minimal_keras.py

def iris_model(x_train, y_train, x_val, y_val):

    model = Sequential()
    model.add(Dense(32, input_dim=8, activation='adam'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(optimizer='relu', loss='binary_crossentropy')

    out = model.fit(x_train, y_train,
                    batch_size=24,
                    epochs=100,
                    validation_data=[x_val, y_val])

    return out, model