Question

我需要在输出中添加天气字符串，我该怎么做？

weather_data_train = list()

for j in range(0,len(weather_sents_train)):
    weather_tokens = weather_sents_train[j].split()
    weather_dict = {}
    for key in weather_tokens:
        weather_dict[key] = True
    weather_data_train.append(weather_dict)

我得到的输出

[{'today': True, 'it': True, 'is': True, 'raining': True}, 
{'looking': True, 'cloudy': True, 'today': True}, 
{'it': True, 'is': True, 'nice': True, 'weather': True}]

我想要得到的输出

[({'today': True, 'it': True, 'is': True, 'raining': True}, 'weather'),
({'looking': True, 'cloudy': True, 'today': True}, 'weather'),
({'it': True, 'is': True, 'nice': True, 'weather': True}, 'weather')]

Answer 1

您可以将dict用第二个import tensorflow as tf # make a converter object from the saved tensorflow file converter = tf.lite.TFLiteConverter.from_frozen_graph('/content/mnist.pb', input_arrays=['main_input'], # input arrays output_arrays=['add_10'] # output arrays as told in upper in my model case it si add_10 ) # tell converter which type of optimization techniques to use converter.optimizations = [tf.lite.Optimize.DEFAULT] # to view the best option for optimization read documentation of tflite about optimization go to this link https://www.tensorflow.org/lite/guide/get_started#4_optimize_your_model_optional # convert the model tf_lite_model = converter.convert() # save the converted model open('eye_state_model_tensorFlowopt.tflite', 'wb').write(tf_lite_model)括在一个元组中。

更改：

'weather'

收件人：

weather_data_train.append(weather_dict)

或者，您可以使用列表理解来重写代码：

weather_data_train.append((weather_dict, 'weather'))

嵌套列表/字典理解如何添加缺失的元素

1 个答案: