Applications skeleton methods added

src/TensorFlowNET.Keras/Applications/Densenet.cs

@@ -15,5 +15,21 @@ namespace Tensorflow.Keras.Applications
         public static Model DenseNet(int blocks, bool include_top=true, string weights = "imagenet",
                                     Tensor input_tensor = null, TensorShape input_shape = null, 
                                     string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model DenseNet121(int blocks, bool include_top = true, string weights = "imagenet",
+                                    Tensor input_tensor = null, TensorShape input_shape = null,
+                                    string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model DenseNet169(int blocks, bool include_top = true, string weights = "imagenet",
+                                   Tensor input_tensor = null, TensorShape input_shape = null,
+                                   string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model DenseNet201(int blocks, bool include_top = true, string weights = "imagenet",
+                                   Tensor input_tensor = null, TensorShape input_shape = null,
+                                   string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/Efficientnet.cs

@@ -4,7 +4,57 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class Efficientnet
+    public class BlockArg
     {
+
+    }
+
+    public class Efficientnet
+    {
+        public static Model EfficientNet(float width_coefficient, float depth_coefficient, int default_size, float dropout_rate = 0.2f,
+                                        float drop_connect_rate = 0.2f, int depth_divisor = 8, string activation = "swish",
+                                        BlockArg[] blocks_args = null, string model_name = "efficientnet", bool include_top = true,
+                                        string weights = "imagenet", Tensor input_tensor = null, TensorShape input_shape = null, 
+                                        string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor block(Tensor inputs, string activation= "swish", float drop_rate= 0f,string name= "",
+                                    int filters_in= 32, int filters_out= 16, int kernel_size= 3, int strides= 1,
+                                    int expand_ratio= 1, float se_ratio= 0, bool  id_skip= true) => throw new NotImplementedException();
+
+        public static Model EfficientNetB0(bool include_top = true, string weights = "imagenet",
+                                    Tensor input_tensor = null, TensorShape input_shape = null,
+                                    string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model EfficientNetB1(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model EfficientNetB2(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model EfficientNetB3(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model EfficientNetB4(bool include_top = true, string weights = "imagenet",
+                                 Tensor input_tensor = null, TensorShape input_shape = null,
+                                 string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model EfficientNetB5(bool include_top = true, string weights = "imagenet",
+                                 Tensor input_tensor = null, TensorShape input_shape = null,
+                                 string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model EfficientNetB6(bool include_top = true, string weights = "imagenet",
+                                 Tensor input_tensor = null, TensorShape input_shape = null,
+                                 string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model EfficientNetB7(bool include_top = true, string weights = "imagenet",
+                                 Tensor input_tensor = null, TensorShape input_shape = null,
+                                 string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/ImagenetUtils.cs

@@ -4,7 +4,19 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class ImagenetUtils
+    public class ImagenetUtils
     {
+        public static Tensor preprocess_input(Tensor x, string data_format= null, string mode= "caffe") => throw new NotImplementedException();
+        
+        public static Tensor decode_predictions(Tensor preds, int top= 5) => throw new NotImplementedException();
+
+        public static Tensor _preprocess_numpy_input(Tensor x, string data_format, string mode) => throw new NotImplementedException();
+
+        public static Tensor _preprocess_symbolic_input(Tensor x, string data_format, string mode) => throw new NotImplementedException();
+
+        public static TensorShape obtain_input_shape(TensorShape input_shape, int default_size, int min_size,
+                       string data_format, bool require_flatten, string weights= null) => throw new NotImplementedException();
+
+        public static ((int, int), (int, int)) correct_pad(Tensor inputs, (int, int) kernel_size) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/InceptionResnetV2.cs

@@ -4,7 +4,19 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class InceptionResnetV2
+    public class InceptionResnetV2
     {
+        public static Model InceptionResNetV2(bool include_top = true, string weights = "imagenet",
+                                   Tensor input_tensor = null, TensorShape input_shape = null,
+                                   string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor conv2d_bn(Tensor x, int filters, (int, int) kernel_size, (int, int) strides, string padding= "same",
+                                        string activation= "relu", bool use_bias= false, string name= null) => throw new NotImplementedException();
+
+        public static Tensor inception_resnet_block(Tensor x, float scale, string block_type, int block_idx, string activation= "relu") => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/InceptionV3.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class InceptionV3
+    public class InceptionV3
     {
+        public static Model Inceptionv3(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor conv2d_bn(Tensor x, int filters, int num_row, int num_col, string padding = "same", (int, int)? strides = null, string name = null) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/Mobilenet.cs

@@ -4,7 +4,15 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class Mobilenet
+    public class Mobilenet
     {
+        public static Model MobileNet(TensorShape input_shape= null, float alpha= 1.0f, int depth_multiplier= 1, float dropout= 1e-3f,
+                                    bool include_top= true, string weights= "imagenet", Tensor input_tensor= null, string pooling= null, int classes= 1000) => throw new NotImplementedException();
+
+        public static Tensor conv2d_bn(Tensor x, int filters, float alpha, (int, int)? kernel = null, (int, int)? strides = null) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/MobilenetV2.cs

