Python，Tensorflow ValueError:没有为任何变量提供梯度

Question

我有一个名为RL_Brain:的类

class RL_Brain():
def __init__(self, n_features, n_action, memory_size=10, batch_size=32, gamma=0.9, fi_size=10):
self.n_features = n_features
self.n_actions = n_action
self.encoder = keras.Sequential([
Input((self.n_features,)),
Dense(16, activation='relu', kernel_initializer='glorot_normal', name='encoder_1'),
Dense(16, activation='relu', kernel_initializer='glorot_normal', name='encoder_2'),
Dense(16, activation='relu', kernel_initializer='glorot_normal', name='encoder_3'),
Dense(self.fi_size, activation='softmax', name='fi'),
])
self.decoder = keras.Sequential([
Input((self.fi_size,)),
Dense(16, activation='relu', name='decoder_1', trainable=True),
Dense(16, activation='relu', name='decoder_2', trainable=True),
Dense(16, activation='relu', name='decoder_3', trainable=True),
Dense(self.n_features, activation=None, name='decoder_output', trainable=True)
])
def learn(self, state, r, a, state_):
encoded = tf.one_hot(tf.argmax(self.encoder(state), axis=1), depth=self.fi_size)
encoded_ = tf.one_hot(tf.argmax(self.encoder(state_), axis=1), depth=self.fi_size)
decoded_state = self.decoder(encoded).numpy()
with tf.GradientTape() as tape:
loss1 = mean_squared_error(state, decoded_state)
grads = tape.gradient(loss1, self.decoder.trainable_variables)
self.opt.apply_gradients(zip(grads, self.decoder.trainable_variables))

当我运行learn函数时，我得到以下错误：

File "/Users/wangheng/app/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/utils.py", line 78, in filter_empty_gradients raise ValueError("No gradients provided for any variable: %s." % ...
ValueError: No gradients provided for any variable: ['decoder_1/kernel:0', 'decoder_1/bias:0', 'decoder_2/kernel:0', 'decoder_2/bias:0', 'decoder_3/kernel:0', 'decoder_3/bias:0', 'decoder_output/kernel:0', 'decoder_output/bias:0'].

Answer 1

下一行导致错误

decoded_state = self.decoder(encoded).numpy()

一旦你这样做，就没有从损失函数到可训练变量的路径，因此无法计算梯度。