Keras Interface #54
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Keras Interface #54
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| @@ -127,6 +127,7 @@ venv/ | |||
| ENV/ | |||
| env.bak/ | |||
| venv.bak/ | |||
| qiboml/ | |||
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| qiboml/ |
better not to ignore this
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| { | ||
| // Usare IntelliSense per informazioni sui possibili attributi. | ||
| // Al passaggio del mouse vengono visualizzate le descrizioni degli attributi esistenti. | ||
| // Per altre informazioni, visitare: https://go.microsoft.com/fwlink/?linkid=830387 | ||
| "version": "0.2.0", | ||
| "configurations": [ | ||
| { | ||
| "name": "Debugger Python: File corrente", | ||
| "type": "debugpy", | ||
| "request": "launch", | ||
| "program": "${file}", | ||
| "justMyCode": false, | ||
| "console": "integratedTerminal", | ||
| "env": { | ||
| "ON_HEROKU": "0", | ||
| "PYTEST_ADDOPTS": "-c pytest.ini", | ||
| "ECHO_SQL_QUERIES": "1" | ||
| }, | ||
| } | ||
| ] | ||
| } |
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this is probably some vscode leftover, better to remove it
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| import tensorflow as tf | ||
| import numpy as np | ||
| from qibo import Circuit, gates | ||
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| @tf.custom_gradient | ||
| def custom_operation(): | ||
| output = | ||
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| def grad_fn() | ||
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| return output, grad_fn | ||
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| class MyLayer(tf.keras.layers.Layer): | ||
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| def __init__(self): | ||
| super(MyLayer, self).__init__(): | ||
| self.circuit = self.circuit() | ||
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| self.weights = self.add_weights(name='w', shape=(4,), initializer="random_normal") | ||
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| def circuit(self): | ||
| c = Circuit(2) | ||
| c.add(gates.X(0)) | ||
| c.add(gates.RX(1, theta=0.5)) | ||
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| def call(self, x): | ||
| self.circuit() | ||
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| def |
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this has to be removed as well
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| [pytest] | ||
| env = | ||
| TESTING=true | ||
| ENV=local |
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even this has to go I would say
| @@ -44,8 +46,9 @@ def __post_init__( | |||
| self._circuit.add(gates.RY(q, theta=0.0, trainable=False)) | |||
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| def _set_phases(self, x: ndarray): | |||
| for gate, phase in zip(self._circuit.parametrized_gates, x.ravel()): | |||
| gate.parameters = phase | |||
| phase = tf.reshape(x, [-1]) | |||
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we cannot have tf functions here, this has to work for pytorch as well and eventually jax when we introduce the Flax interface
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| def draw( | ||
| self, | ||
| ): | ||
| breakpoint() | ||
| print("ciao") | ||
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| def draw( | |
| self, | |
| ): | |
| breakpoint() | |
| print("ciao") |
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| import qibo | ||
| from qibo import gates, Circuit | ||
| import numpy as np | ||
| import tensorflow as tf | ||
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| def random_subset(nqubits, k): | ||
| return np.random.choice(range(nqubits), size=(k,), replace=False).tolist() | ||
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| nqubits = 3 | ||
| dim = 2 | ||
| backend = "tensorflow" | ||
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| c = Circuit(nqubits) | ||
| c.add(gates.X(0)) | ||
| c.add(gates.X(1)) | ||
| c.add(gates.Z(1)) | ||
| c.add(gates.CNOT(0, 1)) | ||
| c.add(gates.RX(0, theta=0.4)) | ||
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| random_choice = random_subset(nqubits, dim) | ||
| print(f"Scelta random {random_choice}") | ||
| result = c().probabilities() | ||
| print(result) | ||
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| tensor = tf.random.uniform((2, nqubits), minval=0, maxval=2, dtype=tf.int32) | ||
| print(f"Tensore: {tensor}") |
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if this test covers some case that is not tested in tests/ then better to add it there, otherwise we should remove this
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| # "pytorch", | ||
| # "jax", |
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| # "pytorch", | |
| # "jax", | |
| "pytorch", | |
| "jax", |
reactivate these to check that pytorch and jax are still working
| ] | ||
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| FRONTENDS = [ | ||
| "pytorch", | ||
| # "pytorch", |
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| # "pytorch", | |
| "pytorch", |
same here
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| def build_linear_layer_adding(frontend, q_model): | ||
| model = frontend.keras.Sequential() | ||
| model.add( | ||
| frontend.keras.layers.Dense( | ||
| 5, | ||
| name="Dense0", | ||
| activation="relu", | ||
| input_shape=(input_size,), | ||
| ) | ||
| ) | ||
| model.add(q_model) | ||
| model.add( | ||
| frontend.keras.layers.Dense( | ||
| 12, | ||
| name="Dense1", | ||
| activation="relu", | ||
| ) | ||
| ) |
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what's this for? can't you use the build_sequential below?
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This is the implementation of the Keras Interface. This PR supports only the differentiation with TensorFlow as backend.
In a future PR, I plan to enhance this Keras Interface by adding support for other types of differentiation, such as JAX.
As stated in issue #47, I had to change the Binary encoding implementation to use Symbolic Tensors.