save dnn model into database

server/analysis/nn_tf.py

@@ -8,6 +8,7 @@ Created on Sep 16, 2019
 @author: Bohan Zhang
 '''
 
+import pickle
 import numpy as np
 import tensorflow as tf
 from tensorflow import keras
@@ -28,7 +29,6 @@ class NeuralNet(object):
 
     def __init__(self,
                  n_input,
-                 weights_file,
                  learning_rate=0.01,
                  debug=False,
                  debug_interval=100,
@@ -36,9 +36,6 @@ class NeuralNet(object):
                  explore_iters=500,
                  noise_scale_begin=0.1,
                  noise_scale_end=0):
-        # absolute path for the model weitghs file
-        # one model for each (project, session)
-        self.weights_file = weights_file
 
         self.history = None
         self.recommend_iters = 0
@@ -58,7 +55,6 @@ class NeuralNet(object):
             layers.Dense(64, activation=tf.nn.relu),
             layers.Dense(1)
         ])
-        self.load_weights()
         self.model.compile(loss='mean_squared_error',
                            optimizer=self.optimizer,
                            metrics=['mean_squared_error', 'mean_absolute_error'])
@@ -66,17 +62,28 @@ class NeuralNet(object):
         self.ops = {}
         self.build_graph()
 
-    def save_weights(self):
-        self.model.save_weights(self.weights_file)
+    def save_weights(self, weights_file):
+        self.model.save_weights(weights_file)
 
-    def load_weights(self):
+    def load_weights(self, weights_file):
         try:
-            self.model.load_weights(self.weights_file)
+            self.model.load_weights(weights_file)
             if self.debug:
                 LOG.info('Neural Network Model weights file exists, load weights from the file')
         except Exception:  # pylint: disable=broad-except
             LOG.info('Weights file does not match neural network model, train model from scratch')
 
+    def get_weights_bin(self):
+        return pickle.dumps(self.model.get_weights())
+
+    def set_weights_bin(self, weights):
+        try:
+            self.model.set_weights(pickle.loads(weights))
+            if self.debug:
+                LOG.info('Neural Network Model weights exists, load the existing weights')
+        except Exception:  # pylint: disable=broad-except
+            LOG.info('Weights does not match neural network model, train model from scratch')
+
     # Build same neural network as self.model, But input X is variables,
     # weights are placedholders. Find optimial X using gradient descent.
     def build_graph(self):
@@ -109,8 +116,6 @@ class NeuralNet(object):
     def fit(self, X_train, y_train, fit_epochs=500):
         self.history = self.model.fit(
             X_train, y_train, epochs=fit_epochs, verbose=0)
-        # save model weights
-        self.save_weights()
         if self.debug:
             mse = self.history.history['mean_squared_error']
             i = 0