@@ -4,7 +4,18 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class MobilenetV2
+    public class MobilenetV2
     {
+        public static Model MobileNetV2(TensorShape input_shape = null, float alpha = 1.0f, bool include_top = true, 
+                                        string weights = "imagenet", Tensor input_tensor = null, string pooling = null,
+                                        int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor _inverted_res_block(Tensor inputs, int expansion, (int, int) stride, float alpha, int filters, string block_id) => throw new NotImplementedException();
+
+        public static Tensor _make_divisible(Tensor v, Tensor divisor, Tensor min_value= null) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/Nasnet.cs

@@ -4,7 +4,28 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class Nasnet
+    public class Nasnet
     {
+        public static Model NASNet(TensorShape input_shape = null, int penultimate_filters = 4032, int num_blocks = 6, int stem_block_filters = 96,
+                                bool skip_reduction = true, int filter_multiplier = 2, bool include_top = true, string weights = null,
+                                Tensor input_tensor = null, string pooling = null, int classes = 1000, int? default_size = null) => throw new NotImplementedException();
+
+        public static Model NASNetMobile(TensorShape input_shape = null, bool include_top = true, string weights = "imagenet",
+                                    Tensor input_tensor = null, string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model NASNetLarge(TensorShape input_shape = null, bool include_top = true, string weights = "imagenet",
+                                    Tensor input_tensor = null, string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor _separable_conv_block(Tensor ip, int filters, (int, int)? kernel_size= null, (int, int)? strides= null, string block_id= null) => throw new NotImplementedException();
+
+        public static Tensor _adjust_block(Tensor p, Tensor ip, int filters, string block_id= null) => throw new NotImplementedException();
+
+        public static Tensor _normal_a_cell(Tensor p, Tensor ip, int filters, string block_id = null) => throw new NotImplementedException();
+
+        public static Tensor _reduction_a_cell(Tensor p, Tensor ip, int filters, string block_id = null) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/Resnet.cs

@@ -4,7 +4,38 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class Resnet
+    public class Resnet
     {
+        public static Model ResNet(Func<Tensor, Tensor> stack_fn, bool preact, bool use_bias, string model_name= "resnet", bool include_top= true,
+                                    string weights= "imagenet", Tensor input_tensor= null, TensorShape input_shape= null, string pooling= null,
+                                    int classes= 1000) => throw new NotImplementedException();
+
+        public static Tensor block1(Tensor x, int filters, int kernel_size= 3, int stride= 1, bool conv_shortcut= true, string name= null) => throw new NotImplementedException();
+
+        public static Tensor stack1(Tensor x, int filters, int blocks, int stride1 = 2, string name = null) => throw new NotImplementedException();
+
+        public static Tensor block2(Tensor x, int filters, int kernel_size = 3, int stride = 1, bool conv_shortcut = true, string name = null) => throw new NotImplementedException();
+
+        public static Tensor stack2(Tensor x, int filters, int blocks, int stride1 = 2, string name = null) => throw new NotImplementedException();
+
+        public static Tensor block3(Tensor x, int filters, int kernel_size = 3, int stride = 1, int groups = 32, bool conv_shortcut = true, string name = null) => throw new NotImplementedException();
+
+        public static Tensor stack3(Tensor x, int filters, int blocks, int stride1 = 2, int groups = 32, string name = null) => throw new NotImplementedException();
+
+        public static Model ResNet50(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model ResNet101(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model ResNet152(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/ResnetV2.cs

@@ -4,7 +4,22 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class ResnetV2
+    public class ResnetV2
     {
+        public static Model ResNet50V2(bool include_top = true, string weights = "imagenet",
+                                   Tensor input_tensor = null, TensorShape input_shape = null,
+                                   string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model ResNet101V2(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Model ResNet152V2(bool include_top = true, string weights = "imagenet",
+                                  Tensor input_tensor = null, TensorShape input_shape = null,
+                                  string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/Vgg16.cs

@@ -4,7 +4,14 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class Vgg16
+    public class Vgg16
     {
+        public static Model VGG16(bool include_top = true, string weights = "imagenet",
+                                Tensor input_tensor = null, TensorShape input_shape = null,
+                                string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/Vgg19.cs

@@ -4,7 +4,14 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class Vgg19
+    public class Vgg19
     {
+        public static Model VGG19(bool include_top = true, string weights = "imagenet",
+                                Tensor input_tensor = null, TensorShape input_shape = null,
+                                string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Applications/Xception.cs

@@ -4,7 +4,14 @@ using System.Text;
 
 namespace Tensorflow.Keras.Applications
 {
-    class Xception
+    public class Xception
     {
+        public static Model XCeption(bool include_top = true, string weights = "imagenet",
+                                Tensor input_tensor = null, TensorShape input_shape = null,
+                                string pooling = null, int classes = 1000) => throw new NotImplementedException();
+
+        public static Tensor preprocess_input(Tensor x, string data_format = null) => throw new NotImplementedException();
+
+        public static Tensor decode_predictions(Tensor preds, int top = 5) => throw new NotImplementedException();
     }
 }