get_layer_result op addded

ravpy/distributed/compute.py

@@ -38,7 +38,8 @@ def compute_locally_bm(*args, **kwargs):
 def compute_locally(payload, subgraph_id, graph_id):
     try:
         # print("Computing ",payload["operator"])
-        # print('\n\nPAYLOAD: ',payload)
+        # print('\n\nPAYLOAD: ',payload['operator'])
+
 
         values = []
 
@@ -273,6 +274,8 @@ def get_unary_result(value1, params, operator):
         result = forward_pass_flatten(value1, params=params)
     elif operator == "backward_pass_flatten":
         result = backward_pass_flatten(value1, params=params)
+    elif operator == "get_layer_result":
+        result = get_layer_result(value1, params=params)
 
     return result
 

ravpy/distributed/op_functions/dl_ops.py

@@ -593,4 +593,8 @@ def backward_pass_flatten(accum_grad, params=None):#prev_input=None, input_layer
     backward_pass_output = {
         'accum_grad': accum_grad.reshape(prev_shape).tolist()
     }
-    return backward_pass_output
+    return backward_pass_output
+
+def get_layer_result(X,params=None):
+    result=np.array(X['result'])
+    return result.tolist()

ravpy/strings.py

@@ -103,6 +103,7 @@ class Operators(object):
     ONE_HOT_ENCODING = "one_hot_encoding"
     SET_VALUE = "set_value"
     SQUEEZE = "squeeze"
+    GET_LAYER_RESULTS="get_layer_result"
 
     # Machine Learning Ops
     LINEAR_REGRESSION = "linear_regression"
@@ -278,6 +279,7 @@ class TFJSOperators(object):
              'federated_mean': Operators.FEDERATED_MEAN,
              'federated_variance': Operators.FEDERATED_VARIANCE,
              'federated_standard_deviation': Operators.FEDERATED_STANDARD_DEVIATION,
+             'get_layer_result':Operators.GET_LAYER_RESULTS,
              }