```python
import tensorflow as tf
```


```python
import numpy as np
```


```python
x_data= np.random.rand(100).astype(np.float32)
```


```python
y_data= x_data * 0.1 +0.3
```


```python
W = tf.Variable(tf.random_uniform([1], -1.0,1.0))
```


```python
b = tf.Variable(tf.zeros([1]))
```


```python
y = W * x_data + b
```


```python
loss = tf.reduce_mean(tf.square(y-y_data))


```


```python
optimizer = tf.train.GradientDescentOptimizer(0.5)
```


```python
train = optimizer.minimize(loss)
```


```python
init = tf.global_variables_initializer()

```


```python
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.2
sess = tf.Session(config=config)
```


```python
sess.run(init)
```


```python
for step in range(100000):
    sess.run(train)
    if step % 2000 == 0:
        print(step, sess.run(W), sess.run(b))
```


```python

```