Skip to content

Instantly share code, notes, and snippets.

@stefan-falk

stefan-falk/valueerror-no-gradients-provided-for-any-variable.ipynb Secret

Created April 20, 2020 13:32
Show Gist options
  • Save stefan-falk/42ef89c6636fd9f91fc471584659512f to your computer and use it in GitHub Desktop.
Save stefan-falk/42ef89c6636fd9f91fc471584659512f to your computer and use it in GitHub Desktop.
Download ZIP
ValueError: No gradients provided for any variable.ipynb
Display the source blob
Display the rendered blob
Raw
valueerror-no-gradients-provided-for-any-variable.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "ValueError: No gradients provided for any variable.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyN8IK0qwS/GZUQMsssLRPD9",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/stefan-falk/42ef89c6636fd9f91fc471584659512f/valueerror-no-gradients-provided-for-any-variable.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "n3Jetx5AizWr",
"colab_type": "code",
"colab": {}
},
"source": [
"# Github issue link: https://github.com/tensorflow/tensorflow/issues/38631"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1emoUQf1gOzQ",
"colab_type": "code",
"outputId": "0326f85e-0466-49a5-f8ac-ea253cb04662",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 768
}
},
"source": [
"!pip install tf-nightly==2.2.0.dev20200410"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting tf-nightly==2.2.0.dev20200410\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7c/49/a0dc7245b1a010ae9ed55336919df72ad551b200cb6a26fc6d22173e118e/tf_nightly-2.2.0.dev20200410-cp36-cp36m-manylinux2010_x86_64.whl (517.6MB)\n",
"\u001b[K |████████████████████████████████| 517.6MB 28kB/s \n",
"\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.1.0)\n",
"Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.18.2)\n",
"Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/69/88/61b9824a0fe10833714ce244be2d7e60f9b5b1d8d8ed7828f00c4c51f8d4/tb_nightly-2.3.0a20200416-py3-none-any.whl (3.0MB)\n",
"\u001b[K |████████████████████████████████| 3.0MB 48.4MB/s \n",
"\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.12.1)\n",
"Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.6.3)\n",
"Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.28.1)\n",
"Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (0.9.0)\n",
"Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (3.10.0)\n",
"Collecting tf-estimator-nightly\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/e5/f1/b61dcbe48e49ef2527b8b95906825765a4f0daa229c768f3a6001410a87b/tf_estimator_nightly-2.3.0.dev2020041701-py2.py3-none-any.whl (455kB)\n",
"\u001b[K |████████████████████████████████| 460kB 44.6MB/s \n",
"\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.1.0)\n",
"Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (2.10.0)\n",
"Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.4.1)\n",
"Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (0.2.0)\n",
"Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (0.3.3)\n",
"Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (1.12.0)\n",
"Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (0.34.2)\n",
"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly==2.2.0.dev20200410) (3.2.0)\n",
"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (1.6.0.post3)\n",
"Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (1.7.2)\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (2.21.0)\n",
"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (46.1.3)\n",
"Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (0.4.1)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (3.2.1)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (1.0.1)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (0.2.8)\n",
"Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (4.0)\n",
"Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (3.1.1)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (2020.4.5.1)\n",
"Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (1.24.3)\n",
"Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (2.8)\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (1.3.0)\n",
"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (0.4.8)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly==2.2.0.dev20200410) (3.1.0)\n",
"Installing collected packages: tb-nightly, tf-estimator-nightly, tf-nightly\n",
"Successfully installed tb-nightly-2.3.0a20200416 tf-estimator-nightly-2.3.0.dev2020041701 tf-nightly-2.2.0.dev20200410\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7PjQ9UBVgXwj",
"colab_type": "code",
"outputId": "32b046fa-77a7-4653-c34d-965c34c24877",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"import random\n",
"from functools import partial\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from tensorflow_datasets.core.features.text import SubwordTextEncoder\n",
"\n",
"\n",
"EOS = '<eos>'\n",
"PAD = '<pad>'\n",
"\n",
"RESERVED_TOKENS = [EOS, PAD]\n",
"EOS_ID = RESERVED_TOKENS.index(EOS)\n",
"PAD_ID = RESERVED_TOKENS.index(PAD)\n",
"\n",
"dictionary = [\n",
" 'verstehen',\n",
" 'verstanden',\n",
" 'vergessen',\n",
" 'verlegen',\n",
" 'verlernen',\n",
" 'vertun',\n",
" 'vertan',\n",
" 'verloren',\n",
" 'verlieren',\n",
" 'verlassen',\n",
" 'verhandeln',\n",
"]\n",
"\n",
"dictionary = [word.lower() for word in dictionary]\n",
"\n",
"\n",
"def get_model(params) -> keras.models.Model:\n",
"\n",
" inputs = layers.Input((None,), dtype=tf.int64, name='inputs')\n",
"\n",
" x = inputs\n",
"\n",
" vocab_size = params['vocab_size']\n",
" hidden_size = params['hidden_size']\n",
" max_input_length = params['max_input_length']\n",
" max_target_length = params['max_target_length']\n",
"\n",
" x = layers.Embedding(vocab_size, hidden_size, input_length=max_input_length)(x)\n",
"\n",
" # Encoder\n",
" x = layers.RNN(layers.GRUCell(hidden_size))(x)\n",
" x = layers.RepeatVector(max_target_length)(x)\n",
"\n",
" # Deoder\n",
" x = layers.RNN(layers.GRUCell(hidden_size), return_sequences=True)(x)\n",
" x = layers.TimeDistributed(layers.Dense(hidden_size, activation='relu'))(x)\n",
"\n",
" # Outputs\n",
" output_dense_layer = layers.Dense(vocab_size, activation='softmax')\n",
" outputs = layers.TimeDistributed(output_dense_layer, name='outputs')(x)\n",
"\n",
" return keras.models.Model(inputs=[inputs], outputs=[outputs])\n",
"\n",
"\n",
"def sample_generator(text_encoder: SubwordTextEncoder, max_sample: int = None):\n",
" count = 0\n",
"\n",
" while True:\n",
" random.shuffle(dictionary)\n",
"\n",
" for word in dictionary:\n",
"\n",
" for i in range(1, len(word)):\n",
"\n",
" inputs = word[:i]\n",
" targets = word\n",
"\n",
" example = dict(\n",
" inputs=text_encoder.encode(inputs) + [EOS_ID],\n",
" targets=text_encoder.encode(targets) + [EOS_ID],\n",
" )\n",
" count += 1\n",
"\n",
" yield example\n",
"\n",
" if max_sample is not None and count >= max_sample:\n",
" print('Reached max_samples (%d)' % max_sample)\n",
" return\n",
"\n",
"\n",
"def make_dataset(generator_fn, params, training):\n",
"\n",
" dataset = tf.data.Dataset.from_generator(\n",
" generator_fn,\n",
" output_types={\n",
" 'inputs': tf.int64,\n",
" 'targets': tf.int64,\n",
" }\n",
" )\n",
"\n",
" if training:\n",
" dataset = dataset.shuffle(100)\n",
"\n",
" dataset = dataset.padded_batch(\n",
" params['batch_size'],\n",
" padded_shapes={\n",
" 'inputs': (None,),\n",
" 'targets': (None,)\n",
" },\n",
" )\n",
"\n",
" if training:\n",
" dataset = dataset.map(lambda example: to_train_example(example, params=params)).repeat()\n",
"\n",
" return dataset\n",
"\n",
"\n",
"def to_train_example(example: dict, params: dict):\n",
" # Make sure targets are one-hot encoded\n",
" example['targets'] = tf.one_hot(example['targets'], depth=params['vocab_size'])\n",
" return example\n",
"\n",
"\n",
"def main():\n",
"\n",
" text_encoder = SubwordTextEncoder.build_from_corpus(\n",
" iter(dictionary),\n",
" target_vocab_size=1000,\n",
" max_subword_length=6,\n",
" reserved_tokens=RESERVED_TOKENS\n",
" )\n",
"\n",
" generator_fn = partial(sample_generator, text_encoder=text_encoder, max_sample=10)\n",
"\n",
" params = dict(\n",
" batch_size=20,\n",
" vocab_size=text_encoder.vocab_size,\n",
" hidden_size=32,\n",
" max_input_length=30,\n",
" max_target_length=30,\n",
" enable_metrics_in_training=True\n",
" )\n",
"\n",
" model = get_model(params)\n",
"\n",
" model.compile(\n",
" optimizer=keras.optimizers.Adam(0.001),\n",
" loss='categorical_crossentropy',\n",
" )\n",
"\n",
" assert len(model.trainable_variables), 'There are no trainable_variables'\n",
" model.summary()\n",
"\n",
" train_dataset = make_dataset(generator_fn, params, training=True)\n",
"\n",
" model.fit(\n",
" train_dataset,\n",
" epochs=5,\n",
" steps_per_epoch=100,\n",
" )\n",
"\n",
"main()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"model\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"inputs (InputLayer) [(None, None)] 0 \n",
"_________________________________________________________________\n",
"embedding (Embedding) (None, None, 32) 8864 \n",
"_________________________________________________________________\n",
"rnn (RNN) (None, 32) 6336 \n",
"_________________________________________________________________\n",
"repeat_vector (RepeatVector) (None, 30, 32) 0 \n",
"_________________________________________________________________\n",
"rnn_1 (RNN) (None, 30, 32) 6336 \n",
"_________________________________________________________________\n",
"time_distributed (TimeDistri (None, 30, 32) 1056 \n",
"_________________________________________________________________\n",
"outputs (TimeDistributed) (None, 30, 277) 9141 \n",
"=================================================================\n",
"Total params: 31,733\n",
"Trainable params: 31,733\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"Epoch 1/5\n"
],
"name": "stdout"
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-8e18fafee95c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 157\u001b[0m )\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m \u001b[0mmain\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[0m",
