How can I intentionally overfit a convolutional neural net in Keras to make sure the model is working?

I'm trying to diagnose what's causing low accuracies when training my model. At this point, I just want to be able to get to high training accuracies (I can worry about testing accuracy/overfitting problems later). How can I adjust the model to overindex on training accuracy? I want to do this to make sure I didn't make any mistakes in a preprocessing step (shuffling, splitting, normalizing, etc.).

#PARAMS
dropout_prob = 0.2
activation_function = 'relu'
loss_function = 'categorical_crossentropy'
verbose_level = 1
convolutional_batches = 32
convolutional_epochs = 5
inp_shape = X_train.shape[1:]
num_classes = 3


def train_convolutional_neural():
    y_train_cat = np_utils.to_categorical(y_train, 3) 
    y_test_cat = np_utils.to_categorical(y_test, 3)

    model = Sequential()
    model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=inp_shape))
    model.add(Conv2D(filters=32, kernel_size=(3, 3)))
    model.add(MaxPooling2D(pool_size = (2,2)))
    model.add(Dropout(rate=dropout_prob))
    model.add(Flatten())
    model.add(Dense(64,activation=activation_function))
    model.add(Dense(num_classes,activation='softmax'))
    model.summary()
    model.compile(loss=loss_function, optimizer="adam", metrics=['accuracy'])
    history = model.fit(X_train, y_train_cat, batch_size=convolutional_batches, epochs = convolutional_epochs, verbose = verbose_level, validation_data=(X_test, y_test_cat))
    model.save('./models/convolutional_model.h5')