error in azure synapse notebook

Question

synapse notebook, I tuned hyperparameter for neural network model, it gave the following error:

com.fasterxml.jackson.databind.exc.MismatchedInputException: No content to map due to end-of-input at [Source: (String)""; line: 1, column: 0]

My codes are as follows:

# Create function
def nn_cl_fun(neurons, layers): 
    num_features = 20
    dropout_rate = 0.25
    
    nn = Sequential() 
    nn.add(Dense(neurons, input_shape=(num_features,), activation='relu')) 
    
    for i in range(layers): 
        nn.add(Dense(neurons, activation='relu'))
        nn.add(Dropout(dropout_rate, seed=123))         
    
    #output 
    nn.add(Dense(1, activation='sigmoid'))  
    opt = tf.keras.optimizers.Adam(learning_rate=0.01)
    nn.compile(loss='binary_crossentropy', optimizer=opt, metrics=[Recall(),Precision()]) 
    return nn 

nn = KerasClassifier(build_fn=nn_cl_fun,verbose=0) 
params={'batch_size':[64, 128], 
        'epochs':[30, 50],
        'neurons':[128,256],
        'layers':[1,2]        
        }

nn_model=GridSearchCV(estimator=nn, param_grid=params, cv=5, scoring='f1', return_train_score=True, verbose=0, n_jobs=-1)
es = EarlyStopping(monitor='loss', mode='min', verbose=0, patience=5)
results = nn_model.fit(X_train_sm, y_train_sm, callbacks=[es], verbose=0)
# print best hyperparameters
print("Best F1 score: ", nn_model.best_score_)
print("Best hyperparameters: ", nn_model.best_params_)

Answer 1

Hi @uuu zhu

I'm glad that you were able to resolve your issue and thank you for posting your solution so that others experiencing the same thing can easily reference this! Since the Microsoft Q&A community has a policy that "The question author cannot accept their own answer. They can only accept answers by others ", I'll repost your solution in case you'd like to accept the answer .

Issue: synapse notebook, I tuned hyperparameter for neural network model, it gave the following error:

com.fasterxml.jackson.databind.exc.MismatchedInputException: No content to map due to end-of-input at [Source: (String)""; line: 1, column: 0]

PythonAI ConvertCopy

My codes are as follows:

# Create function
def nn_cl_fun(neurons, layers): 
    num_features = 20
    dropout_rate = 0.25
    
    nn = Sequential() 
    nn.add(Dense(neurons, input_shape=(num_features,), activation='relu')) 
    
    for i in range(layers): 
        nn.add(Dense(neurons, activation='relu'))
        nn.add(Dropout(dropout_rate, seed=123))         
    
    #output 
    nn.add(Dense(1, activation='sigmoid'))  
    opt = tf.keras.optimizers.Adam(learning_rate=0.01)
    nn.compile(loss='binary_crossentropy', optimizer=opt, metrics=[Recall(),Precision()]) 
    return nn 

nn = KerasClassifier(build_fn=nn_cl_fun,verbose=0) 
params={'batch_size':[64, 128], 
        'epochs':[30, 50],
        'neurons':[128,256],
        'layers':[1,2]        
        }

nn_model=GridSearchCV(estimator=nn, param_grid=params, cv=5, scoring='f1', return_train_score=True, verbose=0, n_jobs=-1)
es = EarlyStopping(monitor='loss', mode='min', verbose=0, patience=5)
results = nn_model.fit(X_train_sm, y_train_sm, callbacks=[es], verbose=0)
# print best hyperparameters
print("Best F1 score: ", nn_model.best_score_)
print("Best hyperparameters: ", nn_model.best_params_)

Solution: I found the issue: there are some null values in 'label' columns. Thanks a lot.

If I missed anything please let me know and I'd be happy to add it to my answer, or feel free to comment below with any additional information.

I hope this helps!

If you have any other questions, please let me know. Thank you again for your time and patience throughout this issue.

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