tf.keras.layers. #570

Author: Oceania2018

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

@@ -40,7 +40,7 @@ public Tensor conv2d(Tensor inputs,
                 string data_format= "channels_last",
                 int[] dilation_rate = null,
                 bool use_bias = true,
-                IActivation activation = null,
+                Activation activation = null,
                 IInitializer kernel_initializer = null,
                 IInitializer bias_initializer = null,
                 bool trainable = true,
@@ -53,20 +53,23 @@ public Tensor conv2d(Tensor inputs,
                 if (bias_initializer == null)
                     bias_initializer = tf.zeros_initializer;
 
-                var layer = new Conv2D(filters,
-                    kernel_size: kernel_size,
-                    strides: strides,
-                    padding: padding,
-                    data_format: data_format,
-                    dilation_rate: dilation_rate,
-                    activation: activation,
-                    use_bias: use_bias,
-                    kernel_initializer: kernel_initializer,
-                    bias_initializer: bias_initializer,
-                    trainable: trainable,
-                    name: name);
+                var layer = new Conv2D(new Conv2DArgs
+                {
+                    Filters = filters,
+                    KernelSize = kernel_size,
+                    Strides = strides,
+                    Padding = padding,
+                    DataFormat = data_format,
+                    DilationRate = dilation_rate,
+                    Activation = activation,
+                    UseBias = use_bias,
+                    KernelInitializer = kernel_initializer,
+                    BiasInitializer = bias_initializer,
+                    Trainable = trainable,
+                    Name = name
+                });
 
-                return layer.apply(inputs).Item1;
+                return layer.Apply(inputs);
             }
 
             /// <summary>
@@ -140,13 +143,16 @@ public Tensor max_pooling2d(Tensor inputs,
                 string data_format = "channels_last",
                 string name = null)
             {
-                var layer = new MaxPooling2D(pool_size: pool_size,
-                    strides: strides,
-                    padding: padding,
-                    data_format: data_format,
-                    name: name);
+                var layer = new MaxPooling2D(new MaxPooling2DArgs
+                {
+                    PoolSize = pool_size,
+                    Strides = strides,
+                    Padding = padding,
+                    DataFormat = data_format,
+                    Name = name
+                });
 
-                return layer.apply(inputs).Item1;
+                return layer.Apply(inputs);
             }
 
             /// <summary>

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

@@ -66,9 +66,8 @@ public Tensor dropout(Tensor x, Tensor keep_prob = null, Tensor noise_shape = nu
                 Tensor keep = null;
                 if (keep_prob != null)
                     keep = 1.0f - keep_prob;
-                var rate_tensor = keep;
 
-                return nn_ops.dropout_v2(x, rate: rate_tensor, noise_shape: noise_shape, seed: seed, name: name);
+                return nn_ops.dropout_v2(x, rate: rate.Value, noise_shape: noise_shape, seed: seed, name: name);
             }
 
             /// <summary>

src/TensorFlowNET.Core/Framework/smart_module.cs

@@ -41,6 +41,14 @@ public static Tensor[] smart_cond<T>(Tensor pred,
                     name: name);
         }
 
+        public static Tensor smart_cond(bool pred,
+            Func<Tensor> true_fn = null,
+            Func<Tensor> false_fn = null,
+            string name = null)
+        {
+            return pred ? true_fn() : false_fn();
+        }
+
         public static bool? smart_constant_value(Tensor pred)
         {
             var pred_value = tensor_util.constant_value(pred);

src/TensorFlowNET.Core/Keras/ArgsDefinition/Conv2DArgs.cs

@@ -0,0 +1,11 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class Conv2DArgs : ConvArgs
+    {
+        
+    }
+}

