My model accuracy doesn't change after first epoch I've created a model to predict housing prices in LA, and what should be a simple regression problem, is giving me headache because the loss is just too big and my accuracy wont change.

I've already tried normalizing, changing the architecture (decreasing layers, hidden units), adding dropout, changed the loss function, batch size, epochs and my accuracy is still only 0.022

input_shape = X_train_2[0].shape

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=input_shape),
    tf.keras.layers.Dense(units=300, activation=tf.nn.relu),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Dense(units=300, activation=tf.nn.relu),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Dense(units = 1, kernel_initializer = 'lecun_normal', activation='linear')
    ])

model.compile(optimizer='adam',loss='mean_squared_error', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=5, batch_size=32)
model.summary()
model.evaluate(X_test_, y_test)


Training Log

Epoch 1/5 32444/32444 [==============================] - 1s
    38us/sample - loss: 90230324650039.5469 - acc: 0.0012 
Epoch 2/5
    32444/32444 [==============================] - 1s 28us/sample -
    loss: 90230315396180.2031 - acc: 0.0022 
Epoch 3/5 32444/32444
    [==============================] - 1s 27us/sample - loss:
    90230293267377.3438 - acc: 0.0022 
Epoch 4/5 32444/32444 [==============================] - 1s 27us/sample - loss:
    90230260607518.6250 - acc: 0.0022 
Epoch 5/5 32444/32444 [==============================] - 1s 28us/sample - loss:
    90230216684525.4375 - acc: 0.0022
Your metric is accuracy although you are working on a regression problem, this doesn't make sense. You should use instead:

metrics = ['mean squared error']