I am trying to impement this code https://github.com/tuhinsharma121/federated-ml/blob/master/notebooks/network-threat-detection-using-federated-learning.ipynb
while creating these pytorch sensor
X = final_df.values
y = df['Target'].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=42)
train_inputs = torch.tensor(X_train,dtype=torch.float).tag("#iot", "#network","#data","#train")
train_labels = torch.tensor(y_train).tag("#iot", "#network","#target","#train")
test_inputs = torch.tensor(X_test,dtype=torch.float).tag("#iot", "#network","#data","#test")
test_labels = torch.tensor(y_test).tag("#iot", "#network","#target","#test")
i got these errors current_tensor = hook_self.torch.native_tensor(*args, **kwargs) TypeError: canβt convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, int64, int32, int16, int8, uint8, and bool. how to resolve this
I tried this code by reading other stack questions
train_inputs = torch.tensor(X_train.astype(np.float32)).tag(β#iotβ, β#networkβ,βdataβ,β#trainβ)
train_labels = torch.tensor(y_train).tag(β#iotβ, β#networkβ,β#targetβ,β#trainβ)
test_inputs = torch.tensor(X_test.astype(np.float32)).tag(β#iotβ, β#networkβ,βdataβ,β#testβ)
test_labels = torch.tensor(y_test).tag(β#iotβ, β#networkβ,β#targetβ,β#testβ
but then it gives error ValueError: could not convert string to float: βTotal Fwd Packetsβ Total Fwd Packets is my first feature name. Any recommendation
I also followed other threads
train_inputs = torch.tensor(X_train.to_numpy()).tag(β#iotβ, β#networkβ,βdataβ,β#trainβ)
but got this errortrain_inputs = torch.tensor(X_train.to_numpy()).tag(β#iotβ, β#networkβ,βdataβ,β#trainβ)
but got this error
train_inputs = torch.tensor(X_train.to_numpy()).tag(β#iotβ, β#networkβ,βdataβ,β#trainβ) AttributeError: βnumpy.ndarrayβ object has no attribute βto_numpyβ