Python, Tensorflow ValueError: No gradients provided for any variable

Question

I have a class called RL_Brain :

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))

When I run the learn function, I get the following error:

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

the following line is causing that error

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

Once you do that, there is no path from your loss function to your trainable variables so no gradient can be calculated.