我有三个输入，这些输入后来被拼接在一起。其中两个输入的形状是(1,)，但第三个输入本身就是一个列表（每个列表有25个元素）。我试图将这三个输入到一个嵌入层中，但第三个输入（即列表）会产生一个错误：ValueError: total size of new array must be unchanged
def rec(n_users, n_movies, n_factors, min_rating, max_rating): user = Input(shape=(1,)) u = Embedding(n_users, n_factors, embeddings_initializer='he_normal', embeddings_regularizer=l2(1e-6))(user) u = Reshape((n_factors,))(u) movie = Input(shape=(1,)) m = Embedding(n_movies, n_factors, embeddings_initializer='he_normal', embeddings_regularizer=l2(1e-6))(movie) m = Reshape((n_factors,))(m) tags = Input(shape=(25,)) t = Embedding(500, n_factors)(tags) t = Reshape((n_factors,))(t) x = Concatenate()([u, m, t]) x = Dropout(0.05)(x) x = Dense(10, kernel_initializer='he_normal')(x) x = Activation('relu')(x) x = Dropout(0.5)(x) x = Dense(1, kernel_initializer='he_normal')(x) x = Activation('sigmoid')(x) x = Lambda(lambda x: x * (max_rating - min_rating) + min_rating)(x) model = Model(inputs=[user, movie, tags], outputs=x) opt = Adam(lr=0.001) model.compile(loss='mean_squared_error', optimizer=opt) return modelX_train_array看起来像这样：
[array([ 90, 291, 473, ..., 479, 5, 102], dtype=int64), array([1829, 98, 1321, ..., 4601, 748, 4522], dtype=int64), array([[ 0, 0, 0, ..., 401, 201, 100], [ 0, 0, 0, ..., 235, 100, 385], [ 0, 0, 0, ..., 439, 487, 385], ..., [ 0, 0, 0, ..., 471, 235, 100], [ 0, 0, 0, ..., 0, 0, 100], [ 0, 0, 0, ..., 0, 0, 221]], dtype=int64)]回答：
因此，这是我给你的建议。这里是我的解决方案的描述。你可以看到，问题出在问号所在的地方。你需要在这个点上进行一些转换，以便能够拼接并得到[None, 150]的形状。
顺便说一下，请注意我做了一些小的改动（这些改动不应该影响解决方案的正确性）。
- 移除了
l2正则化 - 没有创建
Adam优化器，而是将字符串'adam'传递给优化器参数
import tensorflow.keras.backend as Kdef rec(n_users, n_movies, n_factors, min_rating, max_rating): user = Input(shape=(1,)) u = Embedding(n_users, n_factors, embeddings_initializer='he_normal')(user) u = Reshape((n_factors,))(u) movie = Input(shape=(1,)) m = Embedding(n_movies, n_factors, embeddings_initializer='he_normal')(movie) m = Reshape((n_factors,))(m) tags = Input(shape=(25,)) t = Embedding(500, n_factors)(tags) t = Lambda(lambda x: K.mean(x, axis=1))(t) x = Concatenate()([u, m, t]) x = Dropout(0.05)(x) x = Dense(10, kernel_initializer='he_normal')(x) x = Activation('relu')(x) x = Dropout(0.5)(x) x = Dense(1, kernel_initializer='he_normal')(x) x = Activation('sigmoid')(x) x = Lambda(lambda x: x * (max_rating - min_rating) + min_rating)(x) model = Model(inputs=[user, movie, tags], outputs=x) model.compile(loss='mean_squared_error', optimizer='adam') print(model.summary()) return model