小编dyh*_*hia的帖子

我创建了这个模型

num_items = 1250
num_users = 1453
emb_size = 64

input_userID = Input(shape=[1], name='user_ID')
input_itemID = Input(shape=[1], name='item_ID')

user_emb_GMF = Embedding(num_users, emb_size, name='user_emb_GMF')(input_userID)
item_emb_GMF = Embedding(num_items, emb_size, name='item_emb_GMF')(input_itemID)

interraction_map = tf.expand_dims(Dot(axes=1)([user_emb_GMF,item_emb_GMF]), -1)
print(interraction_map)
conv = Conv2D(32, 2, strides=2, activation='relu', padding="SAME", input_shape=interraction_map.shape[1:], name='conv1')(interraction_map)

for i in range(2,7):#les autres conv layer
    conv = Conv2D(32, 2, strides=2, activation='relu', padding="SAME",name='conv%d'%(i))(conv)

reshaped_conv = Flatten()(conv)
# c'est la que je doit agir et ajouter creer la prédiction


out = Dense(1, name='output' )(reshaped_conv)

#out = Dense(1,activation='sigmoid',name='output')(layer)


oncf_model …