added forward prop

cnn.c

@@ -19,9 +19,8 @@ typedef enum {
 } fcpos;
 
 typedef enum {
-	sigmoid,
-	relu,
-	softmax,
+	a_sigmoid,
+	a_softmax,
 } activation;
 
 typedef struct {
@@ -68,6 +67,30 @@ float glorot_init(int fan_in, int fan_out) {
 	return random * 2 * limit - limit;
 }
 
+float relu(float x) {
+	return x > 0 ? x : 0;
+}
+
+float sigmoid(float x) {
+	return 1 / (1 + exp(-x));
+}
+
+void softmax(float* input, float* output, int size) {
+    float max = input[0];
+    for(int i = 1; i < size; i++) {
+        if(input[i] > max) {
+            max = input[i];
+        }
+    }
+    float sum = 0;
+    for(int i = 0; i < size; i++) {
+        output[i] = exp(input[i] - max);
+        sum += output[i];
+    }
+    for(int i = 0; i < size; i++) {
+        output[i] /= sum;
+    }
+}
 
 Layer* create_input(int height, int width, int channels) {
 	Layer* layer = (Layer*)malloc(sizeof(Layer));
@@ -177,3 +200,140 @@ void free_layer(Layer* layer) {
     	free(layer);
 	}
 }
+
+void conv_forward(Layer* layer, float* input) {
+	int padding = layer->params.conv_params.zero_padding;
+	int stride = layer->params.conv_params.stride;
+	int filter_size = layer->params.conv_params.filter_size;
+	int num_filters =  layer->params.conv_params.num_filters;
+	int input_height = layer->height; // from previous layer
+	int input_width = layer->width;
+	int input_channels = layer->channels;
+	
+	int padded_height = input_height + 2 * padding;
+  int padded_width = input_width + 2 * padding;
+  float* padded_input = (float*) calloc(padded_height * padded_width * input_channels, sizeof(float));
+	
+	for (int c = 0; c < input_channels; c++) {
+    for (int h = 0; h < input_height; h++) {
+      for (int w = 0; w < input_width; w++) {
+      	padded_input[c * padded_height * padded_width + (h + padding) * padded_width + (w + padding)] = input[c * input_height * input_width + h * input_width + w];
+      }
+    }
+  }
+	
+	int output_height = (padded_height - filter_size) / stride + 1;
+  int output_width = (padded_width - filter_size) / stride + 1;
+  int output_size = output_height * output_width * num_filters;
+
+	// for every filter
+  for(int f = 0; f < num_filters; f++) {
+  	// for height and width
+    for(int oh = 0; oh < output_height; oh++) {
+    	for(int ow = 0; ow < output_width; ow++) {
+        float sum = 0;
+        // for each "channel (feature maps coming in)", and filter size.
+        for(int c = 0; c < input_channels; c++) {
+        	for(int fh = 0; fh < filter_size; fh++) {
+          	for(int fw = 0; fw < filter_size; fw++) {
+              int ph = oh * stride + fh;
+              int pw = ow * stride + fw;
+              sum += padded_input[c * padded_height * padded_width + ph * padded_width + pw] * layer->params.conv_params.weights[f * input_channels * filter_size * filter_size + c * filter_size * filter_size + fh * filter_size + fw];
+            }
+          }
+        }
+        sum += layer->params.conv_params.biases[f];
+        layer->output[f * output_height * output_width + oh * output_width + ow] = relu(sum);
+      }
+    }
+  }
+
+  free(padded_input);
+}
+
+void maxpool_forward(Layer* layer, float* input) {
+	int pool_size = layer->params.pool_params.pool_size;
+  int stride = layer->params.pool_params.stride;
+  // prev layer
+	int input_height = layer->height; 
+	int input_width = layer->width; 
+	int input_channels = layer->channels; 
+	
+	int output_height = (input_height - pool_size) / stride + 1;
+  int output_width = (input_width - pool_size) / stride + 1;
+  int output_size = output_height * output_width * input_channels;
+
+	for(int c = 0; c < input_channels; c++) {
+    for(int oh = 0; oh < output_height; oh++) {
+      for(int ow = 0; ow < output_width; ow++) {
+      float max_val = -INFINITY;
+      	for(int ph = 0; ph < pool_size; ph++) {
+        	for(int pw = 0; pw < pool_size; pw++) {
+          	int ih = oh * stride + ph;
+            int iw = ow * stride + pw;
+            float val = input[c * input_height * input_width + ih * input_width + iw];
+            if(val > max_val) {
+            	max_val = val;
+            }
+          }
+        }
+      layer->output[c * output_height * output_width + oh * output_width + ow] = max_val;
+      }
+    }
+  }
+}
+
+void fc_forward(Layer* layer, float* input) {
+	int output_size = layer->params.fc_params.output_size;
+	int input_size = layer->height * layer->width * layer->channels;
+
+	// flatten
+	float* flattened_input = (float*) calloc(input_size, sizeof(float));
+  for(int i = 0; i < input_size; i++) {
+  	flattened_input[i] = input[i];
+  }
+	
+	// matmul (output = bias + (input * weight))
+	float* temp_output = (float*) calloc(output_size, sizeof(float));
+  for(int o = 0; o < output_size; o++) {
+  	float sum = 0;
+    for(int i = 0; i < input_size; i++) {
+    	sum += flattened_input[i] * layer->params.fc_params.weights[o * input_size + i];
+    }
+    sum += layer->params.fc_params.biases[o];
+    temp_output[o] = sum;
+  }
+
+	// apply the correct activation (sigmoid for non output layers, softmax for output)
+	if(layer->params.fc_params.type == a_sigmoid) {
+  	for(int o = 0; o < output_size; o++) {
+    	layer->output[o] = sigmoid(temp_output[o]);
+    }
+  } else if(layer->params.fc_params.type == a_softmax) {
+    softmax(temp_output, layer->output, output_size);
+  }
+
+  free(temp_output);
+  free(flattened_input);
+}
+
+void forward_propagation(Layer* layer, float* input_fc) {
+	switch(layer->type) {
+		case input:
+    	// input to layer->output
+      int input_size = layer->height * layer->width * layer->channels;
+      for(int i = 0; i < input_size; i++) {
+      	layer->output[i] = input_fc[i];
+      }
+      break;
+    case conv:
+      conv_forward(layer, input);
+      break;
+    case max_pool:
+      maxpool_forward(layer, input);
+      break;
+    case fully_connected:
+      fc_forward(layer, input);
+      break;
+    }
+}