diff --git a/src/SciSharp.TensorFlow.Redist/README.md b/src/SciSharp.TensorFlow.Redist/README.md index 141bba35..4002aa21 100644 --- a/src/SciSharp.TensorFlow.Redist/README.md +++ b/src/SciSharp.TensorFlow.Redist/README.md @@ -26,7 +26,7 @@ Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5 #### Download pre-build package -[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.4.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.4.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.4.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.4.0.tar.gz), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.4.0.zip) +[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.10.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.10.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.10.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.10.0.zip), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.10.0.zip) @@ -35,6 +35,6 @@ Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5 On Windows, the tar command does not support extracting archives with symlinks. So when `dotnet pack` runs on Windows it will only package the Windows binaries. 1. Run `dotnet pack SciSharp.TensorFlow.Redist.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux. -2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.4.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600` +2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.10.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600` diff --git a/src/TensorFlowNET.Console/Program.cs b/src/TensorFlowNET.Console/Program.cs index 4b7f52de..638fe0a3 100644 --- a/src/TensorFlowNET.Console/Program.cs +++ b/src/TensorFlowNET.Console/Program.cs @@ -10,6 +10,9 @@ namespace Tensorflow var diag = new Diagnostician(); // diag.Diagnose(@"D:\memory.txt"); + var rnn = new SimpleRnnTest(); + rnn.Run(); + // this class is used explor new features. var exploring = new Exploring(); // exploring.Run(); diff --git a/src/TensorFlowNET.Console/SimpleRnnTest.cs b/src/TensorFlowNET.Console/SimpleRnnTest.cs new file mode 100644 index 00000000..b61cee9c --- /dev/null +++ b/src/TensorFlowNET.Console/SimpleRnnTest.cs @@ -0,0 +1,31 @@ +using System; +using System.Collections.Generic; +using System.Text; +using Tensorflow.Keras; +using Tensorflow.NumPy; +using static Tensorflow.Binding; +using static Tensorflow.KerasApi; + +namespace Tensorflow +{ + public class SimpleRnnTest + { + public void Run() + { + tf.keras = new KerasInterface(); + var inputs = np.random.random((32, 10, 8)).astype(np.float32); + var simple_rnn = tf.keras.layers.SimpleRNN(4); + var output = simple_rnn.Apply(inputs); // The output has shape `[32, 4]`. + if (output.shape == (32, 4)) + { + + } + /*simple_rnn = tf.keras.layers.SimpleRNN( + 4, return_sequences = True, return_state = True) + + # whole_sequence_output has shape `[32, 10, 4]`. + # final_state has shape `[32, 4]`. + whole_sequence_output, final_state = simple_rnn(inputs)*/ + } + } +} diff --git a/src/TensorFlowNET.Console/Tensorflow.Console.csproj b/src/TensorFlowNET.Console/Tensorflow.Console.csproj index 058722eb..e66c7033 100644 --- a/src/TensorFlowNET.Console/Tensorflow.Console.csproj +++ b/src/TensorFlowNET.Console/Tensorflow.Console.csproj @@ -6,7 +6,7 @@ Tensorflow Tensorflow AnyCPU;x64 - 9.0 + 11.0 @@ -20,7 +20,7 @@ - + diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMArgs.cs deleted file mode 100644 index 0a2555a6..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMArgs.cs +++ /dev/null @@ -1,22 +0,0 @@ -namespace Tensorflow.Keras.ArgsDefinition -{ - public class LSTMArgs : RNNArgs - { - public int Units { get; set; } - public Activation Activation { get; set; } - public Activation RecurrentActivation { get; set; } - public IInitializer KernelInitializer { get; set; } - public IInitializer RecurrentInitializer { get; set; } - public IInitializer BiasInitializer { get; set; } - public bool UnitForgetBias { get; set; } - public float Dropout { get; set; } - public float RecurrentDropout { get; set; } - public int Implementation { get; set; } - public bool ReturnSequences { get; set; } - public bool ReturnState { get; set; } - public bool GoBackwards { get; set; } - public bool Stateful { get; set; } - public bool TimeMajor { get; set; } - public bool Unroll { get; set; } - } -} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMArgs.cs new file mode 100644 index 00000000..b08d21d8 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMArgs.cs @@ -0,0 +1,12 @@ +using Tensorflow.Keras.ArgsDefinition.Rnn; + +namespace Tensorflow.Keras.ArgsDefinition.Lstm +{ + public class LSTMArgs : RNNArgs + { + public bool UnitForgetBias { get; set; } + public float Dropout { get; set; } + public float RecurrentDropout { get; set; } + public int Implementation { get; set; } + } +} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMCellArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMCellArgs.cs similarity index 53% rename from src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMCellArgs.cs rename to src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMCellArgs.cs index 62f9a0c4..fb0868dc 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMCellArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMCellArgs.cs @@ -1,4 +1,4 @@ -namespace Tensorflow.Keras.ArgsDefinition +namespace Tensorflow.Keras.ArgsDefinition.Lstm { public class LSTMCellArgs : LayerArgs { diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/RNNArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/RNNArgs.cs deleted file mode 100644 index 3ebcf617..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/RNNArgs.cs +++ /dev/null @@ -1,21 +0,0 @@ -using System.Collections.Generic; - -namespace Tensorflow.Keras.ArgsDefinition -{ - public class RNNArgs : LayerArgs - { - public interface IRnnArgCell : ILayer - { - object state_size { get; } - } - - public IRnnArgCell Cell { get; set; } = null; - public bool ReturnSequences { get; set; } = false; - public bool ReturnState { get; set; } = false; - public bool GoBackwards { get; set; } = false; - public bool Stateful { get; set; } = false; - public bool Unroll { get; set; } = false; - public bool TimeMajor { get; set; } = false; - public Dictionary Kwargs { get; set; } = null; - } -} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs new file mode 100644 index 00000000..da527925 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs @@ -0,0 +1,45 @@ +using System.Collections.Generic; + +namespace Tensorflow.Keras.ArgsDefinition.Rnn +{ + public class RNNArgs : LayerArgs + { + public interface IRnnArgCell : ILayer + { + object state_size { get; } + } + + public IRnnArgCell