# fit and evaluate a model
def fit_evaluate_model(X_train, y_train, X_test, y_test, n_filters):
    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=n_filters, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
    model.add(Conv1D(filters=n_filters, kernel_size=3, 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'))
    #save an image of the network
    plot_model(model, show_shapes=True, to_file='sequential.png')
    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