沒有屬性“編譯”，我如何修改類，使其工作？

Question

所述neuMF類不是Keras的類，因此它不能提供任何編譯方法。 我最好使用keras.Model而不是nn.Blocks 。

不幸的是，我不太明白 nn.Blocks 是什么以及如何在課堂上替換它。 我應該如何列表的相關我的代碼，以便它與keras.Model並且可以使用Keras方法？

這是我的代碼：

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)
    )

我收到以下錯誤：

---------------------------------------------------------------------------
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'

非常感謝您提前！

編輯：

我將nn替換為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)

然后我得到了一個新錯誤：

---------------------------------------------------------------------------
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

在評論中已經進行了相當多的討論之后，您的代碼仍然存在一些問題，需要您進行澄清：

1. keras.Model子類應該實現__call__方法，而不是forward方法。
2. 你不能只在你的模型中構建像np.concatenate這樣的numpy操作，總是使用像tf.keras.layers.Concatenate這樣的keras tf.keras.layers.Concatenate 。
3. 正如已經評論過的，您發布的錯誤很可能來自num_factors 、 num_users 、 num_items不是整數，但我只能在這里猜測，因為您沒有向我們提供這些。
4. 另外，我目前只能猜測您要實現的目標，因為從您發布的內容中根本不清楚。

讓我們以不同的方式處理這些問題。 以下代碼片段運行沒有錯誤，可能是您的一個很好的起點：

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)
        )