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Update fidelity
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‎final/hls4ml.ipynb

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@@ -491,6 +491,7 @@
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"\n",
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"# compute fidelity \n",
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"keras_fidelity = 0.5*(e_accuracy + g_accuracy)\n",
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"keras_fidelity = keras_fidelity*2-1\n",
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"keras_fidelity = 1/2 + (0.5*keras_fidelity)\n",
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"\n",
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"\n",
@@ -512,6 +513,7 @@
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"\n",
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"# compute fidelity \n",
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"hls_fidelity = 0.5*(e_accuracy + g_accuracy)\n",
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"hls_fidelity = hls_fidelity*2-1\n",
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"hls_fidelity = 1/2 + (0.5*hls_fidelity)\n",
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"\n",
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"\n",

‎final/train.ipynb

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@@ -2,21 +2,21 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
12-
"2024-12-02 10:50:03.729385: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA\n",
12+
"2024-12-02 12:11:43.237267: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
14-
"2024-12-02 10:50:03.793060: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
15-
"2024-12-02 10:50:03.794942: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
16-
"2024-12-02 10:50:03.794954: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
17-
"2024-12-02 10:50:06.943195: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
18-
"2024-12-02 10:50:06.943319: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
19-
"2024-12-02 10:50:06.943333: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
14+
"2024-12-02 12:11:43.289566: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
15+
"2024-12-02 12:11:43.291412: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
16+
"2024-12-02 12:11:43.291421: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
17+
"2024-12-02 12:11:43.592090: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
18+
"2024-12-02 12:11:43.592117: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
19+
"2024-12-02 12:11:43.592120: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
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]
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}
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],
@@ -56,7 +56,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -69,7 +69,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -94,7 +94,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
@@ -144,10 +144,10 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2024-12-02 10:51:09.801613: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
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"2024-12-02 10:51:09.801630: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
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"2024-12-02 10:51:09.801640: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (correlator7.fnal.gov): /proc/driver/nvidia/version does not exist\n",
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"2024-12-02 10:51:09.801767: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA\n",
147+
"2024-12-02 12:12:42.530268: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
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"2024-12-02 12:12:42.530285: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
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"2024-12-02 12:12:42.530294: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (correlator7.fnal.gov): /proc/driver/nvidia/version does not exist\n",
150+
"2024-12-02 12:12:42.530419: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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},
@@ -206,7 +206,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -230,46 +230,9 @@
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/50\n",
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"3340/3340 [==============================] - 9s 2ms/step - loss: 0.1959 - accuracy: 0.9558 - val_loss: 0.1810 - val_accuracy: 0.9581\n",
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"Epoch 2/50\n",
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"3340/3340 [==============================] - 10s 3ms/step - loss: 0.1802 - accuracy: 0.9592 - val_loss: 0.1770 - val_accuracy: 0.9583\n",
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"Epoch 3/50\n",
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"3340/3340 [==============================] - 3s 800us/step - loss: 0.1787 - accuracy: 0.9595 - val_loss: 0.1783 - val_accuracy: 0.9584\n",
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"Epoch 4/50\n",
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"3340/3340 [==============================] - 8s 2ms/step - loss: 0.1782 - accuracy: 0.9598 - val_loss: 0.1776 - val_accuracy: 0.9585\n",
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"Epoch 5/50\n",
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"3340/3340 [==============================] - 9s 3ms/step - loss: 0.1792 - accuracy: 0.9593 - val_loss: 0.1770 - val_accuracy: 0.9583\n",
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"Epoch 6/50\n",
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"3340/3340 [==============================] - 6s 2ms/step - loss: 0.1791 - accuracy: 0.9593 - val_loss: 0.1796 - val_accuracy: 0.9582\n",
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"Epoch 7/50\n",
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"3340/3340 [==============================] - 6s 2ms/step - loss: 0.1784 - accuracy: 0.9598 - val_loss: 0.1783 - val_accuracy: 0.9588\n",
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"Epoch 8/50\n",
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"3340/3340 [==============================] - 2s 671us/step - loss: 0.1779 - accuracy: 0.9597 - val_loss: 0.1783 - val_accuracy: 0.9584\n",
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"Epoch 9/50\n",
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"3340/3340 [==============================] - 6s 2ms/step - loss: 0.1779 - accuracy: 0.9601 - val_loss: 0.1783 - val_accuracy: 0.9586\n",
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"Epoch 10/50\n",
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"3340/3340 [==============================] - 11s 3ms/step - loss: 0.1778 - accuracy: 0.9599 - val_loss: 0.1777 - val_accuracy: 0.9587\n",
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"Epoch 11/50\n",
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"3340/3340 [==============================] - 8s 2ms/step - loss: 0.1778 - accuracy: 0.9599 - val_loss: 0.1775 - val_accuracy: 0.9584\n",
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"Epoch 12/50\n",
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"3340/3340 [==============================] - 11s 3ms/step - loss: 0.1779 - accuracy: 0.9599 - val_loss: 0.1781 - val_accuracy: 0.9583\n",
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"Epoch 13/50\n",
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"3340/3340 [==============================] - 11s 3ms/step - loss: 0.1779 - accuracy: 0.9599 - val_loss: 0.1779 - val_accuracy: 0.9584\n",
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"Epoch 14/50\n",
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"3340/3340 [==============================] - 6s 2ms/step - loss: 0.1779 - accuracy: 0.9599 - val_loss: 0.1775 - val_accuracy: 0.9586\n",
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"Epoch 15/50\n",
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"3340/3340 [==============================] - 8s 2ms/step - loss: 0.1778 - accuracy: 0.9600 - val_loss: 0.1772 - val_accuracy: 0.9585\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"train = True\n",
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"train = False\n",
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"\n",
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"if train: \n",
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" opt = Adam(learning_rate=init_learning_rate)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1563/1563 [==============================] - 1s 484us/step\n",
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"1563/1563 [==============================] - 1s 359us/step\n",
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"3125/3125 [==============================] - 1s 391us/step\n",
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"1563/1563 [==============================] - 1s 406us/step\n",
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"1563/1563 [==============================] - 1s 389us/step\n",
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"3125/3125 [==============================] - 1s 385us/step\n",
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"\n",
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"===================================\n",
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" Accuracy 0.95999\n",
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" Fidelity 0.979995\n"
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" Fidelity 0.9599899999999999\n"
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]
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}
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],
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"\n",
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"# compute fidelity \n",
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"test_fidelity = 0.5*(e_accuracy + g_accuracy)\n",
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"test_fidelity = test_fidelity*2-1\n",
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"test_fidelity = 1/2 + (0.5*test_fidelity)\n",
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"\n",
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"y_pred = model.predict(X_test)\n",
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"print(' Accuracy', test_acc)\n",
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"print(' Fidelity', test_fidelity)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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