Could you provide an example of regression using tf eager? Like the examples here: https://pennylane.readthedocs.io/en/latest/code/interfaces/tfe.html

Thanks!

`Wei`

Could you provide an example of regression using tf eager? Like the examples here: https://pennylane.readthedocs.io/en/latest/code/interfaces/tfe.html

Thanks!

`Wei`

Hi @cubicgate. You can have a look at some of the TensorFlow examples here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/linear_regression

Thanks @josh for this example. I have a simple program, but it did not work. Could you take a look at it? Thanks!

```
import tensorflow as tf
import tensorflow.contrib.eager as tfe
from pennylane import numpy as np
tf.enable_eager_execution()
import pennylane as qml
dev = qml.device('default.qubit', wires=2)
@qml.qnode(dev, interface='tfe')
def circuit(phi, theta, x):
# print('x ', x)
qml.RX(x, wires=0)
qml.RX(phi[0], wires=0)
qml.RY(phi[1], wires=1)
qml.CNOT(wires=[0, 1])
qml.PhaseShift(theta, wires=0)
return qml.expval(qml.PauliZ(0))
phi = tfe.Variable([0.5, 0.1], dtype=tf.float64)
theta = tfe.Variable(0.2, dtype=tf.float64)
X=[0.1, 0.2, 0.3]
Y=[0.2, 0.3, 0.4]
X = np.array(X)
Y = np.array(Y)
def loss(phi, theta, X, Y):
res = 0
for i in range(len(X)):
res = res + tf.square(circuit(phi, theta, X[i]) - Y[i])
return tf.reduce_mean(res)
opt = tf.train.AdamOptimizer()
steps = 200
grads = tfe.implicit_gradients(loss)
for i in range(steps):
opt.apply_gradients(grads(phi, theta, X, Y))
print(phi)
print(theta)
```