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

@@ -0,0 +1,32 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class ConvArgs : LayerArgs
+    {
+        public int Rank { get; set; } = 2;
+        public int Filters { get; set; }
+        public TensorShape KernelSize { get; set; } = 5;
+
+        /// <summary>
+        /// specifying the stride length of the convolution.
+        /// </summary>
+        public TensorShape Strides { get; set; } = (1, 1);
+
+        public string Padding { get; set; } = "valid";
+        public string DataFormat { get; set; }
+        public TensorShape DilationRate { get; set; } = (1, 1);
+        public int Groups { get; set; } = 1;
+        public Activation Activation { get; set; }
+        public bool UseBias { get; set; }
+        public IInitializer KernelInitializer { get; set; } = tf.glorot_uniform_initializer;
+        public IInitializer BiasInitializer { get; set; } = tf.zeros_initializer;
+        public IInitializer KernelRegularizer { get; set; }
+        public IInitializer BiasRegularizer { get; set; }
+        public Action KernelConstraint { get; set; }
+        public Action BiasConstraint { get; set; }
+    }
+}

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

@@ -0,0 +1,27 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class DropoutArgs : LayerArgs
+    {
+        /// <summary>
+        /// Float between 0 and 1. Fraction of the input units to drop.
+        /// </summary>
+        public float Rate { get; set; }
+
+        /// <summary>
+        /// 1D integer tensor representing the shape of the
+        /// binary dropout mask that will be multiplied with the input.
+        /// </summary>
+        public TensorShape NoiseShape { get; set; }
+
+        /// <summary>
+        /// random seed.
+        /// </summary>
+        public int? Seed { get; set; }
+
+        public bool SupportsMasking { get; set; }
+    }
+}

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

@@ -0,0 +1,11 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class FlattenArgs : LayerArgs
+    {
+        public string DataFormat { get; set; }
+    }
+}

src/TensorFlowNET.Core/Keras/ArgsDefinition/MaxPooling2D.cs

@@ -0,0 +1,12 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.Layers;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class MaxPooling2DArgs : Pooling2DArgs
+    {
+
+    }
+}

src/TensorFlowNET.Core/Keras/ArgsDefinition/Pooling2DArgs.cs

@@ -0,0 +1,34 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class Pooling2DArgs : LayerArgs
+    {
+        /// <summary>
+        /// The pooling function to apply, e.g. `tf.nn.max_pool2d`.
+        /// </summary>
+        public IPoolFunction PoolFunction { get; set; }
+
+        /// <summary>
+        /// specifying the size of the pooling window.
+        /// </summary>
+        public TensorShape PoolSize { get; set; }
+
+        /// <summary>
+        /// specifying the strides of the pooling operation.
+        /// </summary>
+        public TensorShape Strides { get; set; }
+
+        /// <summary>
+        /// The padding method, either 'valid' or 'same'.
+        /// </summary>
+        public string Padding { get; set; } = "valid";
+
+        /// <summary>
+        /// one of `channels_last` (default) or `channels_first`.
+        /// </summary>
+        public string DataFormat { get; set; }
+    }
+}

src/TensorFlowNET.Core/Keras/Engine/Flatten.cs

@@ -0,0 +1,39 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Utils;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.Keras.Engine
+{
+    public class Flatten : Layer
+    {
+        FlattenArgs args;
+        InputSpec input_spec;
+        bool _channels_first;
+
+        public Flatten(FlattenArgs args)
+            : base(args)
+        {
+            args.DataFormat = conv_utils.normalize_data_format(args.DataFormat);
+            input_spec = new InputSpec(min_ndim: 1);
+            _channels_first = args.DataFormat == "channels_first";
+        }
+
+        protected override Tensor call(Tensor inputs, bool is_training = false, Tensor state = null)
+        {
+            if (_channels_first)
+            {
+                throw new NotImplementedException("");
+            }
+
+            if (tf.executing_eagerly())
+            {
+                return array_ops.reshape(inputs, new[] { inputs.shape[0], -1 });
+            }
+
+            throw new NotImplementedException("");
+        }
+    }
+}