我正在遵循页面上的示例：Multi-input and multi-output models
该模型用于预测新闻标题将收到多少次转发和点赞。那么main_output正在预测有多少转发，而aux_output正在预测喜欢？
from keras.layers import Input, Embedding, LSTM, Dense
from keras.models import Model
headline_data=[[i for i in range(100)]]
additional_data=[[100,200]]
labels=[1,2]
# Headline input: meant to receive sequences of 100 integers, between 1 and 10000.
# Note that we can name any layer by passing it a "name" argument.
main_input = Input(shape=(100,), dtype='int32', name='main_input')
# This embedding layer will encode the input sequence
# into a sequence of dense 512-dimensional vectors.
x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)
# A LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
lstm_out = LSTM(32)(x)
auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)
auxiliary_input = Input(shape=(5,), name='aux_input')
x = keras.layers.concatenate([lstm_out, auxiliary_input])
# We stack a deep densely-connected network on top
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
# And finally we add the main logistic regression layer
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
# This defines a model with two inputs and two outputs:
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
# We compile the model and assign a weight of 0.2 to the auxiliary loss.
# To specify different loss_weights or loss for each different output,
# you can use a list or a dictionary. Here we pass a single loss as the loss argument,
# so the same loss will be used on all outputs.
# Since our inputs and outputs are named (we passed them a "name" argument), We could also have compiled the model via:
model.compile(optimizer='rmsprop',
loss={'main_output': 'binary_crossentropy', 'aux_output': 'binary_crossentropy'},
loss_weights={'main_output': 1., 'aux_output': 0.2})
# And trained it via:
model.fit({'main_input': headline_data, 'aux_input': additional_data},
{'main_output': labels, 'aux_output': labels},
epochs=50, batch_size=32)
我收到AttributeError错误：“列表”对象没有属性“ ndim”
最佳答案
您的输入/输出必须是NumPy数组，其中第一维是批处理大小。例如：
headline_data = np.random.randint(1, 10000 + 1, size=(32, 100))
additional_data = np.random.randint(1, 10000 + 1, size=(32, 5))
labels = np.random.randint(0, 1 + 1, size=(32, 1))
请注意，这是一个玩具示例，我们正在随机生成输入。