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)
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))