Python TensorFlow错误：ValueError：没有为任何变量提供梯度

我正在尝试运行下面的tensorflow应用程序，但我不断收到与最后一行代码相关的错误。除最后一行外，所有操作都正常运行。有人能帮忙吗

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error

from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.models import load_model

df = pd.read_csv('kc_house_data.csv')
print(f"df.head():\n{df.head()}")

print(f"df.isnull().sum():\n{df.isnull().sum()}")

print(f"df.describe().transpose():\n{df.describe().transpose()}")

corr = df.corr()
print(f"corr:\n{corr}")

corr_sorted = corr['price'].sort_values()      
sort_df = df.sort_values('price', ascending=False)         
non_top_1_perc = sort_df.iloc[216:]

print(f"df.head(): {df.head()}")

df = df.drop('id', axis=1)

#convert do datetime
df['date'] = pd.to_datetime(df['date'])
#feature engineering
#extracting the year & month
df['year'] = df['date'].apply(lambda date: date.year)
df['month'] = df['date'].apply(lambda date: date.month)

monthly_prices = df.groupby('month').mean()['price']
#monthly_prices.plot()
#plt.show()
print(f"monthly_prices: {monthly_prices}")

yearly_prices = df.groupby('year').mean()['price']
print(f"yearly_prices: {yearly_prices}")

df = df.drop('date', axis=1)

df = df.drop('zipcode', axis=1)

#sklearn
X = df.drop('price', axis=1).values
y = df['price'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)

#perform the scaling to prevent data leakage from the test set
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
#do not fit to your test set because you don't want to assume prior information
X_test = scaler.transform(X_test)

X_train.shape

#tensorflow
model = Sequential()
model.add(Dense(19, activation='relu'))
model.add(Dense(19, activation='relu'))
model.add(Dense(19, activation='relu'))
model.add(Dense(19, activation='relu'))
model.add(Dense(1))

model.compile(optimizer='adam', loss_weights='mse')

model.fit(x=X_train, y=y_train, validation_data=(X_test, y_test), batch_size=128, epochs=400)

错误：

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ValueError：没有为任何变量提供渐变：['sequential/densite/kernel:0'、'sequential/densite/bias:0'、'sequential/densite\u 1/bias:0'、'sequential/densite\u 2/bias:0'、'sequential/densite\u 3/kernel:0'、'sequential/densite\u 4/kernel:0'、'sequential/densitive\u 4/bias:0'].

我确信您的错误是因为您没有指定损失，只指定了损失权重。例如，将编译行更改为

model.compileoptimizer='adam'，loss='mse'

---

您确定loss_weights='mse'不应该是loss='mse'吗？您必须在执行model.compile时提供loss参数。谢谢！这是错误！