Skip to content

Instantly share code, notes, and snippets.

@hguandl

hguandl/mv3.ipynb

Last active October 21, 2021 21:07
Show Gist options
  • Save hguandl/093dff3bcf33383f916f6439e2218769 to your computer and use it in GitHub Desktop.
Save hguandl/093dff3bcf33383f916f6439e2218769 to your computer and use it in GitHub Desktop.
Download ZIP
Reproduction of MobileNetV3 error
Display the source blob
Display the rendered blob
Raw
mv3.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled1.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyMSt8ZLF9be9OZASZdfW6PL",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/hguandl/093dff3bcf33383f916f6439e2218769/untitled1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YNo9JVNiqB9W",
"outputId": "ad2a6708-6ce4-4851-af8f-85d3e6828a97"
},
"source": [
"!pip install tensorflow_model_optimization"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting tensorflow_model_optimization\n",
" Downloading tensorflow_model_optimization-0.7.0-py2.py3-none-any.whl (213 kB)\n",
"\u001b[?25l\r\u001b[K |█▌ | 10 kB 19.8 MB/s eta 0:00:01\r\u001b[K |███ | 20 kB 26.8 MB/s eta 0:00:01\r\u001b[K |████▋ | 30 kB 30.9 MB/s eta 0:00:01\r\u001b[K |██████▏ | 40 kB 21.3 MB/s eta 0:00:01\r\u001b[K |███████▊ | 51 kB 8.2 MB/s eta 0:00:01\r\u001b[K |█████████▎ | 61 kB 8.7 MB/s eta 0:00:01\r\u001b[K |██████████▊ | 71 kB 8.0 MB/s eta 0:00:01\r\u001b[K |████████████▎ | 81 kB 8.8 MB/s eta 0:00:01\r\u001b[K |█████████████▉ | 92 kB 8.8 MB/s eta 0:00:01\r\u001b[K |███████████████▍ | 102 kB 7.7 MB/s eta 0:00:01\r\u001b[K |█████████████████ | 112 kB 7.7 MB/s eta 0:00:01\r\u001b[K |██████████████████▌ | 122 kB 7.7 MB/s eta 0:00:01\r\u001b[K |████████████████████ | 133 kB 7.7 MB/s eta 0:00:01\r\u001b[K |█████████████████████▌ | 143 kB 7.7 MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 153 kB 7.7 MB/s eta 0:00:01\r\u001b[K |████████████████████████▋ | 163 kB 7.7 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▏ | 174 kB 7.7 MB/s eta 0:00:01\r\u001b[K |███████████████████████████▊ | 184 kB 7.7 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▏ | 194 kB 7.7 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▊ | 204 kB 7.7 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 213 kB 7.7 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy~=1.14 in /usr/local/lib/python3.7/dist-packages (from tensorflow_model_optimization) (1.19.5)\n",
"Requirement already satisfied: dm-tree~=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow_model_optimization) (0.1.6)\n",
"Requirement already satisfied: six~=1.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow_model_optimization) (1.15.0)\n",
"Installing collected packages: tensorflow-model-optimization\n",
"Successfully installed tensorflow-model-optimization-0.7.0\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zrmBn5L4qTSO"
},
"source": [
"from tensorflow.keras.applications import MobileNetV3Small as MV3\n",
"import tensorflow_model_optimization as tfmo\n"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 446
},
"id": "a1EmaZe1qbRf",
"outputId": "e38c6d07-20ba-474d-f4ac-91bd7dd263dc"
},
"source": [
"model_mv3 = MV3(include_top=True, weights='imagenet', input_shape=(224, 224, 3))\n",
"\n",
"q_aware_model = tfmo.quantization.keras.quantize_model(model_mv3)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v3/weights_mobilenet_v3_small_224_1.0_float.h5\n",
"10739712/10734624 [==============================] - 0s 0us/step\n",
"10747904/10734624 [==============================] - 0s 0us/step\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
" category=CustomMaskWarning)\n"
]
},
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-ce2987eb651c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmodel_mv3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMV3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagenet'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mq_aware_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfmo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_mv3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py\u001b[0m in \u001b[0;36mquantize_model\u001b[0;34m(to_quantize)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0mannotated_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquantize_annotate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_quantize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mquantize_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mannotated_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/keras/metrics.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_gauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMonitorBoolGauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FAILURE_LABEL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_gauge\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/keras/metrics.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_gauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMonitorBoolGauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SUCCESS_LABEL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py\u001b[0m in \u001b[0;36mquantize_apply\u001b[0;34m(model, scheme)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0;31m# stores relevant quantization information in a map.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 421\u001b[0m (unwrapped_model, layer_quantize_map,\n\u001b[0;32m--> 422\u001b[0;31m requires_output_quantize) = _extract_original_model(model_copy)\n\u001b[0m\u001b[1;32m 423\u001b[0m \u001b[0;31m# Model cloning excludes input layers. Add input layers into the map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0;31m# since they need to be matched for patterns as well.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py\u001b[0m in \u001b[0;36m_extract_original_model\u001b[0;34m(model_to_unwrap)\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m unwrapped_model = keras.models.clone_model(\n\u001b[0;32m--> 366\u001b[0;31m model_to_unwrap, input_tensors=None, clone_function=_unwrap)\n\u001b[0m\u001b[1;32m 367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0munwrapped_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_quantize_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequires_output_quantize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/models.py\u001b[0m in \u001b[0;36mclone_model\u001b[0;34m(model, input_tensors, clone_function)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m return _clone_functional_model(\n\u001b[0;32m--> 452\u001b[0;31m model, input_tensors=input_tensors, layer_fn=clone_function)\n\u001b[0m\u001b[1;32m 453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/models.py\u001b[0m in \u001b[0;36m_clone_functional_model\u001b[0;34m(model, input_tensors, layer_fn)\u001b[0m\n\u001b[1;32m 194\u001b[0m input_tensors, output_tensors, created_layers = (\n\u001b[1;32m 195\u001b[0m functional.reconstruct_from_config(model_configs,\n\u001b[0;32m--> 196\u001b[0;31m created_layers=created_layers))\n\u001b[0m\u001b[1;32m 197\u001b[0m \u001b[0mmetrics_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py\u001b[0m in \u001b[0;36mreconstruct_from_config\u001b[0;34m(config, custom_objects, created_layers)\u001b[0m\n\u001b[1;32m 1281\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0munprocessed_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1282\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode_data\u001b[0m \u001b[0;32min\u001b[0m \u001b[0munprocessed_nodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1283\u001b[0;31m \u001b[0mprocess_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1285\u001b[0m \u001b[0minput_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py\u001b[0m in \u001b[0;36mprocess_node\u001b[0;34m(layer, node_data)\u001b[0m\n\u001b[1;32m 1229\u001b[0m input_tensors = (\n\u001b[1;32m 1230\u001b[0m base_layer_utils.unnest_if_single_tensor(input_tensors))\n\u001b[0;32m-> 1231\u001b[0;31m \u001b[0moutput_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1233\u001b[0m \u001b[0;31m# Update node index map.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 975\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 976\u001b[0m return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0;32m--> 977\u001b[0;31m input_list)\n\u001b[0m\u001b[1;32m 978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[0;31m# Maintains info about the `Layer.call` stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m 1113\u001b[0m \u001b[0;31m# Check input assumptions set after layer building, e.g. input shape.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1114\u001b[0m outputs = self._keras_tensor_symbolic_call(\n\u001b[0;32m-> 1115\u001b[0;31m inputs, input_masks, args, kwargs)\n\u001b[0m\u001b[1;32m 1116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1117\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_keras_tensor_symbolic_call\u001b[0;34m(self, inputs, input_masks, args, kwargs)\u001b[0m\n\u001b[1;32m 846\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeras_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKerasTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_signature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 847\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_infer_output_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_infer_output_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_masks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_infer_output_signature\u001b[0;34m(self, inputs, args, kwargs, input_masks)\u001b[0m\n\u001b[1;32m 886\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 888\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 890\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_activity_regularization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/layers/core.py\u001b[0m in \u001b[0;36m_call_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1348\u001b[0m \u001b[0;31m# Decorate the function to produce this layer's call method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1349\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1351\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__internal__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_decorator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_call_wrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/layers/core.py\u001b[0m in \u001b[0;36m_call_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1380\u001b[0m \u001b[0;31m# multiple ops w/ the same name when the layer is reused)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1381\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1383\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcreated_variables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwatched_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1384\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36m_add_dispatch\u001b[0;34m(x, y, name)\u001b[0m\n\u001b[1;32m 1694\u001b[0m if not isinstance(y, ops.Tensor) and not isinstance(\n\u001b[1;32m 1695\u001b[0m y, sparse_tensor.SparseTensor):\n\u001b[0;32m-> 1696\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype_hint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1697\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1698\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgen_math_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'dtype'"
]
}
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment