complete the implementation public class ZeroPadding2DArgs : LayerArgs { public NDArray Padding { get; set; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMArgs.cs similarity index 67% rename from src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMArgs.cs rename to src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMArgs.cs index b08d21d8..76464147 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMArgs.cs @@ -1,9 +1,8 @@ -using Tensorflow.Keras.ArgsDefinition.Rnn; - -namespace Tensorflow.Keras.ArgsDefinition.Lstm +namespace Tensorflow.Keras.ArgsDefinition.Rnn { public class LSTMArgs : RNNArgs { + // TODO: maybe change the `RNNArgs` and implement this class. public bool UnitForgetBias { get; set; } public float Dropout { get; set; } public float RecurrentDropout { get; set; } diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMCellArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMCellArgs.cs new file mode 100644 index 00000000..594c99bb --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/LSTMCellArgs.cs @@ -0,0 +1,7 @@ +namespace Tensorflow.Keras.ArgsDefinition.Rnn +{ + // TODO: complete the implementation + public class LSTMCellArgs : LayerArgs + { + } +} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs index da527925..2585592c 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs @@ -1,21 +1,30 @@ -using System.Collections.Generic; +using Newtonsoft.Json; +using System.Collections.Generic; namespace Tensorflow.Keras.ArgsDefinition.Rnn { - public class RNNArgs : LayerArgs + public class RNNArgs : AutoSerializeLayerArgs { public interface IRnnArgCell : ILayer { object state_size { get; } } - + [JsonProperty("cell")] + // TODO: the cell should be serialized with `serialize_keras_object`. public IRnnArgCell Cell { get; set; } = null; + [JsonProperty("return_sequences")] public bool ReturnSequences { get; set; } = false; + [JsonProperty("return_state")] public bool ReturnState { get; set; } = false; + [JsonProperty("go_backwards")] public bool GoBackwards { get; set; } = false; + [JsonProperty("stateful")] public bool Stateful { get; set; } = false; + [JsonProperty("unroll")] public bool Unroll { get; set; } = false; + [JsonProperty("time_major")] public bool TimeMajor { get; set; } = false; + // TODO: Add `num_constants` and `zero_output_for_mask`. public Dictionary Kwargs { get; set; } = null; public int Units { get; set; } diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Cropping.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Cropping.cs index 602e7a88..3578652e 100644 --- a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Cropping.cs +++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Cropping.cs @@ -1,5 +1,5 @@ using System; -using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Reshaping; using Tensorflow.NumPy; namespace Tensorflow.Keras.Layers diff --git a/src/TensorFlowNET.Keras/Activations.cs b/src/TensorFlowNET.Keras/Activations.cs new file mode 100644 index 00000000..444c783e --- /dev/null +++ b/src/TensorFlowNET.Keras/Activations.cs @@ -0,0 +1,82 @@ +using System; +using System.Collections.Generic; +using System.Text; +using Tensorflow.Operations.Activation; +using static Tensorflow.Binding; + +namespace Tensorflow.Keras +{ + public class Activations + { + private static Dictionary _nameActivationMap; + private static Dictionary _activationNameMap; + + private static Activation _linear = (features, name) => features; + private static Activation _relu = (features, name) + => tf.Context.ExecuteOp("Relu", name, new ExecuteOpArgs(features)); + private static Activation _sigmoid = (features, name) + => tf.Context.ExecuteOp("Sigmoid", name, new ExecuteOpArgs(features)); + private static Activation _softmax = (features, name) + => tf.Context.ExecuteOp("Softmax", name, new ExecuteOpArgs(features)); + private static Activation _tanh = (features, name) + => tf.Context.ExecuteOp("Tanh", name, new ExecuteOpArgs(features)); + + /// + /// Register the name-activation mapping in this static class. + /// + /// + /// + private static void RegisterActivation(string name, Activation activation) + { + _nameActivationMap[name] = activation; + _activationNameMap[activation] = name; + } + + static Activations() + { + _nameActivationMap = new Dictionary(); + _activationNameMap= new Dictionary(); + + RegisterActivation("relu", _relu); + RegisterActivation("linear", _linear); + RegisterActivation("sigmoid", _sigmoid); + RegisterActivation("softmax", _softmax); + RegisterActivation("tanh", _tanh); + } + + public Activation Linear => _linear; + + public Activation Relu => _relu; + + public Activation Sigmoid => _sigmoid; + + public Activation Softmax => _softmax; + + public Activation Tanh => _tanh; + + + public static Activation GetActivationByName(string name) + { + if (!