Add --reset_learning_rate option to lstmtraining (tesseract-ocr#3470)

When the --reset_learning_rate option is specified,
it resets the learning rate stored in each layer of the network
loaded with --continue_from to the value specified by the --learning_rate option.
If checkpoint is available, it does nothing.

src/lstm/lstmrecognizer.h

@@ -157,6 +157,26 @@ class TESS_API LSTMRecognizer {
     series->ScaleLayerLearningRate(&id[1], factor);
   }
 
+  // Set the all the learning rate(s) to the given value.
+  void SetLearningRate(float learning_rate)
+  {
+    ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES);
+    learning_rate_ = learning_rate;
+    if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) {
+      for (auto &id : EnumerateLayers()) {
+        SetLayerLearningRate(id, learning_rate);
+      }
+    }
+  }
+  // Set the learning rate of the layer with id, by the given value.
+  void SetLayerLearningRate(const std::string &id, float learning_rate)
+  {
+    ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES);
+    ASSERT_HOST(id.length() > 1 && id[0] == ':');
+    auto *series = static_cast<Series *>(network_);
+    series->SetLayerLearningRate(&id[1], learning_rate);
+  }
+
   // Converts the network to int if not already.
   void ConvertToInt() {
     if ((training_flags_ & TF_INT_MODE) == 0) {

src/lstm/plumbing.h

@@ -120,6 +120,14 @@ class Plumbing : public Network {
     ASSERT_HOST(lr_ptr != nullptr);
     *lr_ptr *= factor;
   }
+
+  // Set the learning rate for a specific layer of the stack to the given value.
+  void SetLayerLearningRate(const char *id, float learning_rate) {
+    float *lr_ptr = LayerLearningRatePtr(id);
+    ASSERT_HOST(lr_ptr != nullptr);
+    *lr_ptr = learning_rate;
+  }
+
   // Returns a pointer to the learning rate for the given layer id.
   TESS_API
   float *LayerLearningRatePtr(const char *id);

src/training/lstmtraining.cpp

@@ -36,6 +36,8 @@ static INT_PARAM_FLAG(perfect_sample_delay, 0, "How many imperfect samples betwe
 static DOUBLE_PARAM_FLAG(target_error_rate, 0.01, "Final error rate in percent.");
 static DOUBLE_PARAM_FLAG(weight_range, 0.1, "Range of initial random weights.");
 static DOUBLE_PARAM_FLAG(learning_rate, 10.0e-4, "Weight factor for new deltas.");
+static BOOL_PARAM_FLAG(reset_learning_rate, false,
+                       "Resets all stored learning rates to the value specified by --learning_rate.");
 static DOUBLE_PARAM_FLAG(momentum, 0.5, "Decay factor for repeating deltas.");
 static DOUBLE_PARAM_FLAG(adam_beta, 0.999, "Decay factor for repeating deltas.");
 static INT_PARAM_FLAG(max_image_MB, 6000, "Max memory to use for images.");
@@ -157,6 +159,10 @@ int main(int argc, char **argv) {
         return EXIT_FAILURE;
       }
       tprintf("Continuing from %s\n", FLAGS_continue_from.c_str());
+      if (FLAGS_reset_learning_rate) {
+        trainer.SetLearningRate(FLAGS_learning_rate);
+        tprintf("Set learning rate to %f\n", static_cast<float>(FLAGS_learning_rate));
+      }
       trainer.InitIterations();
     }
     if (FLAGS_continue_from.empty() || FLAGS_append_index >= 0) {