Cell { get; set; } = null; + public bool ReturnSequences { get; set; } = false; + public bool ReturnState { get; set; } = false; + public bool GoBackwards { get; set; } = false; + public bool Stateful { get; set; } = false; + public bool Unroll { get; set; } = false; + public bool TimeMajor { get; set; } = false; + public Dictionary Kwargs { get; set; } = null; + + public int Units { get; set; } + public Activation Activation { get; set; } + public Activation RecurrentActivation { get; set; } + public bool UseBias { get; set; } = true; + public IInitializer KernelInitializer { get; set; } + public IInitializer RecurrentInitializer { get; set; } + public IInitializer BiasInitializer { get; set; } + + // kernel_regularizer=None, + // recurrent_regularizer=None, + // bias_regularizer=None, + // activity_regularizer=None, + // kernel_constraint=None, + // recurrent_constraint=None, + // bias_constraint=None, + // dropout=0., + // recurrent_dropout=0., + // return_sequences=False, + // return_state=False, + // go_backwards=False, + // stateful=False, + // unroll=False, + // **kwargs): + } +} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNArgs.cs new file mode 100644 index 00000000..fcfd694d --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNArgs.cs @@ -0,0 +1,7 @@ +namespace Tensorflow.Keras.ArgsDefinition.Rnn +{ + public class SimpleRNNArgs : RNNArgs + { + + } +} diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/StackedRNNCellsArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/StackedRNNCellsArgs.cs similarity index 82% rename from src/TensorFlowNET.Core/Keras/ArgsDefinition/StackedRNNCellsArgs.cs rename to src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/StackedRNNCellsArgs.cs index 9b910e17..fdfadab8 100644 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/StackedRNNCellsArgs.cs +++ b/src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/StackedRNNCellsArgs.cs @@ -1,6 +1,6 @@ using System.Collections.Generic; -namespace Tensorflow.Keras.ArgsDefinition +namespace Tensorflow.Keras.ArgsDefinition.Rnn { public class StackedRNNCellsArgs : LayerArgs { diff --git a/src/TensorFlowNET.Core/Keras/ArgsDefinition/SimpleRNNArgs.cs b/src/TensorFlowNET.Core/Keras/ArgsDefinition/SimpleRNNArgs.cs deleted file mode 100644 index 65815587..00000000 --- a/src/TensorFlowNET.Core/Keras/ArgsDefinition/SimpleRNNArgs.cs +++ /dev/null @@ -1,30 +0,0 @@ -namespace Tensorflow.Keras.ArgsDefinition -{ - public class SimpleRNNArgs : RNNArgs - { - public int Units { get; set; } - public Activation Activation { get; set; } - - // units, - // activation='tanh', - // use_bias=True, - // kernel_initializer='glorot_uniform', - // recurrent_initializer='orthogonal', - // bias_initializer='zeros', - // kernel_regularizer=None, - // recurrent_regularizer=None, - // bias_regularizer=None, - // activity_regularizer=None, - // kernel_constraint=None, - // recurrent_constraint=None, - // bias_constraint=None, - // dropout=0., - // recurrent_dropout=0., - // return_sequences=False, - // return_state=False, - // go_backwards=False, - // stateful=False, - // unroll=False, - // **kwargs): - } -} diff --git a/src/TensorFlowNET.Core/Keras/IKerasApi.cs b/src/TensorFlowNET.Core/Keras/IKerasApi.cs new file mode 100644 index 00000000..660dcbde --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/IKerasApi.cs @@ -0,0 +1,12 @@ +using System; +using System.Collections.Generic; +using System.Text; +using Tensorflow.Keras.Layers; + +namespace Tensorflow.Keras +{ + public interface IKerasApi + { + public ILayersApi layers { get; } + } +} diff --git a/src/TensorFlowNET.Core/Keras/IPreprocessing.cs b/src/TensorFlowNET.Core/Keras/IPreprocessing.cs new file mode 100644 index 00000000..28eea0f5 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/IPreprocessing.cs @@ -0,0 +1,16 @@ +using System; +using System.Collections.Generic; +using System.Text; + +namespace Tensorflow.Keras +{ + public interface IPreprocessing + { + public ILayer Resizing(int height, int width, string interpolation = "bilinear"); + public ILayer TextVectorization(Func standardize = null, + string split = "whitespace", + int max_tokens = -1, + string output_mode = "int", + int output_sequence_length = -1); + } +} diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Activation.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Activation.cs new file mode 100644 index 00000000..73a6787c --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Activation.cs @@ -0,0 +1,20 @@ +using System; +using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.NumPy; +using Tensorflow.Operations.Activation; + +namespace Tensorflow.Keras.Layers +{ + public partial interface ILayersApi + { + public ILayer ELU(float alpha = 0.1f); + public ILayer SELU(); + public ILayer Softmax(Axis axis); + public ILayer Softplus(); + public ILayer HardSigmoid(); + public ILayer Softsign(); + public ILayer Swish(); + public ILayer Tanh(); + public ILayer Exponential(); + } +} diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Attention.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Attention.cs new file mode 100644 index 00000000..22fb50d3 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Attention.cs @@ -0,0 +1,28 @@ +using System; +using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.NumPy; + +namespace Tensorflow.Keras.Layers +{ + public partial interface ILayersApi + { + public ILayer Attention(bool use_scale = false, + string score_mode = "dot", + bool causal = false, + float dropout = 0f); + public ILayer MultiHeadAttention(int num_heads, + int key_dim, + int? value_dim = null, + float dropout = 0f, + bool use_bias = true, + Shape output_shape = null, + Shape attention_axes = null, + IInitializer kernel_initializer = null, + IInitializer bias_initializer = null, + IRegularizer kernel_regularizer = null, + IRegularizer bias_regularizer = null, + IRegularizer activity_regularizer = null, + Action kernel_constraint = null, + Action bias_constraint = null); + } +} diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Cropping.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Cropping.cs new file mode 100644 index 00000000..602e7a88 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Cropping.cs @@ -0,0 +1,13 @@ +using System; +using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.NumPy; + +namespace Tensorflow.Keras.Layers +{ + public partial interface ILayersApi + { + public ILayer Cropping1D(NDArray cropping); + public ILayer Cropping2D(NDArray cropping, Cropping2DArgs.DataFormat data_format = Cropping2DArgs.DataFormat.channels_last); + public ILayer Cropping3D(NDArray cropping, Cropping3DArgs.DataFormat data_format = Cropping3DArgs.DataFormat.channels_last); + } +} diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Merging.