06情感分析代码出现维度错误 about book HOT 6 CLOSED

exe = fluid.Executor(place)

inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
    # Use fluid.io.load_inference_model to obtain the inference program desc,
    # the feed_target_names (the names of variables that will be feeded
    # data using feed operators), and the fetch_targets (variables that
    # we want to obtain data from using fetch operators).
    [inferencer, feed_target_names,
     fetch_targets] = fluid.io.load_inference_model(params_dirname, exe)

    # Setup input by creating LoDTensor to represent sequence of words.
    # Here each word is the basic element of the LoDTensor and the shape of
    # each word (base_shape) should be [1] since it is simply an index to
    # look up for the corresponding word vector.
    # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
    # which has only one lod level. Then the created LoDTensor will have only
    # one higher level structure (sequence of words, or sentence) than the basic
    # element (word). Hence the LoDTensor will hold data for three sentences of
    # length 3, 4 and 2, respectively.
    # Note that lod info should be a list of lists.
    reviews_str = [
        'read the book forget the movie', 'this is a great movie',
        'this is very bad'
    ]
    reviews = [c.split() for c in reviews_str]

    UNK = word_dict['<unk>']
    lod = []
    for c in reviews:
        lod.append([np.int64(word_dict.get(words, UNK)) for words in c])

    base_shape = [[len(c) for c in lod]]

    tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
    assert feed_target_names[0] == "words"
    results = exe.run(
        inferencer,
        feed={feed_target_names[0]: tensor_words},
        fetch_list=fetch_targets,
        return_numpy=False)
    np_data = np.array(results[0])
    for i, r in enumerate(np_data):
        print("Predict probability of ", r[0], " to be positive and ", r[1],
              " to be negative for review \'", reviews_str[i], "\'")