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Tensorflow Backend Questions #193
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Hi,
Two questions about using TC with the TensorFlow backend:
- When using the Tensorflow backend and keras.Sequential, does TensorCircuit support putting additional Keras layers before the qml_layer, such as a dense layer to transform the input before being fed to a qml_layer defined in TC? If yes, would it be possible to let TC fine-tune the input embeddings? Something like this:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Embedding(100, 5))
model.add(tf.keras.layers.Dense(num_params))
model.add(qml_layer)
model.add(tf.keras.layers.Dense(1, activation="sigmoid"))
- What's the best way to debug and print the variables in the debugger when using the TF backend? tf.print() doesn't seem to work?
Thanks!
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K = tc.set_backend("tensorflow") def f(param, weights): c = tc.Circuit(2) print(param) return 1.0 qmllayer = tc.keras.HardwareLayer(f, [2]) qmllayer(K.ones([2]))
what about this, HardwareLayer is a non jit version wrapper for keras layer, after you debug, you can switch back to KerasLayer
Replies: 1 comment 4 replies
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- of course, qmllayer is just a keras layer, you can compose any computational graph with all kinds of classical and quantum layers in one model
- you can always directly
print(model.variables)outside a jitted function?
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Thanks! Is there a good recommended way to debug and print within a quantum layer such as (when used as a Keras layer -- set run_eagerly=True doesn't seem to help):
def qlayer(x):
c = tc.Circuit(n, inputs=x)
### print here???
for j in range(max_sent_len):
for i in range(n):
c.rx(i, theta=x[i])
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K = tc.set_backend("tensorflow") def f(param, weights): c = tc.Circuit(2) print(param) return 1.0 qmllayer = tc.keras.HardwareLayer(f, [2]) qmllayer(K.ones([2]))
what about this, HardwareLayer is a non jit version wrapper for keras layer, after you debug, you can switch back to KerasLayer
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One quick additional question:
If the JAX backend is used, would it be possible to use jax.lax.scan or jax.lax.fori_loop in place of standard for loops to speed up circuit compile especially for deep circuits?
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yes, of course, pls see this nice example: https://github.com/tencent-quantum-lab/tensorcircuit/blob/master/examples/jax_scan_jit_acc.py
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