# fit and evaluate a model
def fit_evaluate_model(X_train, y_train, X_test, y_test, n_kernel):
    verbose, epochs, batch_size = 0, 10, 32
    n_timesteps, n_features, n_outputs = X_train.shape[1], X_train.shape[2], y_train.shape[1]
    # create model
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=n_kernel, activation='relu', input_shape=(n_timesteps,n_features)))
    model.add(Conv1D(filters=64, kernel_size=n_kernel, activation='relu'))
    model.add(Dropout(0.5))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(100, activation='relu'))
    model.add(Dense(n_outputs, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    # fit model
    model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=verbose)
    # evaluate model
    _, accuracy = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=verbose)
    return accuracy
 
# summarize performance (accuracy, mean, sd)
def performance_summary(scores, params):
    print(scores, params)
    for score in range(len(scores)):
        m, s = mean(scores[score]), std(scores[score])
        print('Kernel=%d: %.3f%% (+/-%.3f)' % (params[score], m, s))
    # boxplot of scores
    plt.boxplot(scores, labels=params)
    plt.savefig('cnn_filters.png')
 
# run the trials
def run_trials(params, repeats=5):
    # test each parameter
    all_scores = list()
    for param in params:
        scores = list()
        for repeat in range(repeats):
            score = fit_evaluate_model(X_train, y_train, X_test, y_test, param)
            score = score * 100.0
            print('>Kernel=%d #%d: %.3f' % (param, repeat+1, score))
            scores.append(score)
        all_scores.append(scores)
    performance_summary(all_scores, params)
 
# run
n_kernels = [2, 3, 5, 7, 11]
run_trials(n_kernels)