没有属性“编译”，我如何修改类，使其工作？

Question

所述neuMF类不是Keras的类，因此它不能提供任何编译方法。 我最好使用keras.Model而不是nn.Blocks 。

不幸的是，我不太明白 nn.Blocks 是什么以及如何在课堂上替换它。 我应该如何列表的相关我的代码，以便它与keras.Model并且可以使用Keras方法？

这是我的代码：

from d2l import mxnet as d2l
from mxnet import autograd, gluon, np, npx
from mxnet.gluon import nn
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers


    class NeuMF(nn.Block):
        def init(self, num_factors, num_users, num_items, nums_hiddens,
                     kwargs):
            super(NeuMF, self).init(kwargs)
            self.P = nn.Embedding(num_users, num_factors)
            self.Q = nn.Embedding(num_items, num_factors)
            self.U = nn.Embedding(num_users, num_factors)
            self.V = nn.Embedding(num_items, num_factors)
            self.mlp = nn.Sequential()
            for num_hiddens in nums_hiddens:
                self.mlp.add(nn.Dense(num_hiddens, activation='relu',
                                      use_bias=True))
            self.prediction_layer = nn.Dense(1, activation='sigmoid', use_bias=False)
    
        def forward(self, user_id, item_id):
            p_mf = self.P(user_id)
            q_mf = self.Q(item_id)
            gmf = p_mf * q_mf
            p_mlp = self.U(user_id)
            q_mlp = self.V(item_id)
            mlp = self.mlp(np.concatenate([p_mlp, q_mlp], axis=1))
            con_res = np.concatenate([gmf, mlp], axis=1)
            return self.prediction_layer(con_res)
    
    
    hidden = [5,5,5]
    
    model = NeuMF(5, num_users, num_items, hidden)
    model.compile(
         #loss=tf.keras.losses.BinaryCrossentropy(),
        loss=tf.keras.losses.MeanSquaredError(),
        optimizer=keras.optimizers.Adam(lr=0.001)
    )

我收到以下错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-21-5979072369bd> in <module>()
      2 
      3 model = NeuMF(5, num_users, num_items, hidden)
----> 4 model.compile(
      5      #loss=tf.keras.losses.BinaryCrossentropy(),
      6     loss=tf.keras.losses.MeanSquaredError(),

AttributeError: 'NeuMF' object has no attribute 'compile'

非常感谢您提前！

编辑：

我将nn替换为layers

class NeuMF(keras.Model):
    def __init__(self, num_factors, num_users, num_items, nums_hiddens,
                 **kwargs):
        super(NeuMF, self).__init__(**kwargs)
        self.P = layers.Embedding(num_users, num_factors)
        self.Q = layers.Embedding(num_items, num_factors)
        self.U = layers.Embedding(num_users, num_factors)
        self.V = layers.Embedding(num_items, num_factors)
        self.mlp = layers.Sequential()
        for num_hiddens in nums_hiddens:
            self.mlp.add(layers.Dense(num_hiddens, activation='relu',
                                  use_bias=True))
        self.prediction_layer = layers.Dense(1, activation='sigmoid', use_bias=False)

    def forward(self, user_id, item_id):
        p_mf = self.P(user_id)
        q_mf = self.Q(item_id)
        gmf = p_mf * q_mf
        p_mlp = self.U(user_id)
        q_mlp = self.V(item_id)
        mlp = self.mlp(np.concatenate([p_mlp, q_mlp], axis=1))
        con_res = np.concatenate([gmf, mlp], axis=1)
        return self.prediction_layer(con_res)

然后我得到了一个新错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-26-7e09b0f80300> in <module>()
      1 hidden = [1,1,1]
      2 
----> 3 model = NeuMF(1, num_users, num_items, hidden)
      4 model.compile(
      5      #loss=tf.keras.losses.BinaryCrossentropy(),

1 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/embeddings.py in __init__(self, input_dim, output_dim, embeddings_initializer, embeddings_regularizer, activity_regularizer, embeddings_constraint, mask_zero, input_length, **kwargs)
    102       else:
    103         kwargs['input_shape'] = (None,)
--> 104     if input_dim <= 0 or output_dim <= 0:
    105       raise ValueError('Both `input_dim` and `output_dim` should be positive, '
    106                        'found input_dim {} and output_dim {}'.format(

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Answer 1

在评论中已经进行了相当多的讨论之后，您的代码仍然存在一些问题，需要您进行澄清：

1. keras.Model子类应该实现__call__方法，而不是forward方法。
2. 你不能只在你的模型中构建像np.concatenate这样的numpy操作，总是使用像tf.keras.layers.Concatenate这样的keras tf.keras.layers.Concatenate 。
3. 正如已经评论过的，您发布的错误很可能来自num_factors 、 num_users 、 num_items不是整数，但我只能在这里猜测，因为您没有向我们提供这些。
4. 另外，我目前只能猜测您要实现的目标，因为从您发布的内容中根本不清楚。

让我们以不同的方式处理这些问题。 以下代码片段运行没有错误，可能是您的一个很好的起点：

import tensorflow as tf

class NeuMF(tf.keras.Model):
    def __init__(self, num_factors, num_users, num_items, nums_hiddens,
                 **kwargs):
        super(NeuMF, self).__init__(**kwargs)
        self.P = tf.keras.layers.Embedding(num_users, num_factors)
        self.Q = tf.keras.layers.Embedding(num_items, num_factors)
        self.U = tf.keras.layers.Embedding(num_users, num_factors)
        self.V = tf.keras.layers.Embedding(num_items, num_factors)
        self.mlp = tf.keras.Sequential()
        for num_hiddens in nums_hiddens:
            self.mlp.add(
                tf.keras.layers.Dense(
                    num_hiddens,
                    activation='relu',
                    use_bias=True
                    )
                )
        self.prediction_layer = tf.keras.layers.Dense(1, activation='sigmoid', use_bias=False)

    def __call__(self, inputs):
        x  = self.P(inputs[0])
        x1 = self.Q(inputs[1])
        x  = tf.keras.layers.Multiply()([x,x1])

        y = self.U(inputs[0])
        y1 = self.V(inputs[1])
        y = tf.keras.layers.Concatenate()([y,y1])
        y = self.mlp(y)
        x = tf.keras.layers.Concatenate()([x,y])
        return self.prediction_layer(x)

if __name__ == '__main__':
    #replace these with values of your choice:
    num_factors = 2
    num_users   = 3
    num_items   = 4 
    nums_hidden = [5,5,5]

    model = NeuMF(num_users, num_items, num_items, nums_hidden)
    model.compile(
        loss = tf.keras.losses.MeanSquaredError(),
        optimizer = tf.keras.optimizers.Adam(lr=0.001)
        )