implementationLog 2.85 KB
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server_weights
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<float32[784,10],float32[10]>@SERVER
mean_client_wieghts
<float32[784,10],float32[10]>@SERVER
## Starting...
## Preprocessing the federated_train_data
## Declaring the model
## Declaring the federated algorithm
tf_dataset_type
<x=float32[?,784],y=int32[?,1]>*
model_weights_type
<float32[784,10],float32[10]>
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server_state type
<class 'list'>
<class 'numpy.ndarray'>
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FINISHEEED
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server_state[1]
[ 5.4024562e-04 -1.6237081e-03  6.2275940e-04  1.4378619e-05
 -3.4344319e-04  4.4040685e-04 -6.7906491e-05 -3.0773325e-04
  1.3574951e-04  5.8925571e-04]
server2_state[1]
[ 5.8093132e-04 -2.8670396e-05  1.1061553e-04 -1.5197636e-04
 -4.6668845e-04  2.7149473e-04 -1.8408171e-04  5.8942172e-05
 -3.8304061e-04  1.9247324e-04]
merged_state[1]
[ 5.6058844e-04 -8.2618924e-04  3.6668745e-04 -6.8798872e-05
 -4.0506583e-04  3.5595079e-04 -1.2599411e-04 -1.2439553e-04
 -1.2364556e-04  3.9086447e-04]
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## Evaluation of the model
server_weights
<float32[784,10],float32[10]>@SERVER
mean_client_wieghts
<float32[784,10],float32[10]>@SERVER
## Starting...
## Preprocessing the federated_train_data
## Declaring the model
## Declaring the federated algorithm
tf_dataset_type
<x=float32[?,784],y=int32[?,1]>*
model_weights_type
<float32[784,10],float32[10]>
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server_state type
<class 'list'>
<class 'numpy.ndarray'>
45
FINISHEEED
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server_state[1]
[ 5.40245732e-04 -1.62370806e-03  6.22759399e-04  1.43785965e-05
 -3.43443331e-04  4.40406700e-04 -6.79065852e-05 -3.07733397e-04
  1.35749448e-04  5.89255709e-04]
server2_state[1]
[ 5.8093149e-04 -2.8670451e-05  1.1061558e-04 -1.5197645e-04
 -4.6668845e-04  2.7149462e-04 -1.8408173e-04  5.8942172e-05
 -3.8304055e-04  1.9247322e-04]
merged_state[1]
[ 5.6058861e-04 -8.2618924e-04  3.6668748e-04 -6.8798923e-05
 -4.0506589e-04  3.5595067e-04 -1.2599415e-04 -1.2439561e-04
 -1.2364556e-04  3.9086447e-04]
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## Evaluation of the model
server_weights
<float32[784,10],float32[10]>@SERVER
mean_client_wieghts
<float32[784,10],float32[10]>@SERVER
server_weights
<float32[784,10],float32[10]>@SERVER
mean_client_wieghts
<float32[784,10],float32[10]>@SERVER
## Starting...
## Preprocessing the federated_train_data
## Declaring the model
## Declaring the federated algorithm
tf_dataset_type
<x=float32[?,784],y=int32[?,1]>*
model_weights_type
<float32[784,10],float32[10]>
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server_state type
<class 'list'>
<class 'numpy.ndarray'>
FINISHEEED
server_state[1]
[ 5.4024585e-04 -1.6237078e-03  6.2275951e-04  1.4378643e-05
 -3.4344324e-04  4.4040682e-04 -6.7906469e-05 -3.0773322e-04
  1.3574972e-04  5.8925588e-04]
server2_state[1]
[ 5.80931432e-04 -2.86704162e-05  1.10615605e-04 -1.51976506e-04
 -4.66688391e-04  2.71494675e-04 -1.84081669e-04  5.89420633e-05
 -3.83040548e-04  1.92473250e-04]
merged_state[1]
[ 5.6058867e-04 -8.2618912e-04  3.6668757e-04 -6.8798930e-05
 -4.0506583e-04  3.5595073e-04 -1.2599406e-04 -1.2439558e-04
 -1.2364541e-04  3.9086456e-04]
## Evaluation of the model