Add generic locally_connected wrapper around locally_connected1d

Author: milancurcic

README.md

@@ -33,7 +33,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
 | Embedding | `embedding` | n/a | 2 | ✅ | ✅ |
 | Dense (fully-connected) | `dense` | `input1d`, `dense`, `dropout`, `flatten` | 1 | ✅ | ✅ |
 | Dropout | `dropout` | `dense`, `flatten`, `input1d` | 1 | ✅ | ✅ |
-| Locally connected (1-d) | `locally_connected1d` | `input2d`, `locally_connected1d`, `conv1d`, `maxpool1d`, `reshape2d` | 2 | ✅ | ✅ |
+| Locally connected (1-d) | `locally_connected` | `input`, `locally_connected`, `conv`, `maxpool`, `reshape` | 2 | ✅ | ✅ |
 | Convolutional (1-d and 2-d) | `conv` | `input`, `conv`, `maxpool`, `reshape` | 2, 3 | ✅ | ✅ |
 | Max-pooling (1-d and 2-d) | `maxpool` | `input`, `conv`, `maxpool`, `reshape` | 2, 3 | ✅ | ✅ |
 | Linear (2-d) | `linear2d` | `input2d`, `layernorm`, `linear2d`, `self_attention` | 2 | ✅ | ✅ |

example/cnn_mnist_1d.f90

@@ -1,7 +1,7 @@
 program cnn_mnist_1d
 
     use nf, only: network, sgd, &
-      input, maxpool, flatten, dense, reshape, locally_connected1d, &
+      input, maxpool, flatten, dense, reshape, locally_connected, &
       load_mnist, label_digits, softmax, relu
 
     implicit none
@@ -21,9 +21,9 @@ program cnn_mnist_1d
     net = network([ &
       input(784), &
       reshape(28, 28), &
-      locally_connected1d(filters=8, kernel_size=3, activation=relu()), &
+      locally_connected(filters=8, kernel_size=3, activation=relu()), &
       maxpool(pool_width=2, stride=2), &
-      locally_connected1d(filters=16, kernel_size=3, activation=relu()), &
+      locally_connected(filters=16, kernel_size=3, activation=relu()), &
       maxpool(pool_width=2, stride=2), &
       dense(10, activation=softmax()) &
     ])

src/nf.f90

@@ -11,7 +11,7 @@ module nf
     input, &
     layernorm, &
     linear2d, &
-    locally_connected1d, &
+    locally_connected, &
     maxpool, &
     reshape, &
     self_attention

src/nf/nf_layer_constructors.f90

@@ -15,7 +15,7 @@ module nf_layer_constructors
     flatten, &
     input, &
     linear2d, &
-    locally_connected1d, &
+    locally_connected, &
     maxpool, &
     reshape, &
     self_attention, &
@@ -154,6 +154,38 @@ end function conv2d
   end interface conv
 
 
+  interface locally_connected
+
+    module function locally_connected1d(filters, kernel_size, activation) result(res)
+      !! 1-d locally connected network constructor
+      !!
+      !! This layer is for building 1-d locally connected network.
+      !! Although the established convention is to call these layers 1-d,
+      !! the shape of the data is actually 2-d: image width,
+      !! and the number of channels.
+      !! A locally connected 1d layer must not be the first layer in the network.
+      !!
+      !! Example:
+      !!
+      !! ```
+      !! use nf, only :: locally_connected1d, layer
+      !! type(layer) :: locally_connected1d_layer
+      !! locally_connected1d_layer = dense(filters=32, kernel_size=3)
+      !! locally_connected1d_layer = dense(filters=32, kernel_size=3, activation='relu')
+      !! ```
+      integer, intent(in) :: filters
+        !! Number of filters in the output of the layer
+      integer, intent(in) :: kernel_size
+        !! Width of the convolution window, commonly 3 or 5
+      class(activation_function), intent(in), optional :: activation
+        !! Activation function (default sigmoid)
+      type(layer) :: res
+        !! Resulting layer instance
+    end function locally_connected1d
+
+  end interface locally_connected
+
+
   interface maxpool
 
     module function maxpool1d(pool_width, stride) result(res)
@@ -290,33 +322,6 @@ module function flatten() result(res)
         !! Resulting layer instance
     end function flatten
 
-    module function locally_connected1d(filters, kernel_size, activation) result(res)
-      !! 1-d locally connected network constructor
-      !!
-      !! This layer is for building 1-d locally connected network.
-      !! Although the established convention is to call these layers 1-d,
-      !! the shape of the data is actually 2-d: image width,
-      !! and the number of channels.
-      !! A locally connected 1d layer must not be the first layer in the network.
-      !!
-      !! Example:
-      !!
-      !! ```
-      !! use nf, only :: locally_connected1d, layer
-      !! type(layer) :: locally_connected1d_layer
-      !! locally_connected1d_layer = dense(filters=32, kernel_size=3)
-      !! locally_connected1d_layer = dense(filters=32, kernel_size=3, activation='relu')
-      !! ```
-      integer, intent(in) :: filters
-        !! Number of filters in the output of the layer
-      integer, intent(in) :: kernel_size
-        !! Width of the convolution window, commonly 3 or 5
-      class(activation_function), intent(in), optional :: activation
-        !! Activation function (default sigmoid)
-      type(layer) :: res
-        !! Resulting layer instance
-    end function locally_connected1d
-
     module function linear2d(out_features) result(res)
       !! Rank-2 (sequence_length, out_features) linear layer constructor.
       !! sequence_length is determined at layer initialization, based on the

test/test_locally_connected1d_layer.f90

@@ -1,7 +1,7 @@
 program test_locally_connected1d_layer
 
     use iso_fortran_env, only: stderr => error_unit
-    use nf, only: locally_connected1d, input, layer
+    use nf, only: locally_connected, input, layer
     use nf_input2d_layer, only: input2d_layer
 
     implicit none
@@ -12,7 +12,7 @@ program test_locally_connected1d_layer
     real, parameter :: tolerance = 1e-7
     logical :: ok = .true.
 
-    locally_connected_1d_layer = locally_connected1d(filters, kernel_size)
+    locally_connected_1d_layer = locally_connected(filters, kernel_size)
 
     if (.not. locally_connected_1d_layer % name == 'locally_connected1d') then
       ok = .false.
@@ -52,7 +52,7 @@ program test_locally_connected1d_layer
     sample_input = 0
 
     input_layer = input(1, 3)
-    locally_connected_1d_layer = locally_connected1d(filters, kernel_size)
+    locally_connected_1d_layer = locally_connected(filters, kernel_size)
     call locally_connected_1d_layer % init(input_layer)
 
     select type(this_layer => input_layer % p); type is(input2d_layer)
@@ -62,7 +62,6 @@ program test_locally_connected1d_layer
     call locally_connected_1d_layer % forward(input_layer)
     call locally_connected_1d_layer % get_output(output)
 
-  
     if (.not. all(abs(output) < tolerance)) then
       ok = .false.
       write(stderr, '(a)') 'locally_connected1d layer with zero input and sigmoid function must forward to all 0.5.. failed'