Regression Model Problems - tensorflow

Question

What is wrong with this model? I am new to learning about this stuff. Did I compile the model incorrectly or is it the structure itself?
Here's what the code looked like:
Y-set was defined as a list of floats [54.7, 52.5, 51.4, 51.5, 50.5] and so was x [0, 1.5, 2, 2.5, 3.5]
Here's the code and the results for the training:
model = Sequential([
Dense(units = 1, input_shape = [1]),
Dense(units = 60, activation = 'relu'),
Dense(units = 1)])
model.compile(optimizer = 'sgd', loss = 'mean_squared_error', metrics = ['mae'])
model.fit(x_set, y_set, epochs = 10)
This was the output it gave me:
Epoch 1/10
1/1 [==============================] - 0s 8ms/step - loss: 1519.2493 - mae: 37.8005
Epoch 2/10
1/1 [==============================] - 0s 9ms/step - loss: 577948.8750 - mae: 674.4330
Epoch 3/10
1/1 [==============================] - 0s 8ms/step - loss: 159614431746567700480.0000 -
mae: 11284396032.0000
Epoch 4/10
1/1 [==============================] - 0s 8ms/step - loss: inf - mae: inf
Epoch 5/10
1/1 [==============================] - 0s 9ms/step - loss: nan - mae: nan
Epoch 6/10
1/1 [==============================] - 0s 8ms/step - loss: nan - mae: nan
Epoch 7/10
1/1 [==============================] - 0s 8ms/step - loss: nan - mae: nan
Epoch 8/10
1/1 [==============================] - 0s 9ms/step - loss: nan - mae: nan
Epoch 9/10
1/1 [==============================] - 0s 10ms/step - loss: nan - mae: nan
Epoch 10/10
1/1 [==============================] - 0s 8ms/step - loss: nan - mae: nan
Obviously, it does not work.

Answer 1

You need to use the activation function to the layers in model definition to fix this issue.
Please check this fixed code:
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
x_set=np.array([54.7, 52.5, 51.4, 51.5, 50.5])
y_set=np.array([0, 1.5, 2, 2.5, 3.5])
model = Sequential([
Dense(units = 1 , input_shape = [1,], activation = 'relu'),
Dense(units = 60, activation = 'relu'),
Dense(units = 1, activation = 'sigmoid')])
model.compile(optimizer = 'sgd', loss = 'mean_squared_error', metrics = ['mae'])
model.fit(x_set, y_set, epochs = 10)
Output:
Epoch 1/10
1/1 [==============================] - 1s 1s/step - loss: 2.1748 - mae: 1.3081
Epoch 2/10
1/1 [==============================] - 0s 13ms/step - loss: 2.1692 - mae: 1.3063
Epoch 3/10
1/1 [==============================] - 0s 17ms/step - loss: 2.1656 - mae: 1.3051
Epoch 4/10
1/1 [==============================] - 0s 17ms/step - loss: 2.1632 - mae: 1.3043
Epoch 5/10
1/1 [==============================] - 0s 18ms/step - loss: 2.1614 - mae: 1.3037
Epoch 6/10
1/1 [==============================] - 0s 29ms/step - loss: 2.1600 - mae: 1.3033
Epoch 7/10
1/1 [==============================] - 0s 30ms/step - loss: 2.1589 - mae: 1.3029
Epoch 8/10
1/1 [==============================] - 0s 10ms/step - loss: 2.1581 - mae: 1.3027
Epoch 9/10
1/1 [==============================] - 0s 12ms/step - loss: 2.1573 - mae: 1.3024
Epoch 10/10
1/1 [==============================] - 0s 12ms/step - loss: 2.1567 - mae: 1.3022
<keras.callbacks.History at 0x7f3b865a75d0>