Question

我使用 tensorflow_recommenders 来测试两个塔模型，但收到以下警告：

<块引用>

tensorflow:Model 是用形状 (None, None) 构建的，用于输入 KerasTensor(type_spec=TensorSpec(shape=(None, None), dtype=tf.float32, name='item_embed_input'), name='item_embed_input', description="created by layer 'item_embed_input'")，但它被称为在形状不兼容的输入上 (None,)

源代码：

import numpy as np
import tensorflow as tf
import tensorflow_recommenders as tfrs

n_unique_users = 100
n_unique_items = 50
n_records = 1000

def test_twotower(ratings, items):
    model = TwoTowerTensorFlowModel(n_unique_users, n_unique_items, items.batch(128))
    model.compile(optimizer=tf.keras.optimizers.Adagrad(0.1))

    train_size = int(n_records * 0.8)
    train = ratings.take(train_size).shuffle(n_records).batch(100)
    test = ratings.skip(train_size).take(n_records - train_size).shuffle(n_records).batch(50)
    model.fit(train, epochs=3)

class TwoTowerTensorFlowModel(tfrs.Model):
    def __init__(self, n_unique_users, n_unique_items, items):
        super().__init__()
        embedding_dim = 32
        print("unique size: user {}, item {}".format(n_unique_users, n_unique_items))

        self.user_model = tf.keras.Sequential([tf.keras.layers.Embedding(n_unique_users, embedding_dim, name="user_embed")])
        self.item_model = tf.keras.Sequential([tf.keras.layers.Embedding(n_unique_items, embedding_dim, name="item_embed")])

        self.task = tfrs.tasks.Retrieval(
            metrics=tfrs.metrics.FactorizedTopK(
                candidates=items.map(self.item_model)
            )
        )

    def compute_loss(self, features, training=False):
        user_embeddings = self.user_model(features[0])
        item_embeddings = self.item_model(features[1])

        return self.task(user_embeddings, item_embeddings)

if '__main__' == __name__:
    rating_users = np.random.randint(n_unique_users, size=(n_records,))
    rating_items = np.random.randint(n_unique_items, size=(n_records,))
    item_ids = np.arange(0, n_unique_items)

    ratings = tf.data.Dataset.from_tensor_slices((rating_users, rating_items))
    items = tf.data.Dataset.from_tensor_slices(item_ids)

    # two tower model
    test_twotower(ratings, items)

参数'features'被发送到TwoTowerTensorFlowModel.comput_loss。

features[0] 具有形状 (None,)，但嵌入层需要 (None, None)。

第一个 None 表示批量大小，但我在设置训练数据集时已经完成了批量操作。那么如何解决问题呢？

Tensorflow：在形状不兼容的输入上调用模型

0 个答案: