commit 67602639ae9cc4f8107178ef338b3eebc8a8ac28 parent cbb0817428b90522255712aaf9b4f2ac6c9ab67d Author: MTRNord <mtrnord1@gmail.com> Date: Sun, 25 Sep 2022 16:01:52 +0200 Add new models and training data Diffstat:
18 files changed, 88 insertions(+), 42 deletions(-)
diff --git a/input/MatrixData b/input/MatrixData @@ -5593,4 +5593,6 @@ ham hosting fluffychat web is weird tho, you need files from the build/ dir ham hey guys I'm trying to build an ARM build of a Flutter app from a Linux distro running under UTM on an M1 Macbook unfortunately, building on ARM seems to yield a blank window, both for the debug build and the release build: how do you guys build for Linux on ARM? spam Congrats on your new iPhone! Click here to claim your prize... ham Greg, can you call me back once you get this? -ham I'm writing a bot. I want to be able to provide the bot with the cross signing recovery key from Element, then have it grab all the room keys from the server. Is that currently possible and if so how? -\ No newline at end of file +ham I'm writing a bot. I want to be able to provide the bot with the cross signing recovery key from Element, then have it grab all the room keys from the server. Is that currently possible and if so how? +ham O thanks, i'll take a look +ham Hello all you Element experts, I'm setting up a homelab with Synapse and Element-web. I have read that there are security downsides in hosting both on the same domain. Can someone confirm whether it is still true, even on different subdomains ? +\ No newline at end of file diff --git a/logs/scalars/20220925-154904/train/events.out.tfevents.1664113744.DESKTOP-GS9C68O.8421.0.v2 b/logs/scalars/20220925-154904/train/events.out.tfevents.1664113744.DESKTOP-GS9C68O.8421.0.v2 Binary files differ. diff --git a/logs/scalars/20220925-154904/validation/events.out.tfevents.1664113746.DESKTOP-GS9C68O.8421.1.v2 b/logs/scalars/20220925-154904/validation/events.out.tfevents.1664113746.DESKTOP-GS9C68O.8421.1.v2 Binary files differ. diff --git a/logs/scalars/20220925-155349/train/events.out.tfevents.1664114029.DESKTOP-GS9C68O.9962.0.v2 b/logs/scalars/20220925-155349/train/events.out.tfevents.1664114029.DESKTOP-GS9C68O.9962.0.v2 Binary files differ. diff --git a/logs/scalars/20220925-155349/validation/events.out.tfevents.1664114031.DESKTOP-GS9C68O.9962.1.v2 b/logs/scalars/20220925-155349/validation/events.out.tfevents.1664114031.DESKTOP-GS9C68O.9962.1.v2 Binary files differ. diff --git a/logs/scalars/20220925-155531/train/events.out.tfevents.1664114131.DESKTOP-GS9C68O.10778.0.v2 b/logs/scalars/20220925-155531/train/events.out.tfevents.1664114131.DESKTOP-GS9C68O.10778.0.v2 Binary files differ. diff --git a/logs/scalars/20220925-155531/validation/events.out.tfevents.1664114133.DESKTOP-GS9C68O.10778.1.v2 b/logs/scalars/20220925-155531/validation/events.out.tfevents.1664114133.DESKTOP-GS9C68O.10778.1.v2 Binary files differ. diff --git a/logs/scalars/20220925-155633/train/events.out.tfevents.1664114193.DESKTOP-GS9C68O.11193.0.v2 b/logs/scalars/20220925-155633/train/events.out.tfevents.1664114193.DESKTOP-GS9C68O.11193.0.v2 Binary files differ. diff --git a/logs/scalars/20220925-155633/validation/events.out.tfevents.1664114195.DESKTOP-GS9C68O.11193.1.v2 b/logs/scalars/20220925-155633/validation/events.out.tfevents.1664114195.DESKTOP-GS9C68O.11193.1.v2 Binary files differ. diff --git a/models/spam_keras_1664113981.8498716/keras_metadata.pb b/models/spam_keras_1664113981.8498716/keras_metadata.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e77b605f3f16ba228cfd9967c843f229998da2d32e4d689594d2419cd490a8a +size 11458 diff --git a/models/spam_keras_1664113981.8498716/saved_model.pb b/models/spam_keras_1664113981.8498716/saved_model.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7167a96c1d08d420bb4020e24949687c75ef4386ccd9a592eabbd9067bc4dfea +size 124581 diff --git a/models/spam_keras_1664113981.8498716/variables/variables.data-00000-of-00001 b/models/spam_keras_1664113981.8498716/variables/variables.data-00000-of-00001 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db38be800f4dbb277cf3f25ca017e7eb3702a56db034ab56bf7e03192d6dc3c8 +size 200607 diff --git a/models/spam_keras_1664113981.8498716/variables/variables.index b/models/spam_keras_1664113981.8498716/variables/variables.index @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:875cf3b516f89668dfb5f555f5996550d62c3a0fae3162d6d6d3ee36c677c25b +size 1636 diff --git a/models/spam_keras_1664114429.671611/keras_metadata.pb b/models/spam_keras_1664114429.671611/keras_metadata.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e77b605f3f16ba228cfd9967c843f229998da2d32e4d689594d2419cd490a8a +size 11458 diff --git a/models/spam_keras_1664114429.671611/saved_model.pb b/models/spam_keras_1664114429.671611/saved_model.pb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7167a96c1d08d420bb4020e24949687c75ef4386ccd9a592eabbd9067bc4dfea +size 124581 diff --git a/models/spam_keras_1664114429.671611/variables/variables.data-00000-of-00001 b/models/spam_keras_1664114429.671611/variables/variables.data-00000-of-00001 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9d90c5b0c7609705736e0bbe098a3b816a86d55cb22d0f80674eec3c387b899 +size 200607 diff --git a/models/spam_keras_1664114429.671611/variables/variables.index b/models/spam_keras_1664114429.671611/variables/variables.index @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8f6c407532d75e3182f186101971c01e2bc92b711cb539e6d06dd97718c5744 +size 1636 diff --git a/spam-keras.ipynb b/spam-keras.ipynb @@ -17,12 +17,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-09-25 15:42:34.455982: 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 FMA\n", + "2022-09-25 15:56:29.432992: 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 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2022-09-25 15:42:34.619065: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2022-09-25 15:42:35.129795: W tensorflow/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", - "2022-09-25 15:42:35.129895: W tensorflow/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", - "2022-09-25 15:42:35.129902: 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" + "2022-09-25 15:56:29.572721: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2022-09-25 15:56:30.191724: W tensorflow/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", + "2022-09-25 15:56:30.191840: W tensorflow/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", + "2022-09-25 15:56:30.191847: 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" ] } ], @@ -178,28 +178,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-09-25 15:42:37.031499: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.147052: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.068347: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.176653: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.068423: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.176739: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.069082: 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 FMA\n", + "2022-09-25 15:56:32.177492: 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 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2022-09-25 15:42:37.069501: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.178205: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.069558: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.178280: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.069594: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.178320: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.641729: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.733186: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.641835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.733276: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.641843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", - "2022-09-25 15:42:37.641890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:56:32.733285: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2022-09-25 15:56:32.733373: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 15:42:37.641934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5904 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 SUPER, pci bus id: 0000:09:00.0, compute capability: 7.5\n" + "2022-09-25 15:56:32.733424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5904 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 SUPER, pci bus id: 0000:09:00.0, compute capability: 7.5\n" ] } ], @@ -253,8 +253,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average train loss: 0.13753025684505701\n", - "Average test loss: 0.07057723039761185\n" + "Average train loss: 0.0944854474812746\n", + "Average test loss: 0.12808816809207202\n" ] } ], @@ -284,12 +284,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: ./models/spam_keras_1664113591.3325882/assets\n" + "INFO:tensorflow:Assets