"\u001b[0;32m<ipython-input-2-8e18fafee95c>\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\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 157\u001b[0m )\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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 70\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;31m# Running inside `run_distribute_coordinator` already.\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/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m 918\u001b[0m batch_size=batch_size):\n\u001b[1;32m 919\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 920\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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 921\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 922\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\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/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 619\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\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 620\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--> 621\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\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[0mkwds\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 622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\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/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0minitializers\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--> 668\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\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 669\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[0;31m# At this point we know that the initialization is complete (or less\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/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m 546\u001b[0m self._concrete_stateful_fn = (\n\u001b[1;32m 547\u001b[0m self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access\n\u001b[0;32m--> 548\u001b[0;31m *args, **kwds))\n\u001b[0m\u001b[1;32m 549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\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/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2445\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2446\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2447\u001b[0;31m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2448\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 2773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2774\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2775\u001b[0;31m \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2776\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2777\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\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[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 2663\u001b[0m \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2664\u001b[0m \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2665\u001b[0;31m capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m 2666\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2667\u001b[0m \u001b[0;31m# Tell the ConcreteFunction to clean up its graph once it goes out of\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/python3.6/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\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 982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 983\u001b[0m \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\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/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\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[0mkwds\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 460\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\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--> 968\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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 969\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 970\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: in user code:\n\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:630 train_function *\n return step_function(self, iterator)\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:620 step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:952 run\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2292 call_for_each_replica\n return self._call_for_each_replica(fn, args, kwargs)\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2651 _call_for_each_replica\n return fn(*args, **kwargs)\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:613 run_step **\n outputs = model.train_step(data)\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:581 train_step\n self.trainable_variables)\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1902 _minimize\n trainable_variables))\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:554 _aggregate_gradients\n filtered_grads_and_vars = _filter_grads(grads_and_vars)\n /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1252 _filter_grads\n ([v.name for _, v in grads_and_vars],))\n\n ValueError: No gradients provided for any variable: ['embedding/embeddings:0', 'rnn/gru_cell/kernel:0', 'rnn/gru_cell/recurrent_kernel:0', 'rnn/gru_cell/bias:0', 'rnn_1/gru_cell_1/kernel:0', 'rnn_1/gru_cell_1/recurrent_kernel:0', 'rnn_1/gru_cell_1/bias:0', 'time_distributed/kernel:0', 'time_distributed/bias:0', 'outputs/kernel:0', 'outputs/bias:0'].\n"
]
}
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment