'Tensor' object has no attribute 'numpy'

Question

I am working on tensorflow federated (tff). The problem arises when I call the iterative process and pass on the instance of the model created.

I have declared a keras model which is used at both the server and at the client. I basically want to modify the client model's weights and biases in order to perform mathematical functions on them. These mathematical functions are performed on the models' weights and biases by using function named 'processed'. The processed function can return weights and layers both in tf or numpy format (the funtion 'processed' just uses tf.convert_to_tensor command to convert the numpy returned weights/biases from the get/set_weights command used)

def create_keras_model():
   return tf.keras.models.Sequential([
     tf.keras.layers.Conv2D(filters=32, kernel_size=[5, 5],name='conv2d_1',activation=tf.nn.relu, use_bias=True, bias_initializer =tf.initializers.lecun_normal(seed=137), input_shape=(28 ,28 ,1 )),
     tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2),
     tf.keras.layers.Conv2D(filters=32, kernel_size=[5 5],name='conv2d_2',activation=tf.nn.relu, use_bias = True, bias_initializer=tf.initializers.lecun_normal(seed=137)),
     tf.keras.layers.MaxPool2D(pool_size=[2 2], strides=2),
     tf.keras.layers.Reshape(target_shape=(4 * 3 * 3)),
     tf.keras.layers.Dense(units= 50, activation=tf.nn.relu, use_bias=True, bias_initializer=tf.initializers.lecun_normal(seed=137), name='dense_1'),
     tf.keras.layers.Dense(units=10 , use_bias=True, bias_initializer=tf.initializers.lecun_normal(seed=137), activation=tf.nn.softmax, name='dense_2'   ),
    ]) ```

I create an instant of the above Keras model named as net_1 = create_keras_model()

Now I am accessing the weights and biases by using the following function (proccessed) and assigning them new weights and biases after some mathmatical operations

    ```def processed(net_1,L_norm):
          a=net_1.get_layer('conv2d_1').get_weights()[0]
          b=net_1.get_layer('conv2d_1').get_weights()[1]
          c= net_1.get_layer('conv2d_2').get_weights()[0]
          d=net_1.get_layer('conv2d_2').get_weights()[1]
          e= net_1.get_layer('dense_1').get_weights()[0]
          f= net_1.get_layer('dense_1').get_weights()[1]
          g=net_1.get_layer('dense_2').get_weights()[0]
          h= net_1.get_layer('dense_2').get_weights()[1]
          L1,B1,L2,B2,L3,B3,L4,B4 = processing_work(a,b,c,d,e,f,g,h,L_norm)
         
          L1=tf.convert_to_tensor(L1)
          B1=tf.convert_to_tensor(B1)
          L2=tf.convert_to_tensor(L2)
          B2=tf.convert_to_tensor(B2)
          L3=tf.convert_to_tensor(L3)
          B3=tf.convert_to_tensor(B3)
          L4=tf.convert_to_tensor(L4)
          B4=tf.convert_to_tensor(B4)
          net_1.get_layer('conv2d_1').set_weights([L1,B1])
          net_1.get_layer('conv2d_2').set_weights([L2,B2])
          net_1.get_layer('dense_1').set_weights([L3,B3])
          net_1.get_layer('dense_2').set_weights([L4,B4])
          return net_1 ```

Following that, I use the following function

def model_fn_of():
  # We _must_ create a new model here, and _not_ capture it from an external
  # scope. TFF will call this within different graph contexts.
  local_model = processed(create_keras_model(),L_norm)
  return tff.learning.from_keras_model(
      local_model,
      input_spec=preprocessed_example_dataset.element_spec,
      loss=tf.keras.losses.SparseCategoricalCrossentropy(),
      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])   ```

Then I call the iterator of tff.


iterative_process = tff.learning.algorithms.build_weighted_fed_avg(
    model_fn_of,
    client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02),
    server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.00))

However I get the following error:

AttributeError                            Traceback (most recent call last)
 in 
      2     model_fn_of,
      3     client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02),
----> 4     server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.00))

8 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in __getattr__(self, name)
    511         from tensorflow.python.ops.numpy_ops import np_config
    512         np_config.enable_numpy_behavior()""".format(type(self).__name__, name))
--> 513     self.__getattribute__(name)
    514 
    515   @staticmethod

AttributeError: 'Tensor' object has no attribute 'numpy'  ```

Can someone please help me with this. I dont know what is wrong and from which part of the code the error is even popping out. Please note that the dtype of weights/biases of model layers before and after using the 'processed' function are same. Thanks in Advance

'Tensor' object has no attribute 'numpy'

0 Answers