gossip.py 文件源码

def make_model(input_shape, nb_epochs=100, batch_size=128, lr=0.01, n_layers=1, n_hidden=16, rate_dropout=0.3):
    model_path = 'model.%s' % input_shape[0]
    wp = WindPuller(input_shape=input_shape, lr=lr, n_layers=n_layers, n_hidden=n_hidden, rate_dropout=rate_dropout)
    train_set, test_set = read_ultimate("./", input_shape)
    wp.fit(train_set.images, train_set.labels, batch_size=batch_size,
           nb_epoch=nb_epochs, shuffle=True, verbose=1,
           validation_data=(test_set.images, test_set.labels),
           callbacks=[TensorBoard(histogram_freq=1),
                      ModelCheckpoint(filepath=model_path+'.best', save_best_only=True, mode='min')])
    scores = wp.evaluate(test_set.images, test_set.labels, verbose=0)
    print('Test loss:', scores[0])
    print('Test accuracy:', scores[1])

    wp.model.save(model_path)
    saved_wp = wp.load_model(model_path)
    scores = saved_wp.evaluate(test_set.images, test_set.labels, verbose=0)
    print('Test loss:', scores[0])
    print('test accuracy:', scores[1])
    pred = saved_wp.predict(test_set.images, 1024)
    # print(pred)
    # print(test_set.labels)
    pred = numpy.reshape(pred, [-1])
    result = numpy.array([pred, test_set.labels]).transpose()
    with open('output.' + str(input_shape[0]), 'w') as fp:
        for i in range(result.shape[0]):
            for val in result[i]:
                fp.write(str(val) + "\t")
            fp.write('\n')