Remove indentation in readme

README.md

@@ -15,38 +15,38 @@ install it with `pip3` with `pip3 install meson`. Next, building Gigagrad is as
 From there, you can run tests such as `./gigagrad-test`.
 
 # Usage
-```
-    // Declare a network
-    gg::nn::Module network;
-
-    // Add an input vector of length 4
-    auto x = network.AddInput(4);
-
-    // Add a weight of length 4
-    auto w = network.AddWeight(4);
-
-    // L1 is now the elementwise difference between w and x
-    auto L1 = w - x;
-
-    // Compile the training context. Currently, we only have a scalar C backend
-    gg::TrainingContext ctx = gg::CompileTrainingGraph<gg::codegen::BackendScalarC>(network, L1);
-
-    // Set input data
-    float x_data[] = { 1.0, 2.0, 3.0, 4.0 };
-    float w_data[] = { -0.1, 0.1, -0.001, 0.0001 };
-    float training_example_data[] = { 0.0, 0.0, 0.0, 0.0 };
-    x.data() = x_data;
-    w.data() = w_data;
-    ctx.training_example = training_example_data;
-
-    // Run twenty iterations of gradient descent, and print the loss!
-    for(int i = 0; i < 20; i++)
-    {
-        ctx.Execute();
-        printf("%.6f\n", ctx.loss[0]);
-    }
-    // Print your learned weight: W = { 0.98, 1.97, 2.96, 3.94 }
-    printf("W = { %.2f, %.2f, %.2f, %.2f }\n", w_data[0], w_data[1], w_data[2], w_data[3]);
+```c++
+// Declare a network
+gg::nn::Module network;
+
+// Add an input vector of length 4
+auto x = network.AddInput(4);
+
+// Add a weight of length 4
+auto w = network.AddWeight(4);
+
+// L1 is now the elementwise difference between w and x
+auto L1 = w - x;
+
+// Compile the training context. Currently, we only have a scalar C backend
+gg::TrainingContext ctx = gg::CompileTrainingGraph<gg::codegen::BackendScalarC>(network, L1);
+
+// Set input data
+float x_data[] = { 1.0, 2.0, 3.0, 4.0 };
+float w_data[] = { -0.1, 0.1, -0.001, 0.0001 };
+float training_example_data[] = { 0.0, 0.0, 0.0, 0.0 };
+x.data() = x_data;
+w.data() = w_data;
+ctx.training_example = training_example_data;
+
+// Run twenty iterations of gradient descent, and print the loss!
+for(int i = 0; i < 20; i++)
+{
+    ctx.Execute();
+    printf("%.6f\n", ctx.loss[0]);
+}
+// Print your learned weight: W = { 0.98, 1.97, 2.96, 3.94 }
+printf("W = { %.2f, %.2f, %.2f, %.2f }\n", w_data[0], w_data[1], w_data[2], w_data[3]);
 ```
 
 # Backends