ValueError: could not broadcast input array from shape (60,60,2) into shape (1)
我尝试在代码中进行某种方式的修改，但仍然存在相同的错误。
这是我的网络Keras：
input_1 = Input(shape=(60, 60, 2))
input_2 = Input(shape=(self.action_size, self.action_size))
x1 = Conv2D(32, (4, 4), strides=(2, 2), padding='Same', activation=LeakyReLU(alpha=self.Beta))(input_1)
x1 = Conv2D(64, (2, 2), strides=(2, 2), padding='Same', activation=LeakyReLU(alpha=self.Beta))(x1)
x1 = Conv2D(128, (2, 2), strides=(1, 1), padding='Same', activation=LeakyReLU(alpha=self.Beta))(x1)
x1 = Flatten()(x1)
x1 = Dense(128, activation=LeakyReLU(alpha=self.Beta))(x1)
x1_value = Dense(64, activation=LeakyReLU(alpha=self.Beta))(x1)
value = Dense(1, activation=LeakyReLU(alpha=self.Beta))(x1_value)
x1_advantage = Dense(64, activation=LeakyReLU(alpha=self.Beta))(x1)
advantage = Dense(self.action_size, activation=LeakyReLU(alpha=self.Beta))(x1_advantage)
A = Dot(axes=1)([input_2, advantage])
A_subtract = Subtract()([advantage, A])
Q_value = Add()([value, A_subtract])
model = Model(inputs=[input_1, input_2], outputs=[Q_value])
model.compile(optimizer=Adam(lr=self.epsilon_r), loss='mse')
这是我训练的功能：
state = []
target_f_list = []
for s, a, r, next_s, done in minibatch:
if not done:
... do calculate target_f ...
state.append(s) # mark 1
target_f_list.append(target_f) # mark 2
# this is fit function i use before and it's worked fine. But i want to train all minibatch add the same time.
# self.model.fit(s, target_f, epochs=1, verbose=0, batch_size=self.minibatch_size)
# This is my code has error
self.model.train_on_batch(state,target_f_list) # mark 3
感谢您阅读我的问题。