neural network always give me accuracy equal zero with Keras

Question

import keras
import pandas as pd
import numpy as np

from google.colab import files
uploaded = files.upload()

import io
dataset = pd.read_csv(io.BytesIO(uploaded['kc_house_data.csv']))

dataset.head()

id  date    price   bedrooms    bathrooms   sqft_living sqft_lot    floors  waterfront  view    ... grade   sqft_above  sqft_basement   yr_built    yr_renovated    zipcode lat long    sqft_living15   sqft_lot15
0   7129300520  20141013T000000 221900.0    3   1.00    1180    5650    1.0 0   0   ... 7   1180    0   1955    0   98178   47.5112 -122.257    1340    5650
1   6414100192  20141209T000000 538000.0    3   2.25    2570    7242    2.0 0   0   ... 7   2170    400 1951    1991    98125   47.7210 -122.319    1690    7639
2   5631500400  20150225T000000 180000.0    2   1.00    770 10000   1.0 0   0   ... 6   770 0   1933    0   98028   47.7379 -122.233    2720    8062
3   2487200875  20141209T000000 604000.0    4   3.00    1960    5000    1.0 0   0   ... 7   1050    910 1965    0   98136   47.5208 -122.393    1360    5000
4   1954400510  20150218T000000 510000.0    3   2.00    1680    8080    1.0 0   0   ... 8   1680    0   1987    0   98074   47.6168 -122.045    1800    7503
5 rows × 21 columns

features = dataset.drop(columns=['price', 'id', 'date'])
labels = dataset[['price']]

model = keras.models.Sequential([
         keras.layers.Dense(19, 'relu', input_shape=(18,)),
         keras.layers.Dense(19, 'relu'),
         keras.layers.Dense(1)                     
])

from keras import metrics
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])

model.fit(features, labels, epochs=10, batch_size=5)

Epoch 1/10
676/676 [==============================] - 2s 3ms/step - loss: 65482420224.0000 - accuracy: 0.0000e+00
Epoch 2/10
676/676 [==============================] - 2s 3ms/step - loss: 65566482432.0000 - accuracy: 0.0000e+00
Epoch 3/10
676/676 [==============================] - 2s 3ms/step - loss: 65582268416.0000 - accuracy: 0.0000e+00
Epoch 4/10
676/676 [==============================] - 2s 3ms/step - loss: 65601855488.0000 - accuracy: 0.0000e+00
Epoch 5/10
676/676 [==============================] - 2s 3ms/step - loss: 65537380352.0000 - accuracy: 0.0000e+00
Epoch 6/10
676/676 [==============================] - 2s 2ms/step - loss: 65665077248.0000 - accuracy: 0.0000e+00
Epoch 7/10
676/676 [==============================] - 2s 2ms/step - loss: 65604460544.0000 - accuracy: 0.0000e+00
Epoch 8/10
676/676 [==============================] - 2s 2ms/step - loss: 65511895040.0000 - accuracy: 0.0000e+00
Epoch 9/10
676/676 [==============================] - 2s 3ms/step - loss: 65589620736.0000 - accuracy: 0.0000e+00
Epoch 10/10
676/676 [==============================] - 2s 3ms/step - loss: 65584775168.0000 - accuracy: 0.0000e+00
<keras.callbacks.History at 0x7f3dc673af90>

Answer 1

This is a regression problem, not a classification one. You can't use accuracy as a metric for a regression problem. Change metrics to 'mean_squared_error' in the model.compile method. something like this:-

model.compile(loss= "mean_squared_error" , optimizer="adam", metrics=["mean_squared_error"])