深度学习 neural machine translation with attention 错误解析

在这次的 练习中，在 load 过模型参数后，进行 example预测时，报错。

以下是代码部分

EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']
for example in EXAMPLES:


    source = string_to_int(example, Tx, human_vocab)
    source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source))).swapaxes(0,1)
    prediction = model.predict([source, s0, c0])
    prediction = np.argmax(prediction, axis = -1)
    output = [inv_machine_vocab[int(i)] for i in prediction]


    print("source:", example)
    print("output:", ''.join(output))

以下是输出错误：

ValueError                                Traceback (most recent call last)
<ipython-input-31-5f0a9dfb7249> in <module>()
      4     source = string_to_int(example, Tx, human_vocab)
      5     source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source))).swapaxes(0,1)
----> 6     prediction = model.predict([source, s0, c0])
      7     prediction = np.argmax(prediction, axis = -1)
      8     output = [inv_machine_vocab[int(i)] for i in prediction]

E:\Python\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps)
   1815         x = _standardize_input_data(x, self._feed_input_names,
   1816                                     self._feed_input_shapes,
-> 1817                                     check_batch_axis=False)
   1818         if self.stateful:
   1819             if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:

E:\Python\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    111                         ': expected ' + names[i] + ' to have ' +
    112                         str(len(shape)) + ' dimensions, but got array '
--> 113                         'with shape ' + str(data_shape))
    114                 if not check_batch_axis:
    115                     data_shape = data_shape[1:]

ValueError: Error when checking : expected input_1 to have 3 dimensions, but got array with shape (37, 30)

从错误来看 是 数据维度不对，还有就是 

shape (37, 30) 这也不对，应该是 shape(30,37),所以修改后如下（看红色部分）

EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']
for example in EXAMPLES:
    
    source = string_to_int(example, Tx, human_vocab)
    # source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source))).swapaxes(0,1)
    # prediction = model.predict([source, s0, c0])
    source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), source))) #不能变换  数据维度 ，  
    ttt=np.expand_dims(source,axis=0) # 在 axis=0的位置 ，增加一个 维度，以适应 输入维度要求
    prediction = model.predict([ttt, s0, c0])
    prediction = np.argmax(prediction, axis = -1)
    output = [inv_machine_vocab[int(i)] for i in prediction]
    
    print("source:", example)
    print("output:", ''.join(output))