_nameActivationMap.TryGetValue(name, out var res)) + { + throw new Exception($"Activation {name} not found"); + } + else + { + return res; + } + } + + public static string GetNameByActivation(Activation activation) + { + if(!_activationNameMap.TryGetValue(activation, out var name)) + { + throw new Exception($"Activation {activation} not found"); + } + else + { + return name; + } + } + } +} diff --git a/src/TensorFlowNET.Keras/Activations/Activations.Linear.cs b/src/TensorFlowNET.Keras/Activations/Activations.Linear.cs deleted file mode 100644 index acd4de6e..00000000 --- a/src/TensorFlowNET.Keras/Activations/Activations.Linear.cs +++ /dev/null @@ -1,10 +0,0 @@ -namespace Tensorflow.Keras -{ - public partial class Activations - { - /// - /// Linear activation function (pass-through). - /// - public Activation Linear = (features, name) => features; - } -} diff --git a/src/TensorFlowNET.Keras/Activations/Activations.Relu.cs b/src/TensorFlowNET.Keras/Activations/Activations.Relu.cs deleted file mode 100644 index dfebfb29..00000000 --- a/src/TensorFlowNET.Keras/Activations/Activations.Relu.cs +++ /dev/null @@ -1,10 +0,0 @@ -using static Tensorflow.Binding; - -namespace Tensorflow.Keras -{ - public partial class Activations - { - public Activation Relu = (features, name) - => tf.Context.ExecuteOp("Relu", name, new ExecuteOpArgs(features)); - } -} diff --git a/src/TensorFlowNET.Keras/Activations/Activations.Sigmoid.cs b/src/TensorFlowNET.Keras/Activations/Activations.Sigmoid.cs deleted file mode 100644 index ad900bde..00000000 --- a/src/TensorFlowNET.Keras/Activations/Activations.Sigmoid.cs +++ /dev/null @@ -1,11 +0,0 @@ -using System; -using static Tensorflow.Binding; - -namespace Tensorflow.Keras -{ - public partial class Activations - { - public Activation Sigmoid = (features, name) - => tf.Context.ExecuteOp("Sigmoid", name, new ExecuteOpArgs(features)); - } -} diff --git a/src/TensorFlowNET.Keras/Activations/Activations.Softmax.cs b/src/TensorFlowNET.Keras/Activations/Activations.Softmax.cs deleted file mode 100644 index 02d86ace..00000000 --- a/src/TensorFlowNET.Keras/Activations/Activations.Softmax.cs +++ /dev/null @@ -1,11 +0,0 @@ -using System; -using static Tensorflow.Binding; - -namespace Tensorflow.Keras -{ - public partial class Activations - { - public Activation Softmax = (features, name) - => tf.Context.ExecuteOp("Softmax", name, new ExecuteOpArgs(features)); - } -} diff --git a/src/TensorFlowNET.Keras/Activations/Activations.Tanh.cs b/src/TensorFlowNET.Keras/Activations/Activations.Tanh.cs deleted file mode 100644 index 33dc5ba6..00000000 --- a/src/TensorFlowNET.Keras/Activations/Activations.Tanh.cs +++ /dev/null @@ -1,11 +0,0 @@ -using System; -using static Tensorflow.Binding; - -namespace Tensorflow.Keras -{ - public partial class Activations - { - public Activation Tanh = (features, name) - => tf.Context.ExecuteOp("Tanh", name, new ExecuteOpArgs(features)); - } -} diff --git a/src/TensorFlowNET.Keras/Layers/Attention/MultiHeadAttention.cs b/src/TensorFlowNET.Keras/Layers/Attention/MultiHeadAttention.cs index 1b82e0a9..701724d5 100644 --- a/src/TensorFlowNET.Keras/Layers/Attention/MultiHeadAttention.cs +++ b/src/TensorFlowNET.Keras/Layers/Attention/MultiHeadAttention.cs @@ -1,4 +1,5 @@ using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Core; using Tensorflow.Keras.Engine; using Tensorflow.NumPy; using static Tensorflow.Binding; diff --git a/src/TensorFlowNET.Keras/Layers/Core/EinsumDense.cs b/src/TensorFlowNET.Keras/Layers/Core/EinsumDense.cs index 0f387570..af71ddf9 100644 --- a/src/TensorFlowNET.Keras/Layers/Core/EinsumDense.cs +++ b/src/TensorFlowNET.Keras/Layers/Core/EinsumDense.cs @@ -4,8 +4,8 @@ using System.Collections; using System.Collections.Generic; using System.Linq; using System.Text.RegularExpressions; -using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.Engine; +using Tensorflow.Keras.ArgsDefinition.Core; namespace Tensorflow.Keras.Layers { diff --git a/src/TensorFlowNET.Keras/Layers/Cropping/Cropping2D.cs b/src/TensorFlowNET.Keras/Layers/Cropping/Cropping2D.cs deleted file mode 100644 index 1f33ee3a..00000000 --- a/src/TensorFlowNET.Keras/Layers/Cropping/Cropping2D.cs +++ /dev/null @@ -1,114 +0,0 @@ -using Tensorflow.Keras.ArgsDefinition; -using Tensorflow.Keras.Engine; - -namespace Tensorflow.Keras.Layers { - /// - /// Crop the input along axis 1 and 2. - /// For example: - /// shape (1, 5, 5, 5) -- crop2D ((1, 2), (1, 3)) --> shape (1, 2, 1, 5) - /// - public class Cropping2D : Layer { - Cropping2DArgs args; - public Cropping2D ( Cropping2DArgs args ) : base(args) { - this.args = args; - } - public override void build(Shape input_shape) { - built = true; - _buildInputShape = input_shape; - } - protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) { - Tensor output = inputs; - if ( output.rank != 4 ) { - // throw an ValueError exception - throw new ValueError("Expected dim=4, found dim=" + output.rank); - } - if ( args.cropping.shape == new Shape(1) ) { - int crop = args.cropping[0]; - if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) { - output = output[new Slice(), - new Slice(crop, ( int ) output.shape[1] - crop), - new Slice(crop, ( int ) output.shape[2] - crop), - new Slice()]; - } - else { - output = output[new Slice(), - new Slice(), - new Slice(crop, ( int ) output.shape[2] - crop), - new Slice(crop, ( int ) output.shape[3] - crop)]; - } - } - // a tuple of 2 integers - else if ( args.cropping.shape == new Shape(2) ) { - int crop_1 = args.cropping[0]; - int crop_2 = args.cropping[1]; - if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) { - output = output[new Slice(), - new Slice(crop_1, ( int ) output.shape[1] - crop_1), - new Slice(crop_2, ( int ) output.shape[2] - crop_2), - new Slice()]; - } - else { - output = output[new Slice(), - new Slice(), - new Slice(crop_1, ( int ) output.shape[2] - crop_1), - new Slice(crop_2, ( int ) output.shape[3] - crop_2)]; - } - } - else if ( args.cropping.shape[0] == 2 && args.cropping.shape[1] == 2 ) { - int x_start = args.cropping[0, 0], x_end = args.cropping[0, 1]; - int y_start = args.cropping[1, 0], y_end = args.cropping[1, 1]; - if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) { - output = output[new Slice(), - new Slice(x_start, ( int ) output.shape[1] - x_end), - new Slice(y_start, ( int ) output.shape[2] - y_end), - new Slice()]; - } - else { - output = output[new Slice(), - new Slice(), - new Slice(x_start, ( int ) output.shape[2] - x_end), - new Slice(y_start, ( int ) output.shape[3] - y_end) - ]; - } - } - return output; - } - - public override Shape ComputeOutputShape ( Shape input_shape ) { - if ( args.cropping.shape == new Shape(1) ) { - int crop = args.cropping[0]; - if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop * 2, ( int ) input_shape[2] - crop * 2, ( int ) input_shape[3]); - } - else { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1], ( int ) input_shape[2] - crop * 2, ( int ) input_shape[3] - crop * 2); - } - } - // a tuple of 2 integers - else if ( args.cropping.shape == new Shape(2) ) { - int crop_1 = args.cropping[0], crop_2 = args.cropping[1]; - if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop_1 * 2, ( int ) input_shape[2] - crop_2 * 2, ( int ) input_shape[3]); - } - else { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1], ( int ) input_shape[2] - crop_1 * 2, ( int ) input_shape[3] - crop_2 * 2); - } - } - else if ( args.cropping.shape == new Shape(2, 2) ) { - int crop_1_start = args.cropping[0, 0], crop_1_end = args.cropping[0, 1]; - int crop_2_start = args.cropping[1, 0], crop_2_end = args.cropping[1, 1]; - if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop_1_start - crop_1_end, - ( int ) input_shape[2] - crop_2_start - crop_2_end, ( int ) input_shape[3]); - } - else { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1], - ( int ) input_shape[2] - crop_1_start - crop_1_end, ( int ) input_shape[3] - crop_2_start - crop_2_end); - } - } - else { - throw new ValueError(); - } - } - } -} diff --git a/src/TensorFlowNET.Keras/Layers/Cropping/Cropping3D.cs b/src/TensorFlowNET.Keras/Layers/Cropping/Cropping3D.cs deleted file mode 100644 index 838a5043..00000000 --- a/src/TensorFlowNET.Keras/Layers/Cropping/Cropping3D.cs +++ /dev/null @@ -1,124 +0,0 @@ -using Tensorflow.Keras.ArgsDefinition; -using Tensorflow.Keras.Engine; - -namespace Tensorflow.Keras.Layers { - /// - /// Similar to copping 2D - /// - public class Cropping3D : Layer { - Cropping3DArgs args; - public Cropping3D ( Cropping3DArgs args ) : base(args) { - this.args = args; - } - - public override void build(Shape input_shape) { - built = true; - _buildInputShape = input_shape; - } - - protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) { - Tensor output = inputs; - if ( output.rank != 5 ) { - // throw an ValueError exception - throw new ValueError("Expected dim=5, found dim=" + output.rank); - } - - if ( args.cropping.shape == new Shape(1) ) { - int crop = args.cropping[0]; - if ( args.data_format == Cropping3DArgs.DataFormat.channels_last ) { - output = output[new Slice(), - new Slice(crop, ( int ) output.shape[1] - crop), - new Slice(crop, ( int ) output.shape[2] - crop), - new Slice(crop, ( int ) output.shape[3] - crop), - new Slice()]; - } - else { - output = output[new Slice(), - new Slice(), - new Slice(crop, ( int ) output.shape[2] - crop), - new Slice(crop, ( int ) output.shape[3] - crop), - new Slice(crop, ( int ) output.shape[4] - crop)]; - } - - } - // int[1][3] equivalent to a tuple of 3 integers - else if ( args.cropping.shape == new Shape(3) ) { - var crop_1 = args.cropping[0]; - var