commit 1d79956eaccb660a8388c97a8eb97a338bf40759
parent 4cdae5d45dfb652479e5e61e700d8975d9b31e84
Author: MTRNord <mtrnord1@gmail.com>
Date: Sun, 25 Sep 2022 02:27:22 +0200
Try increasing max_length and fix gitignore
Diffstat:
8 files changed, 52 insertions(+), 40 deletions(-)
diff --git a/.gitignore b/.gitignore
@@ -1 +1 @@
-./training_checkpoints
-\ No newline at end of file
+/training_checkpoints
+\ No newline at end of file
diff --git a/logs/scalars/20220925-022112/train/events.out.tfevents.1664065272.DESKTOP-GS9C68O.4056.0.v2 b/logs/scalars/20220925-022112/train/events.out.tfevents.1664065272.DESKTOP-GS9C68O.4056.0.v2
Binary files differ.
diff --git a/logs/scalars/20220925-022112/validation/events.out.tfevents.1664065275.DESKTOP-GS9C68O.4056.1.v2 b/logs/scalars/20220925-022112/validation/events.out.tfevents.1664065275.DESKTOP-GS9C68O.4056.1.v2
Binary files differ.
diff --git a/models/spam_keras_1664065523.4707785/keras_metadata.pb b/models/spam_keras_1664065523.4707785/keras_metadata.pb
@@ -0,0 +1,3 @@
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+size 12508
diff --git a/models/spam_keras_1664065523.4707785/saved_model.pb b/models/spam_keras_1664065523.4707785/saved_model.pb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a249044379d9e568daf38a5cefd3b0d7e78cd616ff14ccb18e02b3cdd8019be
+size 143705
diff --git a/models/spam_keras_1664065523.4707785/variables/variables.data-00000-of-00001 b/models/spam_keras_1664065523.4707785/variables/variables.data-00000-of-00001
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3fa795046b687d055982a67a1dc08dba52c4f2eecdf812c30bb2fe40e07b9f5
+size 201244
diff --git a/models/spam_keras_1664065523.4707785/variables/variables.index b/models/spam_keras_1664065523.4707785/variables/variables.index
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+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 02:12:57.754830: 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 02:21:08.685984: 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 02:12:57.911046: 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 02:12:58.471798: 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 02:12:58.471922: 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 02:12:58.471930: 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 02:21:08.863328: 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 02:21:09.573402: 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 02:21:09.573501: 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 02:21:09.573508: 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"
]
}
],
@@ -83,9 +83,9 @@
"metadata": {},
"outputs": [],
"source": [
- "vocab_size = 600\n",
+ "vocab_size = 1000\n",
"embedding_dim = 16\n",
- "max_length = 70\n",
+ "max_length = 120\n",
"trunc_type = 'post'\n",
"padding_type = 'post'\n",
"oov_tok = \"<OOV>\"\n",
@@ -122,9 +122,9 @@
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
- " embedding (Embedding) (None, 70, 16) 9600 \n",
+ " embedding (Embedding) (None, 120, 16) 16000 \n",
" \n",
- " dropout (Dropout) (None, 70, 16) 0 \n",
+ " dropout (Dropout) (None, 120, 16) 0 \n",
" \n",
" global_average_pooling1d (G (None, 16) 0 \n",
" lobalAveragePooling1D) \n",
@@ -138,8 +138,8 @@
" dense_1 (Dense) (None, 1) 7 \n",
" \n",
"=================================================================\n",
- "Total params: 9,709\n",
- "Trainable params: 9,709\n",
+ "Total params: 16,109\n",
+ "Trainable params: 16,109\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
@@ -148,28 +148,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-09-25 02:12:59.779036: 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 02:21:11.260113: 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 02:12:59.820332: 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 02:21:11.305803: 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 02:12:59.820421: 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 02:21:11.305864: 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 02:12:59.821746: 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 02:21:11.306781: 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 02:12:59.823061: 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 02:21:11.307643: 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 02:12:59.823218: 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 02:21:11.307698: 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 02:12:59.823453: 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 02:21:11.307727: 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 02:13:00.488835: 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 02:21:12.052383: 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 02:13:00.488922: 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 02:21:12.052503: 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 02:13:00.488929: 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 02:13:00.488992: 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 02:21:12.052514: 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 02:21:12.052567: 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 02:13:00.489040: 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 02:21:12.052612: 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"
]
}
],
@@ -223,7 +223,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Average test loss: 0.06312644314020872\n"
+ "Average test loss: 0.08333737960085273\n"
]
}
],
@@ -251,12 +251,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "INFO:tensorflow:Assets written to: ./models/spam_keras_1664065030.5260375/assets\n"
+ "INFO:tensorflow:Assets written to: ./models/spam_keras_1664065523.4707785/assets\n"
]
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -290,49 +290,49 @@
"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']\n",
- "1/1 [==============================] - 0s 63ms/step\n",
+ "1/1 [==============================] - 0s 65ms/step\n",
"Greg, can you call me back once you get this?\n",
- "[1.3981993e-07]\n",
+ "[0.00298078]\n",
"\n",
"\n",
"Congrats on your new iPhone! Click here to claim your prize...\n",
- "[0.9567402]\n",
+ "[0.99888474]\n",
"\n",
"\n",
"Really like that new photo of you\n",
- "[2.2730525e-05]\n",
+ "[0.00580836]\n",
"\n",
"\n",
"Did you hear the news today? Terrible what has happened...\n",
- "[6.766815e-05]\n",
+ "[0.0059419]\n",
"\n",
"\n",
"Attend this free COVID webinar today: Book your session now...\n",
- "[0.00327049]\n",
+ "[0.25335604]\n",
"\n",
"\n",
"Are you coming to the party tonight?\n",
- "[1.1324869e-08]\n",
+ "[0.00014567]\n",
"\n",
"\n",
"Your parcel has gone missing\n",
- "[1.4760855e-05]\n",
+ "[0.00142341]\n",
"\n",
"\n",
"Do not forget to bring friends!\n",
- "[2.1756257e-09]\n",
+ "[0.0004853]\n",
"\n",
"\n",
"You have won a million dollars! Fill out your bank details here...\n",
- "[0.00144691]\n",
+ "[0.6658791]\n",
"\n",
"\n",
"Looking forward to seeing you again\n",
- "[5.9962156e-05]\n",
+ "[0.00312363]\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",
- "[1.214179e-09]\n",
+ "[0.00045932]\n",
"\n",
"\n"
]
