Question

我已经写出了一个简单的TFRECORDS文件，其中包含三个功能和一个标签。当我在学习教程时，似乎要使用这些TFRECORDS，我需要创建一个数据集，解析示例，并通过map（）进行标准化等其他操作。如果这不是正确的工作流程，我将不胜感激！

    dataset = tf.data.TFRecordDataset("dataset.tfrecords")
    
    #parse the protobuffer
    
    def _parse_function(proto):
        # define your tfrecord again. 
        keys_to_features = {'weight_pounds': tf.io.FixedLenFeature([], tf.float32),
                            'gestation_weeks': tf.io.FixedLenFeature([], tf.float32),
                            'plurality': tf.io.FixedLenFeature([], tf.float32),
                            'isMale': tf.io.FixedLenFeature([], tf.float32),
                          
                           
                           }
        
        # Load one example
        parsed_features = tf.io.parse_example(proto, keys_to_features)
        
        # Turn your saved image string into an array
        #parsed_features['image'] = tf.decode_raw(
        #    parsed_features['image'], tf.uint8)
        
        return parsed_features
    
    hold_meanstd={
        'weight_pounds':[7.234738,1.330294],
        'gestation_weeks':[38.346464,4.153269],
        'plurality':[1.035285,0.196870]
    }
    
    def normalize(example):
        example['weight_pounds']=(example['weight_pounds']-hold_meanstd['weight_pounds'][0])/hold_meanstd['weight_pounds'][1]
        example['gestation_weeks']=(example['gestation_weeks']-hold_meanstd['gestation_weeks'][0])/hold_meanstd['gestation_weeks'][1]
        example['plurality']=(example['plurality']-hold_meanstd['plurality'][0])/hold_meanstd['plurality'][1]
        label=example.pop('isMale')
        return(example,label)

dataset = tf.data.TFRecordDataset(["dataset.tfrecords"]).map(_parse_function)
dataset =dataset.map(normalize)
dataset =dataset.batch(64)

然后，一旦有了这个数据集，我就想可以将其引入Keras模型：

Dense = keras.layers.Dense
model = keras.Sequential(
        [
            Dense(500, activation="relu", kernel_initializer='uniform',
                  input_shape=(3,)),
            Dense(200, activation="relu"),
            Dense(100, activation="relu"),
            Dense(25, activation="relu"),
            Dense(1, activation="sigmoid")
        ])

optimizer = keras.optimizers.RMSprop(lr=0.01)

    # Compile Keras model
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=[tf.keras.metrics.AUC()])
    
model.fit(dataset)

这会引发错误：

 ValueError: Layer sequential_1 expects 1 inputs, but it received 3 input tensors. Inputs received: [<tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>, <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=float32>, <tf.Tensor 'ExpandDims_2:0' shape=(None, 1) dtype=float32>]

问题似乎是输入数据集看起来像三个输入而不是一个？如何允许Keras在TF RECORDS数据集上进行训练？

Answer 1

在第一个Dense层中指定input_shape=(3,)后，您的keras模型需要输入形状为(None,3)（其中None定义批处理大小）的Tensor作为输入。我们可以举个例子：

[
[0.0,0.0,0.0]
]

如果我们查看您的tf.data.Dataset，则可以看到它正在返回字典。每个输入将如下所示：

{
"weight_pounds":[0.0],
"gestation_weeks":[0.0],
"plurality":[0.0]
}

与上面指定的input_shape有点不同！

要解决此问题，您有两种解决方法：

删除字典数据结构。为此，您可以稍微更改标准化功能：

def normalize(example):
    example['weight_pounds']=(example['weight_pounds']-hold_meanstd['weight_pounds'][0])/hold_meanstd['weight_pounds'][1]
    example['gestation_weeks']=(example['gestation_weeks']-hold_meanstd['gestation_weeks'][0])/hold_meanstd['gestation_weeks'][1]
    example['plurality']=(example['plurality']-hold_meanstd['plurality'][0])/hold_meanstd['plurality'][1]
    label=example.pop('isMale')
    # removing the dict struct
    data_input = [example['weight_pounds'], example['gestation_weeks'], example['plurality']]
    return(data_input,label)

使用layers.DenseFeatures作为输入层：

from keras.layers import Dense, DenseFeatures
from tensorflow.feature_column import numeric_column

feature_names = ['weight_pounds', 'gestation_weeks', 'plurality']
columns = [numeric_column(header) for header in feature_names]

model = keras.Sequential(
        [
            DenseFeatures(columns)
            Dense(500, activation="relu", kernel_initializer='uniform'),
            Dense(200, activation="relu"),
            Dense(100, activation="relu"),
            Dense(25, activation="relu"),
            Dense(1, activation="sigmoid")
        ])

TF.Keras如何使用TFRECORDS数据集拟合模型？

1 个答案: