keras - Applying mlxtend to KerasClassifier leads to ValueError

Question

I'm applying the SFS from mlxtend to a Keras neural net wrapped as in sklearn api. Currently, I'm seemingly stuck at an input-dimension mismatch.

Below is the mlxtend portion of the code, where I tried to ensure the input dimension fits the requirement of the first layer of the network.

# Wrap Keras nn and generating SFS object
skwrapped_model = KerasClassifier(build_fn=make_model,
                                  train_full_input=train_predictor,
                                  epochs=EPOCHS,
                                  batch_size=BATCH_SIZE,
                                  validation_split=1-TRAIN_TEST_SPLIT,
                                  verbose=0)
sffs = SFS(skwrapped_model,
           k_features=(1, train_predictor.shape[1]),
           floating=True,
           clone_estimator=False,
           cv=0,
           n_jobs=1,
           scoring='f1_macro')

# Apply SFS to identify best feature subset
sffs = sffs.fit(train_predictor,
                train_response)

Despite my input having a exactly 45 columns, however, I'm still getting the error ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 45 but received input with shape [None, 1]

Detailed error log suggests my input somehow got converted into a 2-D array of 0s during processing.

My neural network model is define as

def make_model(train_full_input, output_bias=None):
    if output_bias is not None:
        # Incorporate initial guess to speed out convergence
        output_bias = tf.keras.initializers.Constant(output_bias)

    # 1 ReLU layer + 1 Dropout layer + 1 softmax layer for 3-cat classification
    model = keras.Sequential([
        keras.layers.Dense(16,
                           activation='relu',
                           input_shape=((train_full_input.shape[-1]),)),
        keras.layers.Dropout(0.5),
        keras.layers.Dense(3,
                           activation='softmax',
                           bias_initializer=output_bias)
    ])
    
    model.compile(optimizer=keras.optimizers.Adam(lr=1e-3),
                  loss= macro_soft_f1)
    
    return model

EPOCHS = 100
# Use a large batch size ensures that each batch is likely to contain a few
# minority classes from the imbalanced input.
BATCH_SIZE = 2048 

Any helps are appreciated!

question from:https://stackoverflow.com/questions/65950224/applying-mlxtend-to-kerasclassifier-leads-to-valueerror

Answer 1

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