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Merging.cs new file mode 100644 index 00000000..d0a7f09f --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Merging.cs @@ -0,0 +1,10 @@ +using System; +using Tensorflow.NumPy; + +namespace Tensorflow.Keras.Layers +{ + public partial interface ILayersApi + { + public ILayer Concatenate(int axis = -1); + } +} diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Reshaping.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Reshaping.cs new file mode 100644 index 00000000..d41e0688 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.Reshaping.cs @@ -0,0 +1,18 @@ +using System; +using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.NumPy; + +namespace Tensorflow.Keras.Layers +{ + public partial interface ILayersApi + { + public ILayer Reshape(Shape target_shape); + public ILayer Reshape(object[] target_shape); + + public ILayer UpSampling2D(Shape size = null, + string data_format = null, + string interpolation = "nearest"); + + public ILayer ZeroPadding2D(NDArray padding); + } +} diff --git a/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs new file mode 100644 index 00000000..5945bb55 --- /dev/null +++ b/src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs @@ -0,0 +1,169 @@ +using System; +using static Google.Protobuf.Reflection.FieldDescriptorProto.Types; + +namespace Tensorflow.Keras.Layers +{ + public partial interface ILayersApi + { + public IPreprocessing preprocessing { get; } + + public ILayer Add(); + + public ILayer AveragePooling2D(Shape pool_size = null, + Shape strides = null, + string padding = "valid", + string data_format = null); + + public ILayer BatchNormalization(int axis = -1, + float momentum = 0.99f, + float epsilon = 0.001f, + bool center = true, + bool scale = true, + IInitializer beta_initializer = null, + IInitializer gamma_initializer = null, + IInitializer moving_mean_initializer = null, + IInitializer moving_variance_initializer = null, + bool trainable = true, + string name = null, + bool renorm = false, + float renorm_momentum = 0.99f); + + public ILayer Conv1D(int filters, + Shape kernel_size, + int strides = 1, + string padding = "valid", + string data_format = "channels_last", + int dilation_rate = 1, + int groups = 1, + string activation = null, + bool use_bias = true, + string kernel_initializer = "glorot_uniform", + string bias_initializer = "zeros"); + + public ILayer Conv2D(int filters, + Shape kernel_size = null, + Shape strides = null, + string padding = "valid", + string data_format = null, + Shape dilation_rate = null, + int groups = 1, + Activation activation = null, + bool use_bias = true, + IInitializer kernel_initializer = null, + IInitializer bias_initializer = null, + IRegularizer kernel_regularizer = null, + IRegularizer bias_regularizer = null, + IRegularizer activity_regularizer = null); + + public ILayer Conv2D(int filters, + Shape kernel_size = null, + Shape strides = null, + string padding = "valid", + string data_format = null, + Shape dilation_rate = null, + int groups = 1, + string activation = null, + bool use_bias = true, + string kernel_initializer = "glorot_uniform", + string bias_initializer = "zeros"); + + public ILayer Dense(int units); + public ILayer Dense(int units, + string activation = null, + Shape input_shape = null); + public ILayer Dense(int units, + Activation activation = null, + IInitializer kernel_initializer = null, + bool use_bias = true, + IInitializer bias_initializer = null, + Shape input_shape = null); + + public ILayer Dropout(float rate, Shape noise_shape = null, int? seed = null); + + public ILayer Embedding(int input_dim, + int output_dim, + IInitializer embeddings_initializer = null, + bool mask_zero = false, + Shape input_shape = null, + int input_length = -1); + + public ILayer EinsumDense(string equation, + Shape output_shape, + string bias_axes, + Activation activation = null, + IInitializer kernel_initializer = null, + IInitializer bias_initializer = null, + IRegularizer kernel_regularizer = null, + IRegularizer bias_regularizer = null, + IRegularizer activity_regularizer = null, + Action kernel_constraint = null, + Action bias_constraint = null); + + public ILayer Flatten(string data_format = null); + + public ILayer GlobalAveragePooling1D(string data_format = "channels_last"); + public ILayer GlobalAveragePooling2D(); + public ILayer GlobalAveragePooling2D(string data_format = "channels_last"); + public ILayer GlobalMaxPooling1D(string data_format = "channels_last"); + public ILayer GlobalMaxPooling2D(string data_format = "channels_last"); + + public Tensors Input(Shape shape, + string name = null, + bool sparse = false, + bool ragged = false); + public ILayer InputLayer(Shape input_shape, + string name = null, + bool sparse = false, + bool ragged = false); + + public ILayer LayerNormalization(Axis? axis, + float epsilon = 1e-3f, + bool center = true, + bool scale = true, + IInitializer beta_initializer = null, + IInitializer gamma_initializer = null); + + public ILayer LeakyReLU(float alpha = 0.3f); + + public ILayer LSTM(int units, + Activation activation = null, + Activation recurrent_activation = null, + bool use_bias = true, + IInitializer kernel_initializer = null, + IInitializer recurrent_initializer = null, + IInitializer bias_initializer = null, + bool unit_forget_bias = true, + float dropout = 0f, + float recurrent_dropout = 0f, + int implementation = 2, + bool return_sequences = false, + bool return_state = false, + bool go_backwards = false, + bool stateful = false, + bool time_major = false, + bool unroll = false); + + public ILayer MaxPooling1D(int? pool_size = null, + int? strides = null, + string padding = "valid", + string data_format = null); + public ILayer MaxPooling2D(Shape pool_size = null, + Shape strides = null, + string padding = "valid", + string data_format = null); + + public ILayer Permute(int[] dims); + + public ILayer Rescaling(float scale, + float offset = 0, + Shape input_shape = null); + + public ILayer SimpleRNN(int units, + string activation = "tanh", + string kernel_initializer = "glorot_uniform", + string recurrent_initializer = "orthogonal", + string bias_initializer = "zeros"); + + public ILayer Subtract(); + } +} diff --git a/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs b/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs index 222b10bb..064c7362 100644 --- a/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs +++ b/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs @@ -20,11 +20,11 @@ namespace Tensorflow.NumPy Marshal.Copy(y.BufferToArray(), 0, x.TensorDataPointer, (int)x.bytesize); } - public NDArray rand(params int[] shape) - => throw new NotImplementedException(""); + public NDArray random(Shape size) + => uniform(low: 0, high: 1, size: size); [AutoNumPy] - public NDArray randint(int low, int? high = null, Shape size = null, TF_DataType dtype = TF_DataType.TF_INT32) + public NDArray randint(int low, int? high = null, Shape? size = null, TF_DataType dtype = TF_DataType.TF_INT32) { if(high == null) { @@ -41,11 +41,11 @@ namespace Tensorflow.NumPy => new NDArray(random_ops.random_normal(shape ?? Shape.Scalar)); [AutoNumPy] - public NDArray normal(float loc = 0.0f, float scale = 1.0f, Shape size = null) + public NDArray normal(float loc = 0.0f, float scale = 1.0f, Shape? size = null) => new NDArray(random_ops.random_normal(size ?? Shape.Scalar, mean: loc, stddev: scale)); [AutoNumPy] - public NDArray uniform(float low = 0.0f, float high = 1.0f, Shape size = null) + public NDArray uniform(float low = 0.0f, float high = 1.0f, Shape? size = null) => new NDArray(random_ops.random_uniform(size ?? Shape.Scalar, low, high)); } } diff --git a/src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs b/src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs index 7c5b21b6..041268b7 100644 --- a/src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs +++ b/src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs @@ -18,6 +18,7 @@ using System; using System.Collections.Generic; using Tensorflow.Keras; using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Rnn; using Tensorflow.Keras.Engine; using Tensorflow.Operations; using Tensorflow.Util; diff --git a/src/TensorFlowNET.Core/Tensorflow.Binding.csproj b/src/TensorFlowNET.Core/Tensorflow.Binding.csproj index 4bd0a490..36449826 100644 --- a/src/TensorFlowNET.Core/Tensorflow.Binding.csproj +++ b/src/TensorFlowNET.Core/Tensorflow.Binding.csproj @@ -5,8 +5,8 @@ Tensorflow.Binding Tensorflow 2.2.0 - 0.70.2 - 9.0 + 0.100.0 + 10.0 enable Haiping Chen, Meinrad Recheis, Eli Belash SciSharp STACK @@ -20,9 +20,9 @@ Google's TensorFlow full binding in .NET Standard. Building, training and infering deep learning models. https://tensorflownet.readthedocs.io - 0.70.1.0 + 0.100.0.0 - tf.net 0.70.x and above are based on tensorflow native 2.7.0 + tf.net 0.100.x and above are based on tensorflow native 2.10.0 * Eager Mode is added finally. * tf.keras is partially working. @@ -35,14 +35,17 @@ https://tensorflownet.readthedocs.io tf.net 0.4x.x aligns with TensorFlow v2.4.1 native library. tf.net 0.6x.x aligns with TensorFlow v2.6.x native library. - tf.net 0.7x.x aligns with TensorFlow v2.7.x native library. - 0.70.1.0 + tf.net 0.7x.x aligns with TensorFlow v2.7.x native library. + tf.net 0.10x.x aligns with TensorFlow v2.10.x native library. + + 0.100.0.0 LICENSE true true Open.snk AnyCPU;x64 TensorFlow.NET + Debug;Release;GPU @@ -51,6 +54,12 @@ https://tensorflownet.readthedocs.io AnyCPU + + true + TRACE;DEBUG;TRACK_TENSOR_LIFE_1 + AnyCPU + + true TRACE;DEBUG;TRACK_TENSOR_LIFE1 @@ -58,6 +67,13 @@ https://tensorflownet.readthedocs.io TensorFlow.NET.xml + + true + TRACE;DEBUG;TRACK_TENSOR_LIFE1 + x64 + TensorFlow.NET.xml + + true diff --git a/src/TensorFlowNET.Core/tensorflow.cs b/src/TensorFlowNET.Core/tensorflow.cs index 8a2c78a7..e02723b7 100644 --- a/src/TensorFlowNET.Core/tensorflow.cs +++ b/src/TensorFlowNET.Core/tensorflow.cs @@ -20,6 +20,7 @@ using System.Threading; using Tensorflow.Contexts; using Tensorflow.Eager; using Tensorflow.Gradients; +using Tensorflow.Keras; namespace Tensorflow { @@ -51,6 +52,8 @@ namespace Tensorflow ThreadLocal _runner = new ThreadLocal(() => new EagerRunner()); public IEagerRunner Runner => _runner.Value; + public IKerasApi keras { get; set; } + public tensorflow() { Logger = new LoggerConfiguration() diff --git a/src/TensorFlowNET.Keras/KerasApi.cs b/src/TensorFlowNET.Keras/KerasApi.cs index d10ced0c..f79c2b5f 100644 --- a/src/TensorFlowNET.Keras/KerasApi.cs +++ b/src/TensorFlowNET.Keras/KerasApi.cs @@ -2,6 +2,9 @@ namespace Tensorflow { + /// + /// Deprecated, will use tf.keras + /// public static class KerasApi { public static KerasInterface keras { get; } = new KerasInterface(); diff --git a/src/TensorFlowNET.Keras/KerasInterface.cs b/src/TensorFlowNET.Keras/KerasInterface.cs index 02362a55..5bf9f97f 100644 --- a/src/TensorFlowNET.Keras/KerasInterface.cs +++ b/src/TensorFlowNET.Keras/KerasInterface.cs @@ -10,18 +10,17 @@ using Tensorflow.Keras.Losses; using Tensorflow.Keras.Metrics; using Tensorflow.Keras.Models; using Tensorflow.Keras.Optimizers; -using Tensorflow.Keras.Saving; using Tensorflow.Keras.Utils; using System.Threading; namespace Tensorflow.Keras { - public class KerasInterface + public class KerasInterface : IKerasApi { public KerasDataset datasets { get; } = new KerasDataset(); public Initializers initializers { get; } = new Initializers(); public Regularizers regularizers { get; } = new Regularizers(); - public LayersApi layers { get; } = new LayersApi(); + public ILayersApi layers { get; } = new LayersApi(); public LossesApi losses { get; } = new LossesApi(); public Activations activations { get; } = new Activations(); public Preprocessing preprocessing { get; } = new Preprocessing(); diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.Activation.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.Activation.cs index 0978d0d3..24a56839 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.Activation.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.Activation.cs @@ -7,16 +7,16 @@ using static Tensorflow.KerasApi; namespace Tensorflow.Keras.Layers { public partial class LayersApi { - public ELU ELU ( float alpha = 0.1f ) + public ILayer ELU ( float alpha = 0.1f ) => new ELU(new ELUArgs { Alpha = alpha }); - public SELU SELU () + public ILayer SELU () => new SELU(new LayerArgs { }); - public Softmax Softmax ( Axis axis ) => new Softmax(new SoftmaxArgs { axis = axis }); - public Softplus Softplus () => new Softplus(new LayerArgs { }); - public HardSigmoid HardSigmoid () => new HardSigmoid(new LayerArgs { }); - public Softsign Softsign () => new Softsign(new LayerArgs { }); - public Swish Swish () => new Swish(new LayerArgs { }); - public Tanh Tanh () => new Tanh(new LayerArgs { }); - public Exponential Exponential () => new Exponential(new LayerArgs { }); + public ILayer Softmax ( Axis axis ) => new Softmax(new SoftmaxArgs { axis = axis }); + public ILayer Softplus () => new Softplus(new LayerArgs { }); + public ILayer HardSigmoid () => new HardSigmoid(new LayerArgs { }); + public ILayer Softsign () => new Softsign(new LayerArgs { }); + public ILayer Swish () => new Swish(new LayerArgs { }); + public ILayer Tanh () => new Tanh(new LayerArgs { }); + public ILayer Exponential () => new Exponential(new LayerArgs { }); } } diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.Attention.