Fix Glorot uniform initialization for convolutional layers (#22)

Author: jekbradbury

Sources/DeepLearning/Initializers.swift

@@ -129,22 +129,27 @@ public extension Tensor where Scalar: TensorFlowFloatingPoint,
                               Scalar.RawSignificand: FixedWidthInteger {
     /// Performs Glorot uniform initialization for the specified shape, creating a tensor by
     /// randomly sampling scalar values from a uniform distribution between `-limit` and `limit`,
-    /// where limit is `sqrt(6 / (fanIn + fanOut))`.
+    /// where limit is `sqrt(6 / (fanIn + fanOut))` and `fanIn`/`fanOut` represent the number of
+    /// input and output features multiplied by the receptive field if present.
     ///
     /// - Parameters:
     ///   - shape: The dimensions of the tensor.
     ///   - generator: Random number generator to use.
     ///
     init<G: RandomNumberGenerator>(glorotUniform shape: TensorShape, generator: inout G) {
-        let fanIn = shape[shape.count - 2]
-        let fanOut = shape[shape.count - 1]
+        let spatialDimCount = shape.count - 2
+        let receptiveField = shape[0..<spatialDimCount].contiguousSize
+        let fanIn = shape[shape.count - 2] * receptiveField
+        let fanOut = shape[shape.count - 1] * receptiveField
         let minusOneToOne = 2 * Tensor(randomUniform: shape, generator: &generator) - 1
         self = sqrt(Scalar(6) / Scalar(fanIn + fanOut)) * minusOneToOne
     }
 
     /// Creates a tensor by performing Glorot uniform initialization for the specified shape,
     /// randomly sampling scalar values from a uniform distribution between `-limit` and `limit`,
-    /// where limit is `sqrt(6 / (fanIn + fanOut))`, using the default random number generator.
+    /// generated by the default random number generator, where limit is
+    /// `sqrt(6 / (fanIn + fanOut))` and `fanIn`/`fanOut` represent the number of input and output
+    /// features multiplied by the receptive field if present.
     ///
     /// - Parameters:
     ///   - shape: The dimensions of the tensor.