We’re very excited to announce the release of Strawberry Fields version 0.14.0.

This release is focused on updating the `"tf"`

backend to support TensorFlow 2.0 and above. You can now use the features from TensorFlow 2 for building deep learning models in Strawberry Fields.

**TensorFlow 2.0**

Eager execution is now implemented by default in TensorFlow and thus also the default mode in the `"tf"`

backend in Strawberry Fields — there is no longer any need to create a `tf.Session()`

. You can now use `tf.GradientTape()`

for gradient calculations.

See Bermigrasi ke TensorFlow 2 | TensorFlow Core for help with migrating your TensorFlow 1 code to TensorFlow 2.

Below is an example for using TensorFlow 2.0 to train a variational photonic circuit:

## Code example

```
eng = sf.Engine(backend="tf", backend_options={"cutoff_dim": 7})
prog = sf.Program(1)
with prog.context as q:
# Apply a single mode displacement with free parameters
Dgate(prog.params("a"), prog.params("p")) | q[0]
opt = tf.keras.optimizers.Adam(learning_rate=0.1)
alpha = tf.Variable(0.1)
phi = tf.Variable(0.1)
for step in range(50):
# reset the engine if it has already been executed
if eng.run_progs:
eng.reset()
with tf.GradientTape() as tape:
# execute the engine
results = eng.run(prog, args={'a': alpha, 'p': phi})
# get the probability of fock state |1>
prob = results.state.fock_prob([1])
# negative sign to maximize prob
loss = -prob
gradients = tape.gradient(loss, [alpha, phi])
opt.apply_gradients(zip(gradients, [alpha, phi]))
print("Value at step {}: {}".format(step, prob))
```

For more details and demonstrations of the new TensorFlow 2.0-compatible backend, see our optimization and machine learning tutorials.

The full release notes are available at Release Release 0.14.0 · XanaduAI/strawberryfields · GitHub.

As always, this release would not have been possible without all the help from our contributors:

@Tom_Bromley, @theodor, @josh, @nathan, Filippo Miatto, @Nicolas_Quesada, @antalszava, Paul Tan