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.Attention.cs index 5effd175..859e9c14 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.Attention.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.Attention.cs @@ -10,7 +10,7 @@ namespace Tensorflow.Keras.Layers { public partial class LayersApi { - public Attention Attention(bool use_scale = false, + public ILayer Attention(bool use_scale = false, string score_mode = "dot", bool causal = false, float dropout = 0f) => @@ -21,7 +21,7 @@ namespace Tensorflow.Keras.Layers causal = causal, dropout = dropout }); - public MultiHeadAttention MultiHeadAttention(int num_heads, + public ILayer MultiHeadAttention(int num_heads, int key_dim, int? value_dim = null, float dropout = 0f, diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs index f4d2230c..339ddb85 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.Cropping.cs @@ -10,7 +10,7 @@ namespace Tensorflow.Keras.Layers { /// Cropping layer for 1D input /// /// cropping size - public Cropping1D Cropping1D ( NDArray cropping ) + public ILayer Cropping1D ( NDArray cropping ) => new Cropping1D(new CroppingArgs { cropping = cropping }); @@ -18,7 +18,7 @@ namespace Tensorflow.Keras.Layers { /// /// Cropping layer for 2D input
/// - public Cropping2D Cropping2D ( NDArray cropping, Cropping2DArgs.DataFormat data_format = Cropping2DArgs.DataFormat.channels_last ) + public ILayer Cropping2D ( NDArray cropping, Cropping2DArgs.DataFormat data_format = Cropping2DArgs.DataFormat.channels_last ) => new Cropping2D(new Cropping2DArgs { cropping = cropping, data_format = data_format @@ -27,7 +27,7 @@ namespace Tensorflow.Keras.Layers { /// /// Cropping layer for 3D input
/// - public Cropping3D Cropping3D ( NDArray cropping, Cropping3DArgs.DataFormat data_format = Cropping3DArgs.DataFormat.channels_last ) + public ILayer Cropping3D ( NDArray cropping, Cropping3DArgs.DataFormat data_format = Cropping3DArgs.DataFormat.channels_last ) => new Cropping3D(new Cropping3DArgs { cropping = cropping, data_format = data_format diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.Merging.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.Merging.cs index ecf8c0a6..d94bfb4d 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.Merging.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.Merging.cs @@ -13,7 +13,7 @@ namespace Tensorflow.Keras.Layers /// /// Axis along which to concatenate. /// - public Concatenate Concatenate(int axis = -1) + public ILayer Concatenate(int axis = -1) => new Concatenate(new MergeArgs { Axis = axis diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.Reshaping.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.Reshaping.cs index 5cfec89e..d3db1d66 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.Reshaping.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.Reshaping.cs @@ -11,7 +11,7 @@ namespace Tensorflow.Keras.Layers { /// /// /// - public ZeroPadding2D ZeroPadding2D ( NDArray padding ) + public ILayer ZeroPadding2D ( NDArray padding ) => new ZeroPadding2D(new ZeroPadding2DArgs { Padding = padding }); @@ -24,7 +24,7 @@ namespace Tensorflow.Keras.Layers { /// /// /// - public UpSampling2D UpSampling2D ( Shape size = null, + public ILayer UpSampling2D ( Shape size = null, string data_format = null, string interpolation = "nearest" ) => new UpSampling2D(new UpSampling2DArgs { @@ -34,7 +34,7 @@ namespace Tensorflow.Keras.Layers { /// /// Permutes the dimensions of the input according to a given pattern. /// - public Permute Permute ( int[] dims ) + public ILayer Permute ( int[] dims ) => new Permute(new PermuteArgs { dims = dims }); @@ -44,12 +44,12 @@ namespace Tensorflow.Keras.Layers { /// /// /// - public Reshape Reshape ( Shape target_shape ) - => new Reshape(new ReshapeArgs { - TargetShape = target_shape - }); + public ILayer Reshape ( Shape target_shape ) + => new Reshape(new ReshapeArgs { + TargetShape = target_shape + }); - public Reshape Reshape ( object[] target_shape ) + public ILayer Reshape ( object[] target_shape ) => new Reshape(new ReshapeArgs { TargetShapeObjects = target_shape }); diff --git a/src/TensorFlowNET.Keras/Layers/LayersApi.cs b/src/TensorFlowNET.Keras/Layers/LayersApi.cs index 48856735..8498f5ac 100644 --- a/src/TensorFlowNET.Keras/Layers/LayersApi.cs +++ b/src/TensorFlowNET.Keras/Layers/LayersApi.cs @@ -1,16 +1,18 @@ using System; -using Tensorflow.NumPy; -using System.Collections.Generic; using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Lstm; +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; namespace Tensorflow.Keras.Layers { - public partial class LayersApi + public partial class LayersApi : ILayersApi { - public Preprocessing preprocessing { get; } = new Preprocessing(); + public IPreprocessing preprocessing { get; } = new Preprocessing(); /// /// Layer that normalizes its inputs. @@ -38,7 +40,7 @@ namespace Tensorflow.Keras.Layers /// Note that momentum is still applied to get the means and variances for inference. /// /// Tensor of the same shape as input. - public BatchNormalization BatchNormalization(int axis = -1, + public ILayer BatchNormalization(int axis = -1, float momentum = 0.99f, float epsilon = 0.001f, bool center = true, @@ -84,7 +86,7 @@ namespace Tensorflow.Keras.Layers /// Initializer for the kernel weights matrix (see keras.initializers). /// Initializer for the bias vector (see keras.initializers). /// A tensor of rank 3 representing activation(conv1d(inputs, kernel) + bias). - public Conv1D Conv1D(int filters, + public ILayer Conv1D(int filters, Shape kernel_size, int strides = 1, string padding = "valid", @@ -131,7 +133,7 @@ namespace Tensorflow.Keras.Layers /// Regularizer function applied to the bias vector (see keras.regularizers). /// Regularizer function applied to the output of the layer (its "activation") (see keras.regularizers). /// A tensor of rank 4+ representing activation(conv2d(inputs, kernel) + bias). - public Conv2D Conv2D(int filters, + public ILayer Conv2D(int filters, Shape kernel_size = null, Shape strides = null, string padding = "valid", @@ -184,7 +186,7 @@ namespace Tensorflow.Keras.Layers /// The name of the regularizer function applied to the bias vector (see keras.regularizers). /// The name of the regularizer function applied to the output of the layer (its "activation") (see keras.regularizers). /// A tensor of rank 4+ representing