crop_2 = args.cropping[1]; - var crop_3 = args.cropping[2]; - if ( args.data_format == Cropping3DArgs.DataFormat.channels_last ) { - output = output[new Slice(), - new Slice(crop_1, ( int ) output.shape[1] - crop_1), - new Slice(crop_2, ( int ) output.shape[2] - crop_2), - new Slice(crop_3, ( int ) output.shape[3] - crop_3), - new Slice()]; - } - else { - output = output[new Slice(), - new Slice(), - new Slice(crop_1, ( int ) output.shape[2] - crop_1), - new Slice(crop_2, ( int ) output.shape[3] - crop_2), - new Slice(crop_3, ( int ) output.shape[4] - crop_3)]; - } - } - else if ( args.cropping.shape[0] == 3 && args.cropping.shape[1] == 2 ) { - int x = args.cropping[0, 0], x_end = args.cropping[0, 1]; - int y = args.cropping[1, 0], y_end = args.cropping[1, 1]; - int z = args.cropping[2, 0], z_end = args.cropping[2, 1]; - if ( args.data_format == Cropping3DArgs.DataFormat.channels_last ) { - output = output[new Slice(), - new Slice(x, ( int ) output.shape[1] - x_end), - new Slice(y, ( int ) output.shape[2] - y_end), - new Slice(z, ( int ) output.shape[3] - z_end), - new Slice()]; - } - else { - output = output[new Slice(), - new Slice(), - new Slice(x, ( int ) output.shape[2] - x_end), - new Slice(y, ( int ) output.shape[3] - y_end), - new Slice(z, ( int ) output.shape[4] - z_end) - ]; - } - } - return output; - } - public override Shape ComputeOutputShape ( Shape input_shape ) { - if ( args.cropping.shape == new Shape(1) ) { - int crop = args.cropping[0]; - if ( args.data_format == Cropping3DArgs.DataFormat.channels_last ) { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop * 2, ( int ) input_shape[2] - crop * 2, ( int ) input_shape[3] - crop * 2, ( int ) input_shape[4]); - } - else { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1], ( int ) input_shape[2] - crop * 2, ( int ) input_shape[3] - crop * 2, ( int ) input_shape[4] - crop * 2); - } - } - // int[1][3] equivalent to a tuple of 3 integers - else if ( args.cropping.shape == new Shape(3) ) { - var crop_start_1 = args.cropping[0]; - var crop_start_2 = args.cropping[1]; - var crop_start_3 = args.cropping[2]; - if ( args.data_format == Cropping3DArgs.DataFormat.channels_last ) { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop_start_1 * 2, ( int ) input_shape[2] - crop_start_2 * 2, ( int ) input_shape[3] - crop_start_3 * 2, ( int ) input_shape[4]); - } - else { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1], ( int ) input_shape[2] - crop_start_1 * 2, ( int ) input_shape[3] - crop_start_2 * 2, ( int ) input_shape[4] - crop_start_3 * 2); - } - } - else if ( args.cropping.shape == new Shape(3, 2) ) { - int x = args.cropping[0, 0], x_end = args.cropping[0, 1]; - int y = args.cropping[1, 0], y_end = args.cropping[1, 1]; - int z = args.cropping[2, 0], z_end = args.cropping[2, 1]; - if ( args.data_format == Cropping3DArgs.DataFormat.channels_last ) { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - x - x_end, ( int ) input_shape[2] - y - y_end, ( int ) input_shape[3] - z - z_end, ( int ) input_shape[4]); - } - else { - return new Shape(( int ) input_shape[0], ( int ) input_shape[1], ( int ) input_shape[2] - x - x_end, ( int ) input_shape[3] - y - y_end, ( int ) input_shape[4] - z - z_end); - } - } - else { - throw new ValueError(); - } - } - } -} diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs index 339ddb85..3e3442f2 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs @@ -2,16 +2,18 @@ using System; using System.Collections.Generic; using System.Text; -using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.Layers.Reshaping; +using Tensorflow.Keras.ArgsDefinition.Reshaping; -namespace Tensorflow.Keras.Layers { - public partial class LayersApi { +namespace Tensorflow.Keras.Layers +{ + public partial class LayersApi { /// /// Cropping layer for 1D input /// /// cropping size public ILayer Cropping1D ( NDArray cropping ) - => new Cropping1D(new CroppingArgs { + => new Cropping1D(new Cropping1DArgs { cropping = cropping }); diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.cs index 769beea0..76634918 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.cs @@ -1,9 +1,8 @@ using System; using Tensorflow.Keras.ArgsDefinition; -using Tensorflow.Keras.ArgsDefinition.Lstm; +using Tensorflow.Keras.ArgsDefinition.Core; using Tensorflow.Keras.ArgsDefinition.Rnn; using Tensorflow.Keras.Engine; -using Tensorflow.Keras.Layers.Lstm; using Tensorflow.Keras.Layers.Rnn; using static Tensorflow.Binding; using static Tensorflow.KerasApi; @@ -108,7 +107,7 @@ namespace Tensorflow.Keras.Layers DilationRate = dilation_rate, Groups = groups, UseBias = use_bias, - Activation = GetActivationByName(activation), + Activation = Activations.GetActivationByName(activation), KernelInitializer = GetInitializerByName(kernel_initializer), BiasInitializer = GetInitializerByName(bias_initializer) }); @@ -163,7 +162,7 @@ namespace Tensorflow.Keras.Layers BiasInitializer = bias_initializer == null ? tf.zeros_initializer : bias_initializer, BiasRegularizer = bias_regularizer, ActivityRegularizer = activity_regularizer, - Activation = activation ?? keras.activations.Linear + Activation = activation ?? keras.activations.Linear, }); /// @@ -210,7 +209,8 @@ namespace Tensorflow.Keras.Layers UseBias = use_bias, KernelInitializer = GetInitializerByName(kernel_initializer), BiasInitializer = GetInitializerByName(bias_initializer), - Activation = GetActivationByName(activation) + Activation = Activations.GetActivationByName(activation), + ActivationName = activation }); /// @@ -255,7 +255,7 @@ namespace Tensorflow.Keras.Layers UseBias = use_bias, KernelInitializer = GetInitializerByName(kernel_initializer), BiasInitializer = GetInitializerByName(bias_initializer), - Activation = GetActivationByName(activation) + Activation = Activations.GetActivationByName(activation) }); /// @@ -300,7 +300,7 @@ namespace Tensorflow.Keras.Layers => new Dense(new DenseArgs { Units = units, - Activation = GetActivationByName("linear"), + Activation = Activations.GetActivationByName("linear"), ActivationName = "linear" }); @@ -321,7 +321,7 @@ namespace Tensorflow.Keras.Layers => new Dense(new DenseArgs { Units = units, - Activation = GetActivationByName(activation), + Activation = Activations.GetActivationByName(activation), ActivationName = activation, InputShape = input_shape }); @@ -666,7 +666,7 @@ namespace Tensorflow.Keras.Layers => new SimpleRNN(new SimpleRNNArgs { Units = units, - Activation = GetActivationByName(activation), + Activation = Activations.GetActivationByName(activation), KernelInitializer = GetInitializerByName(kernel_initializer), RecurrentInitializer = GetInitializerByName(recurrent_initializer), BiasInitializer = GetInitializerByName(bias_initializer), @@ -814,24 +814,7 @@ namespace Tensorflow.Keras.Layers public ILayer GlobalMaxPooling2D(string data_format = "channels_last") => new GlobalMaxPooling2D(new Pooling2DArgs { DataFormat = data_format }); - - /// - /// Get an activation function layer from its name. - /// - /// The name of the activation function. One of linear, relu, sigmoid, and tanh. - /// - - Activation GetActivationByName(string name) - => name switch - { - "linear" => keras.activations.Linear, - "relu" => keras.activations.Relu, - "sigmoid" => keras.activations.Sigmoid, - "tanh" => keras.activations.Tanh, - "softmax" => keras.activations.Softmax, - _ => throw new Exception($"Activation {name} not found") - }; - + Activation GetActivationByName(string name) => Activations.GetActivationByName(name); /// /// Get an weights initializer from its name. /// diff --git a/src/TensorFlowNET.Keras/Layers/Normalization/BatchNormalization.cs b/src/TensorFlowNET.Keras/Layers/Normalization/BatchNormalization.cs index c0b16c81..3b8e1ee8 100644 --- a/src/TensorFlowNET.Keras/Layers/Normalization/BatchNormalization.cs +++ b/src/TensorFlowNET.Keras/Layers/Normalization/BatchNormalization.cs @@ -58,7 +58,7 @@ namespace Tensorflow.Keras.Layers var ndims = input_shape.ndim; foreach (var (idx, x) in enumerate(axis)) if (x < 0) - axis[idx] = ndims + x; + args.Axis.dims[idx] = axis[idx] = ndims + x; fused = ndims == 4; diff --git a/src/TensorFlowNET.Keras/Layers/Rescaling/Rescaling.cs b/src/TensorFlowNET.Keras/Layers/Preprocessing/Rescaling.cs similarity index 100% rename from src/TensorFlowNET.Keras/Layers/Rescaling/Rescaling.cs rename to src/TensorFlowNET.Keras/Layers/Preprocessing/Rescaling.cs diff --git a/src/TensorFlowNET.Keras/Layers/Cropping/Cropping1D.cs b/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping1D.cs similarity index 79% rename from src/TensorFlowNET.Keras/Layers/Cropping/Cropping1D.cs rename to src/TensorFlowNET.Keras/Layers/Reshaping/Cropping1D.cs index 44b338c2..10c15b69 100644 --- a/src/TensorFlowNET.Keras/Layers/Cropping/Cropping1D.cs +++ b/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping1D.cs @@ -1,11 +1,12 @@ -using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Reshaping; using Tensorflow.Keras.Engine; -namespace Tensorflow.Keras.Layers { +namespace Tensorflow.Keras.Layers.Reshaping +{ public class Cropping1D : Layer { - CroppingArgs args; - public Cropping1D(CroppingArgs args) : base(args) + Cropping1DArgs args; + public Cropping1D(Cropping1DArgs args) : base(args) { this.args = args; } @@ -41,7 +42,7 @@ namespace Tensorflow.Keras.Layers { else { int crop_start = args.cropping[0], crop_end = args.cropping[1]; - output = output[new Slice(), new Slice(crop_start, (int)(output.shape[1]) - crop_end), new Slice()]; + output = output[new Slice(), new Slice(crop_start, (int)output.shape[1] - crop_end), new Slice()]; } return output; } @@ -51,12 +52,12 @@ namespace Tensorflow.Keras.Layers { if (args.cropping.shape[0] == 1) { int crop = args.cropping[0]; - return new Shape((int)(input_shape[0]), (int)(input_shape[1] - crop * 2), (int)(input_shape[2])); + return new Shape((int)input_shape[0], (int)(input_shape[1] - crop * 2), (int)input_shape[2]); } else { int crop_start = args.cropping[0], crop_end = args.cropping[1]; - return new Shape((int)(input_shape[0]), (int)(input_shape[1] - crop_start - crop_end), (int)(input_shape[2])); + return new Shape((int)input_shape[0], (int)(input_shape[1] - crop_start - crop_end), (int)input_shape[2]); } } } diff --git a/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping2D.cs b/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping2D.cs new file mode 100644 index 00000000..a8d7043e --- /dev/null +++ b/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping2D.cs @@ -0,0 +1,140 @@ +using Tensorflow.Keras.ArgsDefinition.Reshaping; +using Tensorflow.Keras.Engine; + +namespace Tensorflow.Keras.Layers.Reshaping +{ + /// + /// Crop the input along axis 1 and 2. + /// For example: + /// shape (1, 5, 5, 5) -- crop2D ((1, 2), (1, 3)) --> shape (1, 2, 1, 5) + /// + public class Cropping2D : Layer + { + Cropping2DArgs args; + public Cropping2D(Cropping2DArgs args) : base(args) + { + this.args = args; + } + public override void build(Shape input_shape) + { + built = true; + _buildInputShape = input_shape; + } + protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) + { + Tensor output = inputs; + if (output.rank != 4) + { + // throw an ValueError exception + throw new ValueError("Expected dim=4, found dim=" + output.rank); + } + if (args.cropping.shape == new Shape(1)) + { + int crop = args.cropping[0]; + if (args.data_format == Cropping2DArgs.DataFormat.channels_last) + { + output = output[new Slice(), + new Slice(crop, (int)output.shape[1] - crop), + new Slice(crop, (int)output.shape[2] - crop), + new Slice()]; + } + else + { + output = output[new Slice(), + new Slice(), + new Slice(crop, (int)output.shape[2] - crop), + new Slice(crop, (int)output.shape[3] - crop)]; + } + } + // a tuple of 2 integers + else if (args.cropping.shape == new Shape(2)) + { + int crop_1 = args.cropping[0]; + int crop_2 = args.cropping[1]; + if (args.data_format == Cropping2DArgs.DataFormat.channels_last) + { + output = output[new Slice(), + new Slice(crop_1, (int)output.shape[1] - crop_1), + new Slice(crop_2, (int)output.shape[2] - crop_2), + new Slice()]; + } + else + { + output = output[new Slice(), + new Slice(), + new Slice(crop_1, (int)output.shape[2] - crop_1), + new Slice(crop_2, (int)output.shape[3] - crop_2)]; + } + } + else if (args.cropping.shape[0] == 2 && args.cropping.shape[1] == 2) + { + int x_start = args.cropping[0, 0], x_end = args.cropping[0, 1]; + int y_start = args.cropping[1, 0], y_end = args.cropping[1, 1]; + if (args.data_format == Cropping2DArgs.DataFormat.channels_last) + { + output = output[new Slice(), + new Slice(x_start, (int)output.shape[1] - x_end), + new Slice(y_start, (int)output.shape[2] - y_end), + new Slice()]; + } + else + { + output = output[new Slice(), + new Slice(), + new Slice(x_start, (int)output.shape[2] - x_end), + new Slice(y_start, (int)output.shape[3] - y_end) + ]; + } + } + return output; + } + + public override Shape ComputeOutputShape(Shape input_shape) + { + if (args.cropping.shape == new Shape(1)) + { + int crop = args.cropping[0]; + if (args.data_format == Cropping2DArgs.DataFormat.channels_last) + { + return new Shape((int)input_shape[0], (int)input_shape[1] - crop * 2, (int)input_shape[2] - crop * 2, (int)input_shape[3]); + } + else + { + return new Shape((int)input_shape[0], (int)input_shape[1], (int)input_shape[2] - crop * 2, (int)input_shape[3] - crop * 2); + } + } + // a tuple of 2 integers + else if (args.cropping.shape == new Shape(2)) + { + int crop_1 = args.cropping[0], crop_2 = args.cropping[1]; + if (args.data_format == Cropping2DArgs.DataFormat.channels_last) + { + return new Shape((int)input_shape[0], (int)input_shape[1] - crop_1 * 2, (int)input_shape[2] - crop_2 * 2, (int)input_shape[3]); + } + else + { + return new Shape((int)input_shape[0], (int)input_shape[1], (int)input_shape[2] - crop_1 * 2, (int)input_shape[3] - crop_2 * 2); + } + } + else if (args.cropping.shape == new Shape(2, 2)) + { + int crop_1_start = args.cropping[0, 0], crop_1_end = args.cropping[0, 1]; + int crop_2_start = args.cropping[1, 0], crop_2_end = args.cropping[1, 1]; + if (args.data_format == Cropping2DArgs.DataFormat.channels_last) + { + return new Shape((int)input_shape[0], (int)input_shape[1] - crop_1_start - crop_1_end, + (int)input_shape[2] - crop_2_start - crop_2_end, (int)input_shape[3]); + } + else + { + return new Shape((int)input_shape[0], (int)input_shape[1], + (int)input_shape[2] - crop_1_start - crop_1_end, (int)input_shape[3] - crop_2_start - crop_2_end); + } + } + else + { + throw new ValueError(); + } + } + } +} diff --git a/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping3D.cs b/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping3D.cs new file mode 100644 index 00000000..796c2dd3 --- /dev/null +++ b/src/TensorFlowNET.Keras/Layers/Reshaping/Cropping3D.cs @@ -0,0 +1,150 @@ +using Tensorflow.Keras.ArgsDefinition.Reshaping; +using Tensorflow.Keras.Engine; + +namespace Tensorflow.Keras.Layers.Reshaping +{ + /// + /// Similar to copping 2D + /// + public class Cropping3D : Layer + { + Cropping3DArgs args; + public Cropping3D(Cropping3DArgs args) : base(args) + { + this.args = args; + } + + public override void build(Shape input_shape) + { + built = true; + _buildInputShape = input_shape; + } + + protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null) + { + Tensor output = inputs; + if (output.rank != 5) + { + // throw an ValueError exception + throw new ValueError("Expected dim=5, found dim=" + output.rank); + } + + if (args.cropping.shape == new Shape(1)) + { + int crop = args.cropping[0]; + if (args.data_format == Cropping3DArgs.DataFormat.channels_last) + { + output = output[new Slice(), + new Slice(crop, (int)output.shape[1] - crop), + new Slice(crop, (int)output.shape[2] - crop), + new Slice(crop, (int)output.shape[3] - crop), + new Slice()]; + } + else + { + output = output[new Slice(), + new Slice(), + new Slice(crop, (int)output.shape[2] - crop), + new Slice(crop, (int)output.shape[3] - crop), + new Slice(crop, (int)output.shape[4] - crop)]; + } + + } + // int[1][3] equivalent to a tuple of 3 integers + else if (args.cropping.shape == new Shape(3)) + { + var crop_1 = args.cropping[0]; + var crop_2 = args.cropping[1]; + var crop_3 = args.cropping[2]; + if (args.data_format == Cropping3DArgs.DataFormat.channels_last) + { + output = output[new Slice(), + new Slice(crop_1, (int)output.shape[1] - crop_1), + new Slice(crop_2, (int)output.shape[2] - crop_2), + new Slice(crop_3, (int)output.shape[3] - crop_3), + new Slice()]; + } + else + { + output = output[new Slice(), + new Slice(), + new Slice(crop_1, (int)output.shape[2] - crop_1), + new Slice(crop_2, (int)output.shape[3] - crop_2), + new Slice(crop_3, (int)output.shape[4] - crop_3)]; + } + } + else if (args.cropping.shape[0] == 3 && args.cropping.shape[1] == 2) + { + int x = args.cropping[0, 0], x_end = args.cropping[0, 1]; + int y = args.cropping[1, 0], y_end = args.cropping[1, 1]; + int z = args.cropping[2, 0], z_end = args.cropping[2, 1]; + if (args.data_format == Cropping3DArgs.DataFormat.channels_last) + { + output = output[new Slice(), + new Slice(x, (int)output.shape[1] - x_end), + new Slice(y, (int)output.shape[2] - y_end), + new Slice(z, (int)output.shape[3] - z_end), + new Slice()]; + } + else + { + output = output[new Slice(), + new Slice(), + new Slice(x, (int)output.shape[2] - x_end), + new Slice(y, (int)output.shape[3] - y_end), + new Slice(z, (int)output.shape[4] - z_end) + ]; + } + } + return output; + } + public override Shape ComputeOutputShape(Shape input_shape) + { + if (args.cropping.shape == new Shape(1)) + { + int crop = args.cropping[0]; + if (args.data_format == Cropping3DArgs.DataFormat.channels_last) + { + return new Shape((int)input_shape[0], (int)input_shape[1] - crop * 2, (int)input_shape[2] - crop * 2, (int)input_shape[3] - crop * 2, (int)input_shape[4]); + } + else + { + return new Shape((int)input_shape[0], (int)input_shape[1], (int)input_shape[2] - crop * 2, (int)input_shape[3] - crop * 2, (int)input_shape[4] - crop * 2); + } + } + // int[1][3] equivalent to a tuple of 3 integers + else if (args.cropping.shape == new Shape(3)) + { + var crop_start_1 = args.cropping[0]; + var crop_start_2 = args.cropping[1]; + var crop_start_3 = args.cropping[2]; + if (args.data_format == Cropping3DArgs.DataFormat.channels_last) + { + return new Shape((int)input_shape[0], (int)input_shape[1] - crop_start_1 * 2, (int)input_shape[2] - crop_start_2 * 2, (int)input_shape[3] - crop_start_3 * 2, (int)input_shape[4]); + } + else + { + return new Shape((int)input_shape[0], (int)input_shape[1], (int)input_shape[2] - crop_start_1 * 2, (int)input_shape[3] - crop_start_2 * 2, (int)input_shape[4] - crop_start_3 * 2); + } + } + else if (args.cropping.shape == new Shape(3, 2)) + { + int x = args.cropping[0, 0], x_end = args.cropping[0, 1]; + int y = args.cropping[1, 0], y_end = args.cropping[1, 1]; + int z = args.cropping[2, 0], z_end = args.cropping[2, 1]; + if (args.data_format == Cropping3DArgs.DataFormat.channels_last) + { + return new Shape((int)input_shape[0], (int)input_shape[1] - x - x_end, (int)input_shape[2] - y - y_end, (int)input_shape[3] - z - z_end, (int)input_shape[4]); + } + else + { + return new