我创建了一个简单的回归模型来训练csv数据。我已经成功地完成了训练和评估，但是当我试图通过单行来预测其输出时，我得到了一个输入形状错误。
我在google collab上试过了。
下面是代码。

# load libraries and csv
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt

insurance_data = pd.read_csv("https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv")

insurance_data.head()

# import the Classes. 
from sklearn.compose import make_column_transformer
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from sklearn.model_selection import train_test_split

# set up Normalization and One Hot encoding for columns. 
CT = make_column_transformer(
    (MinMaxScaler(), ["age", "bmi", "children"]), #normalize all these columns only. 
    (OneHotEncoder(handle_unknown="ignore"), ["sex", "smoker", "region"])
)

# make X and Y. 
X1 = insurance_data.drop("charges", axis=1)
Y1 = insurance_data["charges"]

# make Train and Test data. 
X1_train, X1_test, Y1_train, Y1_test = train_test_split(X1, Y1, test_size=0.2, random_state=42)

# Fit column transformer to our Training data only. 
CT.fit(X1_train)

# transform training and test data with Normalization
X1_train_normalized = CT.transform(X1_train)
X1_test_normalized = CT.transform(X1_test)

X1_train.shape, X1_train_normalized.shape

# make the model, 
tf.random.set_seed(43)

insurance_v3 = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(11)),
    tf.keras.layers.Dense(100, activation=None, name="hidden1"),
    tf.keras.layers.Dense(10, activation=None, name="hidden2"),
    tf.keras.layers.Dense(1, activation=None, name="out1")
], name="insurance_model_v3")

insurance_v3.compile(
    loss=tf.keras.losses.mae, 
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
    metrics=["mae"]
)

insurance_v3.fit(X1_train_normalized, Y1_train, epochs=100, verbose=0)
insurance_v3.evaluate(X1_test_normalized, Y1_test)

# test custom output. 

t = X1_train_normalized[2]
t = tf.convert_to_tensor(t, tf.float64)
t = tf.expand_dims(t, 1)
t
insurance_v3.predict(t)

我从标准化的X训练中取了一行（我不应该这样做），只是为了看看它是否接受它并返回一个Y值。
你能告诉我如何正确地做到这一点，以便我可以从网站表单中获取值，将其转换为Tensor，然后将其传递给模型，然后模型返回一个值吗？因为我应该在django应用程序中使用这个模型。
我尝试将行转换为Tensor，但每次它都会给我一个或另一个错误。首先，我得到了所需维度为2的ndim错误，但有时我会得到unrankTensor的错误。