activation(conv2d(inputs, kernel) + bias). - public Conv2D Conv2D(int filters, + public ILayer Conv2D(int filters, Shape kernel_size = null, Shape strides = null, string padding = "valid", @@ -228,7 +230,7 @@ namespace Tensorflow.Keras.Layers /// The name of the regularizer function applied to the bias vector (see keras.regularizers). /// The name of the regularizer function applied to the output of the layer (its "activation") (see keras.regularizers). /// A tensor of rank 4+ representing activation(conv2d(inputs, kernel) + bias). - public Conv2DTranspose Conv2DTranspose(int filters, + public ILayer Conv2DTranspose(int filters, Shape kernel_size = null, Shape strides = null, string output_padding = "valid", @@ -270,7 +272,7 @@ namespace Tensorflow.Keras.Layers /// Initializer for the bias vector. /// N-D tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim). /// N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units). - public Dense Dense(int units, + public ILayer Dense(int units, Activation activation = null, IInitializer kernel_initializer = null, bool use_bias = true, @@ -294,7 +296,7 @@ namespace Tensorflow.Keras.Layers /// /// Positive integer, dimensionality of the output space. /// N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units). - public Dense Dense(int units) + public ILayer Dense(int units) => new Dense(new DenseArgs { Units = units, @@ -312,7 +314,7 @@ namespace Tensorflow.Keras.Layers /// Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x). /// N-D tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim). /// N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units). - public Dense Dense(int units, + public ILayer Dense(int units, string activation = null, Shape input_shape = null) => new Dense(new DenseArgs @@ -364,7 +366,7 @@ namespace Tensorflow.Keras.Layers } - public EinsumDense EinsumDense(string equation, + public ILayer EinsumDense(string equation, Shape output_shape, string bias_axes, Activation activation = null, @@ -402,7 +404,7 @@ namespace Tensorflow.Keras.Layers /// /// An integer to use as random seed. /// - public Dropout Dropout(float rate, Shape noise_shape = null, int? seed = null) + public ILayer Dropout(float rate, Shape noise_shape = null, int? seed = null) => new Dropout(new DropoutArgs { Rate = rate, @@ -421,7 +423,7 @@ namespace Tensorflow.Keras.Layers /// Initializer for the embeddings matrix (see keras.initializers). /// /// - public Embedding Embedding(int input_dim, + public ILayer Embedding(int input_dim, int output_dim, IInitializer embeddings_initializer = null, bool mask_zero = false, @@ -446,7 +448,7 @@ namespace Tensorflow.Keras.Layers /// If you never set it, then it will be "channels_last". /// /// - public Flatten Flatten(string data_format = null) + public ILayer Flatten(string data_format = null) => new Flatten(new FlattenArgs { DataFormat = data_format @@ -482,7 +484,7 @@ namespace Tensorflow.Keras.Layers return input_layer.InboundNodes[0].Outputs; } - public InputLayer InputLayer(Shape input_shape, + public ILayer InputLayer(Shape input_shape, string name = null, bool sparse = false, bool ragged = false) @@ -502,7 +504,7 @@ namespace Tensorflow.Keras.Layers /// /// /// - public AveragePooling2D AveragePooling2D(Shape pool_size = null, + public ILayer AveragePooling2D(Shape pool_size = null, Shape strides = null, string padding = "valid", string data_format = null) @@ -527,7 +529,7 @@ namespace Tensorflow.Keras.Layers /// channels_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps). /// /// - public MaxPooling1D MaxPooling1D(int? pool_size = null, + public ILayer MaxPooling1D(int? pool_size = null, int? strides = null, string padding = "valid", string data_format = null) @@ -564,7 +566,7 @@ namespace Tensorflow.Keras.Layers /// It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. /// If you never set it, then it will be "channels_last" /// - public MaxPooling2D MaxPooling2D(Shape pool_size = null, + public ILayer MaxPooling2D(Shape pool_size = null, Shape strides = null, string padding = "valid", string data_format = null) @@ -618,7 +620,7 @@ namespace Tensorflow.Keras.Layers return layer.Apply(inputs); } - public Layer LayerNormalization(Axis? axis, + public ILayer LayerNormalization(Axis? axis, float epsilon = 1e-3f, bool center = true, bool scale = true, @@ -638,45 +640,30 @@ namespace Tensorflow.Keras.Layers /// /// Negative slope coefficient. /// - public Layer LeakyReLU(float alpha = 0.3f) + public ILayer LeakyReLU(float alpha = 0.3f) => new LeakyReLu(new LeakyReLuArgs { Alpha = alpha }); - /// - /// Fully-connected RNN where the output is to be fed back to input. - /// - /// Positive integer, dimensionality of the output space. - /// - public Layer SimpleRNN(int units) => SimpleRNN(units, "tanh"); - - /// - /// Fully-connected RNN where the output is to be fed back to input. - /// - /// Positive integer, dimensionality of the output space. - /// Activation function to use. If you pass null, no activation is applied (ie. "linear" activation: a(x) = x). - /// - public Layer SimpleRNN(int units, - Activation activation = null) - => new SimpleRNN(new SimpleRNNArgs - { - Units = units, - Activation = activation - }); - /// /// /// /// Positive integer, dimensionality of the output space. /// The name of the activation function to use. Default: hyperbolic tangent (tanh).. /// - public Layer SimpleRNN(int units, - string activation = "tanh") + public ILayer SimpleRNN(int units, + string activation = "tanh", + string kernel_initializer = "glorot_uniform", + string recurrent_initializer = "orthogonal", + string bias_initializer = "zeros") => new SimpleRNN(new SimpleRNNArgs { Units = units, - Activation = GetActivationByName(activation) + Activation = GetActivationByName(activation), + KernelInitializer = GetInitializerByName(kernel_initializer), + RecurrentInitializer= GetInitializerByName(recurrent_initializer), + BiasInitializer= GetInitializerByName(bias_initializer) }); /// @@ -706,7 +693,7 @@ namespace Tensorflow.Keras.Layers /// although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. /// /// - public Layer LSTM(int units, + public ILayer LSTM(int units, Activation activation = null, Activation recurrent_activation = null, bool use_bias = true, @@ -749,7 +736,7 @@ namespace Tensorflow.Keras.Layers /// /// /// - public Rescaling Rescaling(float scale, + public ILayer Rescaling(float scale, float offset = 0, Shape input_shape = null) => new Rescaling(new RescalingArgs @@ -763,21 +750,21 @@ namespace Tensorflow.Keras.Layers /// /// /// - public Add Add() + public ILayer Add() => new Add(new MergeArgs { }); /// /// /// /// - public Subtract Subtract() + public ILayer Subtract() => new Subtract(new MergeArgs { }); /// /// Global max pooling operation for spatial data. /// /// - public GlobalAveragePooling2D GlobalAveragePooling2D() + public ILayer GlobalAveragePooling2D() => new GlobalAveragePooling2D(new Pooling2DArgs { }); /// @@ -787,7 +774,7 @@ namespace Tensorflow.Keras.Layers /// channels_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps). /// /// - public GlobalAveragePooling1D GlobalAveragePooling1D(string data_format = "channels_last") + public ILayer GlobalAveragePooling1D(string data_format = "channels_last") => new GlobalAveragePooling1D(new Pooling1DArgs { DataFormat = data_format }); /// @@ -796,7 +783,7 @@ namespace Tensorflow.Keras.Layers /// A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. /// channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). /// - public GlobalAveragePooling2D GlobalAveragePooling2D(string data_format = "channels_last") + public ILayer GlobalAveragePooling2D(string data_format = "channels_last") => new GlobalAveragePooling2D(new Pooling2DArgs { DataFormat = data_format }); /// @@ -807,7 +794,7 @@ namespace Tensorflow.Keras.Layers /// channels_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps). /// /// - public GlobalMaxPooling1D GlobalMaxPooling1D(string data_format = "channels_last") + public ILayer GlobalMaxPooling1D(string data_format = "channels_last") => new GlobalMaxPooling1D(new Pooling1DArgs { DataFormat = data_format }); /// @@ -816,7 +803,7 @@ namespace Tensorflow.Keras.Layers /// A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. /// channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). /// - public GlobalMaxPooling2D GlobalMaxPooling2D(string data_format = "channels_last") + public ILayer GlobalMaxPooling2D(string data_format = "channels_last") => new GlobalMaxPooling2D(new Pooling2DArgs { DataFormat = data_format }); @@ -848,6 +835,7 @@ namespace Tensorflow.Keras.Layers "glorot_uniform" => tf.glorot_uniform_initializer, "zeros" => tf.zeros_initializer, "ones" => tf.ones_initializer, + "orthogonal" => tf.orthogonal_initializer, _ => tf.glorot_uniform_initializer }; } diff --git a/src/TensorFlowNET.Keras/Layers/LSTM.cs b/src/TensorFlowNET.Keras/Layers/Lstm/LSTM.cs similarity index 87% rename from src/TensorFlowNET.Keras/Layers/LSTM.cs rename to src/TensorFlowNET.Keras/Layers/Lstm/LSTM.cs index 73a2df12..b7d97384 100644 --- a/src/TensorFlowNET.Keras/Layers/LSTM.cs +++ b/src/TensorFlowNET.Keras/Layers/Lstm/LSTM.cs @@ -1,8 +1,9 @@ using System.Linq; -using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Lstm; using Tensorflow.Keras.Engine; +using Tensorflow.Keras.Layers.Rnn; -namespace Tensorflow.Keras.Layers +namespace Tensorflow.Keras.Layers.Lstm { /// /// Long Short-Term Memory layer - Hochreiter 1997. diff --git a/src/TensorFlowNET.Keras/Layers/LSTMCell.cs b/src/TensorFlowNET.Keras/Layers/Lstm/LSTMCell.cs similarity index 72% rename from src/TensorFlowNET.Keras/Layers/LSTMCell.cs rename to src/TensorFlowNET.Keras/Layers/Lstm/LSTMCell.cs index dda279a7..3cd35a09 100644 --- a/src/TensorFlowNET.Keras/Layers/LSTMCell.cs +++ b/src/TensorFlowNET.Keras/Layers/Lstm/LSTMCell.cs @@ -1,7 +1,7 @@ -using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Lstm; using Tensorflow.Keras.Engine; -namespace Tensorflow.Keras.Layers +namespace Tensorflow.Keras.Layers.Lstm { public class LSTMCell : Layer { diff --git a/src/TensorFlowNET.Keras/Layers/RNN.cs b/src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs similarity index 95% rename from src/TensorFlowNET.Keras/Layers/RNN.cs rename to src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs index 293c27fb..c2b86ae4 100644 --- a/src/TensorFlowNET.Keras/Layers/RNN.cs +++ b/src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs @@ -1,10 +1,12 @@ using System; 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 +namespace Tensorflow.Keras.Layers.Rnn { public class RNN : Layer { @@ -14,6 +16,8 @@ namespace Tensorflow.Keras.Layers private object _states = null; private object constants_spec = null; private int _num_constants = 0; + protected IVariableV1 kernel; + protected IVariableV1 bias; public RNN(RNNArgs args) : base(PreConstruct(args)) { diff --git a/src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNN.cs b/src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNN.cs new file mode 100644 index 00000000..58b700fe --- /dev/null +++ b/src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNN.cs @@ -0,0 +1,31 @@ +using System.Data; +using Tensorflow.Keras.ArgsDefinition.Rnn; +using Tensorflow.Operations.Activation; +using static HDF.PInvoke.H5Z; +using static Tensorflow.ApiDef.Types; + +namespace Tensorflow.Keras.Layers.Rnn +{ + public class SimpleRNN : RNN + { + SimpleRNNArgs args; + SimpleRNNCell cell; + public SimpleRNN(SimpleRNNArgs args) : base(args) + { + this.args = args; + } + + protected override void build(Tensors inputs) + { + var input_shape = inputs.shape; + var input_dim = input_shape[-1]; + + kernel = add_weight("kernel", (input_shape[-1], args.Units), + initializer: args.KernelInitializer + //regularizer = self.kernel_regularizer, + //constraint = self.kernel_constraint, + //caching_device = default_caching_device, + ); + } + } +} \ No newline at end of file diff --git a/src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNNCell.cs b/src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNNCell.cs new file mode 100644 index 00000000..de50c361 --- /dev/null +++ b/src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNNCell.cs @@ -0,0 +1,21 @@ +using System; +using System.Collections.Generic; +using System.Text; +using Tensorflow.Keras.ArgsDefinition.Rnn; +using Tensorflow.Keras.Engine; + +namespace Tensorflow.Keras.Layers.Rnn +{ + public class SimpleRNNCell : Layer + { + public SimpleRNNCell(SimpleRNNArgs args) : base(args) + { + + } + + protected override void build(Tensors inputs) + { + + } + } +} diff --git a/src/TensorFlowNET.Keras/Layers/StackedRNNCells.cs b/src/TensorFlowNET.Keras/Layers/Rnn/StackedRNNCells.cs similarity index 98% rename from src/TensorFlowNET.Keras/Layers/StackedRNNCells.cs rename to src/TensorFlowNET.Keras/Layers/Rnn/StackedRNNCells.cs index 2da206ca..eead274a 100644 --- a/src/TensorFlowNET.Keras/Layers/StackedRNNCells.cs +++ b/src/TensorFlowNET.Keras/Layers/Rnn/StackedRNNCells.cs @@ -2,9 +2,10 @@ using System.Collections.Generic; using System.ComponentModel; using Tensorflow.Keras.ArgsDefinition; +using Tensorflow.Keras.ArgsDefinition.Rnn; using Tensorflow.Keras.Engine; -namespace Tensorflow.Keras.Layers +namespace Tensorflow.Keras.Layers.Rnn { public class StackedRNNCells : Layer, RNNArgs.IRnnArgCell { diff --git