Shape((int)input_shape[0], (int)input_shape[1], (int)input_shape[2] - x - x_end, (int)input_shape[3] - y - y_end, (int)input_shape[4] - z - z_end); + } + } + else + { + throw new ValueError(); + } + } + } +} diff --git a/src/TensorFlowNET.Keras/Layers/Lstm/LSTM.cs b/src/TensorFlowNET.Keras/Layers/Rnn/LSTM.cs similarity index 87% rename from src/TensorFlowNET.Keras/Layers/Lstm/LSTM.cs rename to src/TensorFlowNET.Keras/Layers/Rnn/LSTM.cs index b7d97384..59555e62 100644 --- a/src/TensorFlowNET.Keras/Layers/Lstm/LSTM.cs +++ b/src/TensorFlowNET.Keras/Layers/Rnn/LSTM.cs @@ -1,9 +1,8 @@ using System.Linq; -using Tensorflow.Keras.ArgsDefinition.Lstm; +using Tensorflow.Keras.ArgsDefinition.Rnn; using Tensorflow.Keras.Engine; -using Tensorflow.Keras.Layers.Rnn; -namespace Tensorflow.Keras.Layers.Lstm +namespace Tensorflow.Keras.Layers.Rnn { /// /// Long Short-Term Memory layer - Hochreiter 1997. diff --git a/src/TensorFlowNET.Keras/Layers/Lstm/LSTMCell.cs b/src/TensorFlowNET.Keras/Layers/Rnn/LSTMCell.cs similarity index 72% rename from src/TensorFlowNET.Keras/Layers/Lstm/LSTMCell.cs rename to src/TensorFlowNET.Keras/Layers/Rnn/LSTMCell.cs index 3cd35a09..a622c91a 100644 --- a/src/TensorFlowNET.Keras/Layers/Lstm/LSTMCell.cs +++ b/src/TensorFlowNET.Keras/Layers/Rnn/LSTMCell.cs @@ -1,7 +1,7 @@ -using Tensorflow.Keras.ArgsDefinition.Lstm; +using Tensorflow.Keras.ArgsDefinition.Rnn; using Tensorflow.Keras.Engine; -namespace Tensorflow.Keras.Layers.Lstm +namespace Tensorflow.Keras.Layers.Rnn { public class LSTMCell : Layer { diff --git a/src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs b/src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs index 877c3599..6b755ece 100644 --- a/src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs +++ b/src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs @@ -3,7 +3,6 @@ using System.Collections.Generic; using Tensorflow.Keras.ArgsDefinition; using Tensorflow.Keras.ArgsDefinition.Rnn; using Tensorflow.Keras.Engine; -using Tensorflow.Keras.Layers.Lstm; // from tensorflow.python.distribute import distribution_strategy_context as ds_context; namespace Tensorflow.Keras.Layers.Rnn diff --git a/test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelTest.cs b/test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelTest.cs deleted file mode 100644 index 288a92b3..00000000 --- a/test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelTest.cs +++ /dev/null @@ -1,82 +0,0 @@ -using Microsoft.VisualStudio.TestTools.UnitTesting; -using Tensorflow.NumPy; -using System; -using System.Collections.Generic; -using System.Linq; -using System.Text; -using System.Threading.Tasks; -using Tensorflow; -using static Tensorflow.Binding; -using static Tensorflow.KerasApi; -using Tensorflow.Keras; -using Tensorflow.Keras.ArgsDefinition; -using Tensorflow.Keras.Engine; -using Tensorflow.Keras.Layers; -using Tensorflow.Keras.Losses; -using Tensorflow.Keras.Metrics; -using Tensorflow.Keras.Optimizers; -using Tensorflow.Operations; - -namespace TensorFlowNET.Keras.UnitTest.SaveModel; - -[TestClass] -public class SequentialModelTest -{ - [TestMethod] - public void SimpleModelFromAutoCompile() - { - var inputs = new KerasInterface().Input((28, 28, 1)); - var x = new Flatten(new FlattenArgs()).Apply(inputs); - x = new Dense(new DenseArgs() { Units = 100, Activation = tf.nn.relu }).Apply(x); - x = new LayersApi().Dense(units: 10).Apply(x); - var outputs = new LayersApi().Softmax(axis: 1).Apply(x); - var model = new KerasInterface().Model(inputs, outputs); - - model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(), new string[] { "accuracy" }); - - var data_loader = new MnistModelLoader(); - var num_epochs = 1; - var batch_size = 50; - - var dataset = data_loader.LoadAsync(new ModelLoadSetting - { - TrainDir = "mnist", - OneHot = false, - ValidationSize = 10000, - }).Result; - - model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs); - - model.save("C:\\Work\\tf.net\\tf_test\\tf.net.simple.compile", save_format: "tf"); - } - - [TestMethod] - public void SimpleModelFromSequential() - { - Model model = KerasApi.keras.Sequential(new List() - { - keras.layers.InputLayer((28, 28, 1)), - keras.layers.Flatten(), - keras.layers.Dense(100, "relu"), - keras.layers.Dense(10), - keras.layers.Softmax(1) - }); - - model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(), new string[] { "accuracy" }); - - var data_loader = new MnistModelLoader(); - var num_epochs = 1; - var batch_size = 50; - - var dataset = data_loader.LoadAsync(new ModelLoadSetting - { - TrainDir = "mnist", - OneHot = false, - ValidationSize = 10000, - }).Result; - - model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs); - - model.save("C:\\Work\\tf.net\\tf_test\\tf.net.simple.sequential", save_format: "tf"); - } -} \ No newline at end of file