Train on a complete time series

firmware/firmware.c

@@ -88,15 +88,15 @@ void main()
     uint32_t current_num_neurons;
 
     // Write params
-    uint32_t value_write_counter;
+    uint32_t value_write_counter = 0;
     int new_value;
 
     // Training
     uint32_t new_token;
     uint32_t token_series[MAX_NUM_TOKENS];
-    int token_counter;
-    uint32_t learning_rate;
-    uint32_t decay_rate;
+    int token_counter = 0;
+    uint32_t learning_rate = 0;
+    uint32_t decay_rate = 0;
 
     while(true) {
 
@@ -304,7 +304,7 @@ void main()
 
             case TRAIN_RUN_EPOCHS:
                 uint32_t num_epochs = atoi(numstr);
-                response = run_training(response, num_epochs, learning_rate, decay_rate, token_series[0], token_series[1]);
+                //response = run_training(response, num_epochs, learning_rate, decay_rate, token_series, token_counter);
                 command_mode = START;
                 break;
 

firmware/include/rnn.h

@@ -141,14 +141,14 @@ void reset_network();
  * num_epochs: Amount of epochs
  * learning_rate_zero: initial learning rate
  * decay_rate: the decay rate for gradient decay
- * x: input token
- * y: output token
+ * xp: Pointer to array of values
+ * xn: Amount of values in array
  */
 char* run_training(
     char *msgbuf,
     int num_epochs,
     uint32_t learning_rate_zero,
     uint32_t decay_rate,
-    uint32_t x,
-    uint32_t y
+    uint32_t *xp,
+    uint32_t xn
 );

firmware/rnn.c

@@ -393,32 +393,40 @@ void set_bias_values(int bias)
  * num_epochs: Amount of epochs
  * learning_rate_zero: initial learning rate
  * decay_rate: the decay rate for gradient decay
- * x: input token
- * y: output token
+ * xp: Pointer to array of values
+ * xn: Amount of values in array
  */
 char* run_training(
     char *msgbuf,
     int num_epochs,
     uint32_t learning_rate_zero,
     uint32_t decay_rate,
-    uint32_t x,
-    uint32_t y
+    uint32_t *xp,
+    uint32_t xn
 )
 {
     int last_val;
     uint32_t train_mask;
-
-    for(int epoch=0; epoch<num_epochs;epoch++) {
-        reset_network();
-        last_val = predict_next_token(x);
-        if(last_val==y) {
-            return "SUCCESS";
-            break;
+    uint32_t y;
+
+    for(uint32_t xni=1; xni<xn; xni++) {
+        msgbuf = "FAIL";
+        y = xp[xni];
+        for(int epoch=0; epoch<num_epochs;epoch++) {
+            reset_network();
+            for(int xpi=0; xpi<xni; xpi++) {
+                last_val = predict_next_token(xp[xpi]);
+            }
+            if(last_val==y) {
+                msgbuf = "SUCCESS";
+                break;
+            }
+            train_mask = last_val ^ y;
+            set_alpha(learning_rate_zero/(1+(decay_rate*epoch)));
+            mask_back_propgatation(train_mask);
         }
-        train_mask = last_val ^ y;
-        set_alpha(learning_rate_zero/(1+(decay_rate*epoch)));
-        mask_back_propgatation(train_mask);
     }
-    return "FAIL";
+
+    return msgbuf;
 }
 

src/py/tty3.py

@@ -54,7 +54,6 @@ run_command(server,"DECAY_RATE")
 # α=(1/(1+decayRate×epochNumber))*​α0
 run_command(server,str(decay_rate))
 
-
 run_command(server,"TRAIN")
 run_command(server,"RUN_EPOCHS")
 run_command(server,str(20000))