Replicate MLPClassifier() of sklearn in keras

Question

I am new to keras. I was attempting an ML problem. About the data:

It has 5 input features, 4 output classes and about 26000 records.

I had first attempted it us

Answer 1

To get a bona fide scikit estimator you can use KerasClassifier from tensorflow.keras.wrappers.scikit_learn. For example:

from sklearn.datasets import make_classification
from tensorflow import keras
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier


X, y = make_classification(
    n_samples=26000, n_features=5, n_classes=4, n_informative=3, random_state=0
)


def build_fn(optimizer):
    model = Sequential()
    model.add(
        Dense(200, input_dim=5, kernel_initializer="he_normal", activation="relu")
    )
    model.add(Dense(100, kernel_initializer="he_normal", activation="relu"))
    model.add(Dense(100, kernel_initializer="he_normal", activation="relu"))
    model.add(Dense(100, kernel_initializer="he_normal", activation="relu"))
    model.add(Dense(4, kernel_initializer="he_normal", activation="softmax"))
    model.compile(
        loss="categorical_crossentropy",
        optimizer=optimizer,
        metrics=[
            keras.metrics.Precision(name="precision"),
            keras.metrics.Recall(name="recall"),
            keras.metrics.AUC(name="auc"),
        ],
    )
    return model


clf = KerasClassifier(build_fn, optimizer="rmsprop", epochs=500, batch_size=300)
clf.fit(X, y)
clf.predict(X)