dnn.py 文件源码

def predict_regress(model_path):
    #redefine model
    target_var = T.fmatrix('y')
    target_labels = T.switch(T.gt(target_var, 0), 1, 0)
    dnn_strategy = model_path.split('/')[-1].split('_')[0]
    network = get_model_by_strategy(dnn_strategy)

    #load params
    params = []
    with open(model_path, 'r') as f:
        lines = f.readlines()
        for line in lines:
            params.append(np.array(json.loads(line)))
    set_all_param_values(network, params)

    predict_prediction = get_output(network, deterministic=True)
    predict_labels = T.switch(T.gt(predict_prediction, 0), 1, 0)
    predict_acc = binary_accuracy(predict_labels, target_labels, threshold=0).mean()

    input_layer = get_all_layers(network)[0]
    predict = theano.function([input_layer.input_var, target_var],[predict_prediction, predict_acc])

    X, y, labels, values, _, _, _, _, _, _ = load_dataset('../../data/test')
    predict_prediction, predict_acc = predict(X, y)

    sys.stdout.write("  predict accuracy:\t\t\t{} %\n".format(predict_acc * 100))

    #output predict result
    with open('../../data/prediction', 'w') as f:
        for ix in xrange(len(labels)):
            line = str(labels[ix]) + '\t' + str(values[ix]) + '\t' + str(predict_prediction[ix][0]) + '\n'
            f.write(line)
    sys.stdout.flush()