diff --git a/tutorials/binary_mnist.ipynb b/tutorials/binary_mnist.ipynb index df2e0bf..066e966 100644 --- a/tutorials/binary_mnist.ipynb +++ b/tutorials/binary_mnist.ipynb @@ -106,15 +106,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Qibo 0.2.14|INFO|2024-11-08 11:16:48]: Using pytorch backend on cpu\n" + "2024-11-19 20:07:35.829624: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-19 20:07:35.841509: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-19 20:07:35.845157: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-19 20:07:35.855091: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-11-19 20:07:36.540988: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "[Qibo 0.2.13|INFO|2024-11-19 20:07:37]: Using pytorch backend on cuda:0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "q0: ─RY─RZ─RY─RZ─\n", - "q1: ─RY─RZ─RY─RZ─\n" + "q0: ─RY─RZ─o─RY─RZ─o─RY─RZ─o─RY─RZ─o─RY─RZ─o─\n", + "q1: ─RY─RZ─X─RY─RZ─X─RY─RZ─X─RY─RZ─X─RY─RZ─X─\n" ] } ], @@ -126,7 +132,7 @@ "\n", "from qiboml.models.pytorch import QuantumModel\n", "from qiboml.models.encoding import PhaseEncoding\n", - "from qiboml.models.decoding import Expectation\n", + "from qiboml.models.decoding import Expectation, Probabilities\n", "\n", "# then we build the quantum model, that will\n", "# act as a classification layer on top of the\n", @@ -138,16 +144,17 @@ "\n", "# we prepare a quantum encoder to encode the \n", "# classical data in a quantum circuit\n", - "encoding = PhaseEncoding(nqubits=2)\n", + "nqubits = 2\n", + "encoding = PhaseEncoding(nqubits=nqubits)\n", "\n", "# we construct a trainable parametrized circuit\n", "# that is the core of our quantum model\n", - "circuit = Circuit(2)\n", - "for _ in range(2):\n", - " for q in (0,1):\n", + "circuit = Circuit(nqubits)\n", + "for _ in range(5):\n", + " for q in range(nqubits):\n", " circuit.add(gates.RY(q, theta=np.random.randn() * np.pi))\n", " circuit.add(gates.RZ(q, theta=np.random.randn() * np.pi))\n", - " #circuit.add(gates.CNOT(0,1))\n", + " circuit.add(gates.CNOT(0,1))\n", "circuit.draw()\n", "\n", "# and finally we need a decoder to decode the quantum\n", @@ -156,9 +163,9 @@ "\n", "# for this we need to define the observable we wish to\n", "# measure\n", - "observable = SymbolicHamiltonian(3 * Z(0) * Z(1), nqubits=2)\n", + "observable = SymbolicHamiltonian(Z(0), nqubits=nqubits)\n", "# and then construct the expectation decoder\n", - "decoding = Expectation(nqubits=2, observable=observable, analytic=True)\n", + "decoding = Expectation(nqubits=nqubits, observable=observable, analytic=True)\n", "\n", "# we can then build the complete quantum model\n", "q_model = QuantumModel(\n", @@ -171,19 +178,48 @@ { "cell_type": "code", "execution_count": 4, + "id": "74c070bc-c58a-4703-97f3-699ee78fef66", + "metadata": {}, + "outputs": [], + "source": [ + "# Since we are using an angular encoding, it is better to\n", + "# make sure that the output of the img_encoder are meaningful\n", + "# angles. To do so we can define a custom torch activation\n", + "# that rescales everything in the interval (-pi, pi)\n", + "\n", + "class PiTanh(nn.Module):\n", + "\n", + " def forward(self, x):\n", + " # we first rescale x to avoid the risk\n", + " # of saturating the tanh too often and, thus,\n", + " # producing always the same angle\n", + " x = x / x.max()\n", + " # then we just apply the tanh and rescale by pi\n", + " return np.pi * F.tanh(x)\n", + "\n", + "activation = PiTanh().double()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "dac45461-78c3-48a0-95fc-9fafb5d14bcd", "metadata": {}, "outputs": [], "source": [ + "# Finally, the complete hybrid classical-quantum pipeline can\n", + "# be built by stacking the different components in a \n", + "# torch.nn.Sequential as usual\n", "model = nn.Sequential(\n", " img_encoder,\n", - " q_model\n", + " activation,\n", + " q_model,\n", ")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "bc298c92-88fd-44b4-91ef-efe6ebbae0f6", "metadata": {}, "outputs": [ @@ -191,13 +227,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6555)\n" + "tensor(0.)\n" ] } ], "source": [ + "# to evaluate the performance of the model we can use the\n", + "# standard binary F1 score\n", "from torcheval.metrics.functional import binary_f1_score\n", "\n", + "# let's check how the model does before training\n", "with torch.no_grad():\n", " predictions = torch.as_tensor([F.sigmoid(model(x.double().unsqueeze(0))) for x in data])\n", " print(binary_f1_score(predictions, targets))" @@ -205,23 +244,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "b547bb43-bd97-40ee-b0f5-b765fffba02f", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Final Loss: 0.3201002721815339\n" + ] }, { 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", 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" ] @@ -234,25 +270,39 @@ "import matplotlib.pyplot as plt\n", "from torch.optim import Adam\n", "\n", + "# now we can train the model as we would do\n", + "# with any other pytorch model\n", + "\n", + "# let's use the torch.optim.Adam optimizer\n", "optimizer = Adam(model.parameters())\n", + "# we are going to train for 5 epochs\n", "losses = []\n", - "for _ in range(10):\n", + "for _ in range(5):\n", + " # reshuffle the data before each epoch\n", " permutation = torch.randperm(len(data))\n", " for x, y in zip(data[permutation], targets[permutation]):\n", " optimizer.zero_grad()\n", + " # get the predictions\n", " out = model(x.double().unsqueeze(0))\n", - " #print(out)\n", + " # calculate the loss, we can take the\n", + " # standard binary cross entropy\n", " loss = F.binary_cross_entropy_with_logits(out.view(1,), y.double().view(1,))\n", + " # backpropagate\n", " loss.backward()\n", + " # update the parameters\n", " optimizer.step()\n", " losses.append(loss.item())\n", "\n", - "plt.plot(range(len(losses)), losses)" + "# we can plot the training loss over the epochs\n", + "plt.plot(range(len(losses)), losses)\n", + "# for reference we calculate the average loss obtained for\n", + "# the last 20 predictions after training\n", + "print(f\"Final Loss: {sum(losses[-20:])/20}\")" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "id": "0f1c1d2a-0426-4e43-8ade-8f00f4d0b9bf", "metadata": {}, "outputs": [ @@ -260,11 +310,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6481)\n" + "tensor(1.)\n" ] } ], "source": [ + "# Finally, let's double check that the F1 score has improved\n", "with torch.no_grad():\n", " predictions = torch.as_tensor([F.sigmoid(model(x.double().unsqueeze(0))) for x in data])\n", " print(binary_f1_score(predictions, targets))" @@ -273,7 +324,7 @@ { "cell_type": "code", "execution_count": null, - "id": "93d1ad58-946d-4f45-87d0-956b1caca221", + "id": "e68ab0b6-1a42-4c36-84d1-c1bfd439a24b", "metadata": {}, "outputs": [], "source": [] @@ -295,7 +346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.3" } }, "nbformat": 4,