使用Generator的TensorFlow中的多输入建模

如何解决使用Generator的TensorFlow中的多输入建模

import numpy as np
import tensorflow as tf

class ProbDistWRTChoices(tf.keras.layers.Layer):
    def __init__(self,maxChoice):
        super().__init__()
        self.maxChoice = maxChoice

    def call(self,inputs):
        utility,rowlengths = inputs
        utility = tf.reshape(utility,-1)
        utility = tf.RaggedTensor.from_row_lengths(values = utility,row_lengths = rowlengths)
        utility = utility.to_tensor(default_value = -1e9,shape = (None,self.maxChoice))
        prob = tf.nn.softmax(utility,axis=-1)
        return prob

    
class MNLogit(tf.keras.Model):

    def __init__(self,maxChoice):
        super(MNLogit,self).__init__()
        self.dense = tf.keras.layers.Dense(1,use_bias = False,kernel_initializer = 'glorot_uniform',activation=None)
        self.probabilityCalculator = ProbDistWRTChoices(maxChoice)

    def call(self,inputs):
        print(inputs)
        x,rowlengths = inputs
        print (x.shape,rowlengths.shape)
        x = self.dense(x)
        x = self.probabilityCalculator([x,rowlengths])
        print (x.shape)
        print(x.numpy())
        print( "++++++++++=========++++++++++")
        return x


model = MNLogit(maxChoice=100)
model.compile(
    optimizer = tf.keras.optimizers.Adam(),loss = tf.keras.losses.SparseCategoricalCrossentropy(),metrics = [
        tf.keras.metrics.SparseTopKCategoricalAccuracy(k = 1,name = 'Accuracy'),tf.keras.metrics.SparseTopKCategoricalAccuracy(k = 5,name = 'Top5_Accuracy'),tf.keras.metrics.SparseTopKCategoricalAccuracy(k = 10,name = 'Top10_Accuracy')
    ]
)


    row_lengths = np.array([100,100,88,68,99,46,87,74,100])
    targets = np.array([ 4,8,53,36,13,31,7,3,91,75,66,86,55,20,1,6,42,97,9,37,16,14,26,57])
    X = np.random.random((3062,10))
    values = [([X,row_lengths],targets) for i in range(100)]
    
    def generator(values):
        """
        Yields the next training batch.
        """
        
        iterator = iter(values)
        while True:
            yield next(iterator)
    
    train_gen = generator(values)
    model.fit(train_gen,epochs=10,steps_per_epoch = 1)
    
    **O/P because of the print statement:**
     
    
    > (<tf.Tensor: shape=(3062,10),dtype=float32,numpy=
    > array([[0.45570728,0.2092263,0.68047154,...,0.7563979,0.5050498
    >,>         0.679467  ],>        [0.78198177,0.39459062,0.1891338,0.34017387,0.93216115,>         0.05743273],>        [0.37135497,0.88671786,0.08154485,0.4763579,0.49207243,>         0.01604719],>        ...,>        [0.99842083,0.41491947,0.17116761,0.27906555,0.10698277,>         0.52499497],>        [0.06340311,0.14407901,0.8654476,0.74813706,0.18045615,>         0.6346456 ],>        [0.54209155,0.22341223,0.5253111,0.86075026,0.79696625,>         0.80810267]],dtype=float32)>,<tf.Tensor: shape=(32,),dtype=int64,numpy= array([100,> 100,>        100,>         87,100])>) (3062,10) (32,) (32,100) [[0.00836133 0.00883811 0.01003097 ... 0.01875334 0.00826117
    > 0.00648314]  [0.01327305 0.01223391 0.00698607 ... 0.00874015 0.01100503 0.01377677]  [0.01167065 0.00867095 0.00506141 ... 0.00904635 0.01058154 0.00611446]  ...  [0.01133993 0.01008244 0.01226319 ... 0.         0.         0.        ]  [0.01300668 0.01112028 0.00956304 ... 0.02183897 0.00758292 0.0092134 ]  [0.00588106 0.00859331 0.01139334 ... 0.01909281 0.00397551
    > 0.01512818]]
    > ++++++++++=========++++++++++ Epoch 1/10 (<tf.Tensor 'IteratorGetNext:0' shape=(None,None) dtype=float32>,<tf.Tensor
    > 'ExpandDims:0' shape=(None,1) dtype=int64>) (None,None) (None,1)
    > --------------------------------------------------------------------------- ValueError

我不明白为什么第二次模型没有从生成器接收任何输入（它应该是基本上生成相同数据的生成器）。

输入是有意随机的。我的发电机有问题吗？我知道这不是一个好习惯，但从逻辑上讲，我认为它应该有助于建立榜样。但是我被困住了。

我特意添加了一些打印声明，以理解和解释目的。

解决方法

我认为您在此行中有一个错误：

X = np.random.random((3062,10))

应该是：

X = np.random.random((32,10))