TensorFlow 自定义损失 ValueError：“没有为任何变量提供渐变：[....'Layers'....]”

Question

我正在尝试使用内置的 TensorFlow 函数作为损失函数来编译我的模型。是因为从 numpy 数组变为张量，还是与函数有关。 我的代码：

import numpy as np
from pandas import read_csv
from keras.utils import to_categorical
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, Flatten, LSTM, TimeDistributed, Bidirectional,RepeatVector, SimpleRNN, GRU
from keras.layers.normalization import BatchNormalization
import tensorflow as tf
import math


def edit_dist(y_pred, y_true):
  dist = tf.edit_distance(tf.sparse.from_dense([y_pred]), tf.sparse.from_dense([y_true]), normalize=False)
  return dist

def baseline_model():
    model = Sequential()
    model.add(Bidirectional(LSTM(200,  input_shape=(1,x_train.shape[2]))))
    model.add(RepeatVector(y_train.shape[1]))
    model.add(Bidirectional(LSTM(100, return_sequences = True)))
    model.add(Dropout(0.2))
    model.add(Bidirectional(LSTM(100, return_sequences = True)))
    model.add(Dropout(0.2))
    model.add(TimeDistributed(Dense(y_train.shape[2], activation='softmax')))
    model.compile(loss=edit_dist, optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), metrics=['accuracy'])  
    return model


ips = read_csv('Data (1).csv', dtype=float)
fullset = np.array(ips)
fullset=fullset[2:,:]

xy = fullset[np.where(fullset[:,0]==518)]
np.random.shuffle(xy)

x = xy[:,4:76]
y_1 = xy[:,76:]
x=x/x.max(axis=0)

k=int(np.around(0.85*x.shape[0]))
l=int(np.around(0.85*x.shape[0]))
x_train = x[0:l,:]
y_train = y_1[0:l,:]
x_val = x[l+1:,:]
y_val = y_1[l+1:,:]
y_train = to_categorical(y_train)
y_val = to_categorical(y_val)

x_train = np.reshape(x_train, (x_train.shape[0],1, x_train.shape[1]))
x_val = np.reshape(x_val, (x_val.shape[0],1, x_val.shape[1]))

model = baseline_model()
hist = model.fit(x_train, y_train, epochs=300, batch_size = 128, verbose=1,validation_data=(x_val, 
y_val))

plt.plot(hist.history['val_accuracy'])
plt.show()

错误：

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:757 train_step
        self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:498 minimize
        return self.apply_gradients(grads_and_vars, name=name)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:598 apply_gradients
        grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/utils.py:79 filter_empty_gradients
        ([v.name for _, v in grads_and_vars],))

    ValueError: No gradients provided for any variable: ['bidirectional_27/forward_lstm_27/lstm_cell_82/kernel:0', 'bidirectional_27/forward_lstm_27/lstm_cell_82/recurrent_kernel:0', 'bidirectional_27/forward_lstm_27/lstm_cell_82/bias:0', 'bidirectional_27/backward_lstm_27/lstm_cell_83/kernel:0', 'bidirectional_27/backward_lstm_27/lstm_cell_83/recurrent_kernel:0', 'bidirectional_27/backward_lstm_27/lstm_cell_83/bias:0', 'bidirectional_28/forward_lstm_28/lstm_cell_85/kernel:0', 'bidirectional_28/forward_lstm_28/lstm_cell_85/recurrent_kernel:0', 'bidirectional_28/forward_lstm_28/lstm_cell_85/bias:0', 'bidirectional_28/backward_lstm_28/lstm_cell_86/kernel:0', 'bidirectional_28/backward_lstm_28/lstm_cell_86/recurrent_kernel:0', 'bidirectional_28/backward_lstm_28/lstm_cell_86/bias:0', 'bidirectional_29/forward_lstm_29/lstm_cell_88/kernel:0', 'bidirectional_29/forward_lstm_29/lstm_cell_88/recurrent_kernel:0', 'bidirectional_29/forward_lstm_29/lstm_cell_88/bias:0', 'bidirectional_29/backward_lstm_29/lstm_cell_89/kernel:0', 'bidirectional_29/backward_lstm_29/lstm_cell_89/recurrent_kernel:0', 'bidirectional_29/backward_lstm_29/lstm_cell_89/bias:0', 'time_distributed_9/kernel:0', 'time_distributed_9/bias:0'].

另外，我是否应该制作自己的编辑距离函数来用作损失函数，而不是使用内置的函数来加快操作？

Answer 1

编辑距离不可微，因此不能将其用作损失函数。您只能将可微函数用作损失。