# How to pass "step" to ExponentialDecay in GradientTape

I tried to use an optimizers.schedules.ExponentialDecay isntance as the learning_rate to Adm optimizer, but i don't know how to pass "step" to it when train the model in GradientTape.

I use tensorflow-gpu-2.0-alpha0 and python3.6. And i read the doc https://tensorflow.google.cn/versions/r2.0/api_docs/python/tf/optimizers/schedules/ExponentialDecay but with no idea how to tackle it.

```
initial_learning_rate = 0.1
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate,
decay_steps=100000,
decay_rate=0.96)
optimizer = tf.optimizers.Adam(learning_rate = lr_schedule)
for epoch in range(self.Epoch):
...
...
with GradientTape as tape:
pred_label = model(images)
loss = calc_loss(pred_label, ground_label)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# I tried this but the result seem not right.
# I want to pass "epoch" as "step" to lr_schedule
```