written to: ./models/spam_keras_1664114429.671611/assets\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -315,73 +315,89 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['Greg, can you call me back once you get this?', 'Congrats on your new iPhone! Click here to claim your prize...', 'Really like that new photo of you', 'Did you hear the news today? Terrible what has happened...', 'Attend this free COVID webinar today: Book your session now...', 'Are you coming to the party tonight?', 'Your parcel has gone missing', 'Do not forget to bring friends!', 'You have won a million dollars! Fill out your bank details here...', 'Looking forward to seeing you again', 'oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D', 'ayo', 'Almost all my spam is coming to my non-gmail address actually', 'Oh neat I think I found the sizing sweetspot for my data :D', 'would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail']\n", - "1/1 [==============================] - 0s 19ms/step\n", + "['Greg, can you call me back once you get this?', 'Congrats on your new iPhone! Click here to claim your prize...', 'Really like that new photo of you', 'Did you hear the news today? Terrible what has happened...', 'Attend this free COVID webinar today: Book your session now...', 'Are you coming to the party tonight?', 'Your parcel has gone missing', 'Do not forget to bring friends!', 'You have won a million dollars! Fill out your bank details here...', 'Looking forward to seeing you again', 'oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D', 'ayo', 'Almost all my spam is coming to my non-gmail address actually', 'Oh neat I think I found the sizing sweetspot for my data :D', 'would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail', 'back to cacophony ', 'Room version 11 when', 'skip 11 and go straight to 12', '100 events should clear out any events that might be causing a request to fail lol']\n", + "1/1 [==============================] - 0s 22ms/step\n", "Greg, can you call me back once you get this?\n", - "[0.00013209]\n", + "[1.527862e-05]\n", "\n", "\n", "Congrats on your new iPhone! Click here to claim your prize...\n", - "[1.]\n", + "[0.99988234]\n", "\n", "\n", "Really like that new photo of you\n", - "[2.3931017e-05]\n", + "[8.316931e-12]\n", "\n", "\n", "Did you hear the news today? Terrible what has happened...\n", - "[4.1954652e-05]\n", + "[4.049786e-12]\n", "\n", "\n", "Attend this free COVID webinar today: Book your session now...\n", - "[0.9865063]\n", + "[0.9273404]\n", "\n", "\n", "Are you coming to the party tonight?\n", - "[6.177529e-09]\n", + "[4.417985e-22]\n", "\n", "\n", "Your parcel has gone missing\n", - "[0.00260421]\n", + "[2.0575136e-08]\n", "\n", "\n", "Do not forget to bring friends!\n", - "[0.00025998]\n", + "[2.7286378e-09]\n", "\n", "\n", "You have won a million dollars! Fill out your bank details here...\n", - "[0.9999875]\n", + "[0.00021836]\n", "\n", "\n", "Looking forward to seeing you again\n", - "[0.00708251]\n", + "[2.4189583e-05]\n", "\n", "\n", "oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D\n", - "[4.980462e-14]\n", + "[6.2298964e-25]\n", "\n", "\n", "ayo\n", - "[0.00903365]\n", + "[6.080875e-05]\n", "\n", "\n", "Almost all my spam is coming to my non-gmail address actually\n", - "[7.743632e-12]\n", + "[5.039635e-35]\n", "\n", "\n", "Oh neat I think I found the sizing sweetspot for my data :D\n", - "[4.4713904e-08]\n", + "[1.8463455e-16]\n", "\n", "\n", "would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail\n", - "[4.2191292e-11]\n", + "[1.5138318e-19]\n", + "\n", + "\n", + "back to cacophony \n", + "[0.07821694]\n", + "\n", + "\n", + "Room version 11 when\n", + "[3.4843818e-12]\n", + "\n", + "\n", + "skip 11 and go straight to 12\n", + "[5.895254e-07]\n", + "\n", + "\n", + "100 events should clear out any events that might be causing a request to fail lol\n", + "[2.1740408e-13]\n", "\n", "\n" ] @@ -403,7 +419,11 @@ " 'ayo',\n", " 'Almost all my spam is coming to my non-gmail address actually',\n", " 'Oh neat I think I found the sizing sweetspot for my data :D',\n", - " 'would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail']\n", + " 'would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail',\n", + " 'back to cacophony ',\n", + " 'Room version 11 when',\n", + " 'skip 11 and go straight to 12',\n", + " '100 events should clear out any events that might be causing a request to fail lol']\n", "\n", "print(text_messages)\n", "\n",