我的代码使用所有megasena结果的csv文件。

data['Data'] = pd.to_datetime(data['Data'], format='%d/%m/%Y')
features = data[['Data', 'Conc']]
labels = data[['NR1', 'NR2', 'NR3', 'NR4', 'NR5', 'NR6']]

# convert date column to numerical features
features['Day'] = features['Data'].dt.day 
features['Month'] = features['Data'].dt.month 
features['Year'] = features['Data'].dt.year  

# normalize the features (year should have more weightage than day and month)
features['Day'] = features['Day'] / 31.0
features['Month'] = features['Month'] / 12.0
features['Year'] = (features['Year'] - 2000) / 20.0

# split the data into training and testing sets
train_features = features[features['Conc'] <= 2601].values
train_labels = labels[labels.index <= 2601].values
test_features = features[features['Conc'] > 2601].values

# define the model architecture
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(64, input_shape=[5], activation='relu'),
    tf.keras.layers.Dense(32, activation='relu'),
    tf.keras.layers.Dense(6, activation='softmax')
])

这个错误发生在训练中，我已经尝试通过将日，月和年的类型改为float，np.float32，tf.float32来解决它，但没有一个解决了这个问题。

# train the model
model.fit(train_features, train_labels, epochs=100)