I downloaded the csv data of boston house price to practice tensorflow; the csv data have 13 columns and the number 13 column is the price;

I processed the data and send them into the model to tranining;

But the training result is that the loss is a negative number and the absolute value of loss is become bigger and bigger per epoch. And the accuracy is always 0 simutaneously.

So is there any wrong with my code when process the csv data or create the model?

please help me!

```
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
f = open('bostonHouse.csv')
df = pd.read_csv(f)
data = np.array(df)
plt.figure()
plt.plot(data)
plt.show()
normalize_data = (data - np.mean(data)) / np.std(data)
normalize_data = normalize_data[:, np.newaxis]
train_x, train_y = [], []
for i in range(len(normalize_data)):
x = normalize_data[i][0][:12]
y = normalize_data[i][0][12]
train_x.append(x.tolist())
train_y.append(y.tolist())
print("train_x data:{}".format(train_x[:1]))
print("train_y data:{}".format(train_y[:1]))
print('train x len', len(train_x[0]))
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(12,)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(
loss="binary_crossentropy",
optimizer='adam',
metrics=['accuracy'],
)
model.summary()
model.fit(x=train_x, y=train_y, epochs=20)
```