a/src/TensorFlowNET.Keras/Layers/SimpleRNN.cs b/src/TensorFlowNET.Keras/Layers/SimpleRNN.cs deleted file mode 100644 index c1fc4afd..00000000 --- a/src/TensorFlowNET.Keras/Layers/SimpleRNN.cs +++ /dev/null @@ -1,14 +0,0 @@ -using Tensorflow.Keras.ArgsDefinition; - -namespace Tensorflow.Keras.Layers -{ - public class SimpleRNN : RNN - { - - public SimpleRNN(RNNArgs args) : base(args) - { - - } - - } -} \ No newline at end of file diff --git a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.Resizing.cs b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.Resizing.cs index 5e93f583..0be7f1e6 100644 --- a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.Resizing.cs +++ b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.Resizing.cs @@ -15,7 +15,7 @@ namespace Tensorflow.Keras /// /// /// - public Resizing Resizing(int height, int width, string interpolation = "bilinear") + public ILayer Resizing(int height, int width, string interpolation = "bilinear") => new Resizing(new ResizingArgs { Height = height, diff --git a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.cs b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.cs index 994a36d6..94fc4a20 100644 --- a/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.cs +++ b/src/TensorFlowNET.Keras/Preprocessings/Preprocessing.cs @@ -5,7 +5,7 @@ using Tensorflow.Keras.Preprocessings; namespace Tensorflow.Keras { - public partial class Preprocessing + public partial class Preprocessing : IPreprocessing { public Sequence sequence => new Sequence(); public DatasetUtils dataset_utils => new DatasetUtils(); @@ -14,7 +14,7 @@ namespace Tensorflow.Keras private static TextApi _text = new TextApi(); - public TextVectorization TextVectorization(Func standardize = null, + public ILayer TextVectorization(Func standardize = null, string split = "whitespace", int max_tokens = -1, string output_mode = "int", diff --git a/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj b/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj index 3d448454..0c3eff9f 100644 --- a/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj +++ b/src/TensorFlowNET.Keras/Tensorflow.Keras.csproj @@ -3,11 +3,11 @@ netstandard2.0 Tensorflow.Keras - 9.0 + 10.0 enable Tensorflow.Keras AnyCPU;x64 - 0.7.0 + 0.10.0 Haiping Chen Keras for .NET Apache 2.0, Haiping Chen 2021 @@ -37,9 +37,10 @@ Keras is an API designed for human beings, not machines. Keras follows best prac Git true Open.snk - 0.7.0.0 - 0.7.0.0 + 0.10.0.0 + 0.10.0.0 LICENSE + Debug;Release;GPU @@ -47,6 +48,11 @@ Keras is an API designed for human beings, not machines. Keras follows best prac false + + DEBUG;TRACE + false + + false @@ -55,6 +61,10 @@ Keras is an API designed for human beings, not machines. Keras follows best prac Tensorflow.Keras.xml + + Tensorflow.Keras.xml + + diff --git a/src/TensorFlowNET.Keras/tf.layers.cs b/src/TensorFlowNET.Keras/tf.layers.cs index 3f5ed01c..da7c2347 100644 --- a/src/TensorFlowNET.Keras/tf.layers.cs +++ b/src/TensorFlowNET.Keras/tf.layers.cs @@ -134,7 +134,7 @@ namespace Tensorflow.Keras /// /// /// - public Tensor max_pooling2d(Tensor inputs, + public Tensor MaxPooling2D(Tensor inputs, int[] pool_size, int[] strides, string padding = "valid", diff --git a/src/python/.vscode/launch.json b/src/python/.vscode/launch.json new file mode 100644 index 00000000..2b2502c6 --- /dev/null +++ b/src/python/.vscode/launch.json @@ -0,0 +1,16 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": false + } + ] +} \ No newline at end of file diff --git a/src/python/simple_rnn.py b/src/python/simple_rnn.py new file mode 100644 index 00000000..97f9f3f3 --- /dev/null +++ b/src/python/simple_rnn.py @@ -0,0 +1,15 @@ +import numpy as np +import tensorflow as tf + +# tf.experimental.numpy +inputs = np.random.random([32, 10, 8]).astype(np.float32) +simple_rnn = tf.keras.layers.SimpleRNN(4) + +output = simple_rnn(inputs) # The output has shape `[32, 4]`. + +simple_rnn = tf.keras.layers.SimpleRNN( + 4, return_sequences=True, return_state=True) + +# whole_sequence_output has shape `[32, 10, 4]`. +# final_state has shape `[32, 4]`. +whole_sequence_output, final_state = simple_rnn(inputs) \ No newline at end of file diff --git a/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs b/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs index 0c02b5db..02298ce8 100644 --- a/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs +++ b/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs @@ -83,7 +83,7 @@ namespace TensorFlowNET.Keras.UnitTest { 2.5f, 2.6f, 2.7f, 2.8f }, { 3.5f, 3.6f, 3.7f, 3.8f } } }, dtype: np.float32); - var attention_layer = keras.layers.Attention(); + var attention_layer = (Attention)keras.layers.Attention(); //attention_layer.build(((1, 2, 4), (1, 3, 4))); var actual = attention_layer._calculate_scores(query: q, key: k); // Expected tensor of shape [1, 2, 3]. @@ -116,7 +116,7 @@ namespace TensorFlowNET.Keras.UnitTest { 2.5f, 2.6f, 2.7f, 2.8f }, { 3.5f, 3.6f, 3.7f, 3.8f } } }, dtype: np.float32); - var attention_layer = keras.layers.Attention(score_mode: "concat"); + var attention_layer = (Attention)keras.layers.Attention(score_mode: "concat"); //attention_layer.concat_score_weight = 1; attention_layer.concat_score_weight = base_layer_utils.make_variable(new VariableArgs() { Name = "concat_score_weight", diff --git a/test/TensorFlowNET.Keras.UnitTest/Layers/LayersTest.cs b/test/TensorFlowNET.Keras.UnitTest/Layers/LayersTest.cs index 53a13394..f4fdf94a 100644 --- a/test/TensorFlowNET.Keras.UnitTest/Layers/LayersTest.cs +++ b/test/TensorFlowNET.Keras.UnitTest/Layers/LayersTest.cs @@ -148,10 +148,9 @@ namespace TensorFlowNET.Keras.UnitTest } [TestMethod] - [Ignore] public void SimpleRNN() { - var inputs = np.random.rand(32, 10, 8).astype(np.float32); + var inputs = np.random.random((32, 10, 8)).astype(np.float32); var simple_rnn = keras.layers.SimpleRNN(4); var output = simple_rnn.Apply(inputs); Assert.AreEqual((32, 4), output.shape); diff --git a/test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj b/test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj index 6d0b1ca3..fc693b1e 100644 --- a/test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj +++ b/test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj @@ -4,7 +4,7 @@ net6.0 false - + 11.0 AnyCPU;x64 diff --git a/test/TensorFlowNET.UnitTest/Tensorflow.Binding.UnitTest.csproj b/test/TensorFlowNET.UnitTest/Tensorflow.Binding.UnitTest.csproj index ffb583c9..36ff4a3d 100644 --- a/test/TensorFlowNET.UnitTest/Tensorflow.Binding.UnitTest.csproj +++ b/test/TensorFlowNET.UnitTest/Tensorflow.Binding.UnitTest.csproj @@ -11,7 +11,7 @@ Open.snk - 9.0 + 11.0 AnyCPU;x64