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tfp-distribution-unsubscriptable.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyMMFjuTV9G8La0eHWfT+ETc",
"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/i418c/d58f02b7f89adf4511792e12cd5ea65f/tfp-distribution-unsubscriptable.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!pip uninstall -y tensorflow tensorflow_probability\n",
"!pip install tf-nightly tfp-nightly"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NWMS4SXpIJsU",
"outputId": "a7d67d03-989e-46da-ce0c-2dee340b1d4c"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found existing installation: tensorflow 2.15.0\n",
"Uninstalling tensorflow-2.15.0:\n",
" Successfully uninstalled tensorflow-2.15.0\n",
"Found existing installation: tensorflow-probability 0.22.0\n",
"Uninstalling tensorflow-probability-0.22.0:\n",
" Successfully uninstalled tensorflow-probability-0.22.0\n",
"Collecting tf-nightly\n",
" Downloading tf_nightly-2.16.0.dev20240106-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (566.2 MB)\n",
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"Collecting h5py>=3.10.0 (from tf-nightly)\n",
" Downloading h5py-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)\n",
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" Downloading ml_dtypes-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n",
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"Collecting tb-nightly~=2.16.0.a (from tf-nightly)\n",
" Downloading tb_nightly-2.16.0a20240108-py3-none-any.whl (5.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m104.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting tf-estimator-nightly~=2.14.0.dev (from tf-nightly)\n",
" Downloading tf_estimator_nightly-2.14.0.dev2023080308-py2.py3-none-any.whl (440 kB)\n",
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"\u001b[?25hCollecting keras-nightly~=3.0.0.dev (from tf-nightly)\n",
" Downloading keras_nightly-3.0.3.dev2024010803-py3-none-any.whl (1.0 MB)\n",
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"\u001b[?25hRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tf-nightly) (0.35.0)\n",
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"Collecting namex (from keras-nightly~=3.0.0.dev->tf-nightly)\n",
" Downloading namex-0.0.7-py3-none-any.whl (5.8 kB)\n",
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"Collecting tf-keras-nightly (from tb-nightly~=2.16.0.a->tf-nightly)\n",
" Downloading tf_keras_nightly-2.16.0.dev2024010810-py3-none-any.whl (1.7 MB)\n",
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"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tb-nightly~=2.16.0.a->tf-nightly) (1.3.1)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tb-nightly~=2.16.0.a->tf-nightly) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tb-nightly~=2.16.0.a->tf-nightly) (2.0.7)\n",
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"Installing collected packages: namex, tfp-nightly, tf-keras-nightly, tf-estimator-nightly, ml-dtypes, h5py, keras-nightly, tb-nightly, tf-nightly\n",
" Attempting uninstall: ml-dtypes\n",
" Found existing installation: ml-dtypes 0.2.0\n",
" Uninstalling ml-dtypes-0.2.0:\n",
" Successfully uninstalled ml-dtypes-0.2.0\n",
" Attempting uninstall: h5py\n",
" Found existing installation: h5py 3.9.0\n",
" Uninstalling h5py-3.9.0:\n",
" Successfully uninstalled h5py-3.9.0\n",
"Successfully installed h5py-3.10.0 keras-nightly-3.0.3.dev2024010803 ml-dtypes-0.3.2 namex-0.0.7 tb-nightly-2.16.0a20240108 tf-estimator-nightly-2.14.0.dev2023080308 tf-keras-nightly-2.16.0.dev2024010810 tf-nightly-2.16.0.dev20240106 tfp-nightly-0.24.0.dev20240108\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "tpaR6JRyHHfI",
"outputId": "af354477-848b-4209-f7cb-9d0686b14f11",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.16.0-dev20240106\n",
"0.24.0-dev20240108\n"
]
}
],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import tensorflow_probability as tfp\n",
"from tensorflow import keras\n",
"\n",
"print(tf.__version__)\n",
"print(tfp.__version__)\n"
]
},
{
"cell_type": "code",
"source": [
"encoded_size=2\n",
"\n",
"class EncoderDecoder(keras.Layer):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.dense1=keras.layers.Dense(128, activation='relu')\n",
" self.dense2=keras.layers.Dense(tfp.layers.IndependentNormal.params_size(encoded_size))\n",
" self.dist=tfp.layers.IndependentNormal(event_shape=[encoded_size],validate_args=True)\n",
"\n",
" def call(self,inputs):\n",
" x=self.dense1(inputs=inputs)\n",
" x=self.dense2(x)\n",
" return self.dist(x)\n",
"\n",
"class AutoEncoder(keras.Model):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.encoder = EncoderDecoder()\n",
" self.decoder = EncoderDecoder()\n",
"\n",
" def call(self, x):\n",
" encoded = self.encoder(x)\n",
" decoded = self.decoder(inputs=encoded)\n",
" return decoded\n",
"\n",
"def negative_log_likelihood(y_true, y_pred):\n",
" return -y_pred.log_prob(y_true)"
],
"metadata": {
"id": "oKaB8_XWKQvk"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"batch_size=32\n",
"output_dim=2\n",
"\n",
"train=np.random.rand(batch_size,40,5)\n",
"verify=np.random.rand(batch_size,10,output_dim)"
],
"metadata": {
"id": "qmGP6BkSN8dW"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model=AutoEncoder()\n",
"model.compile(optimizer=tf.keras.optimizers.Adam(\n",
" learning_rate=0.0001), loss=negative_log_likelihood,\n",
" run_eagerly=False, jit_compile=False)"
],
"metadata": {
"id": "XosLavY5LLCj"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model.fit(train,verify,epochs=10,batch_size=batch_size)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"id": "1c-723fFRBtr",
"outputId": "25f99fe0-17a4-4b85-ac4c-6877c5610124"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n"
]
},
{
"output_type": "error",
"ename": "TypeError",
"evalue": "Exception encountered when calling EncoderDecoder.call().\n\n\u001b[1m'NoneType' object is not subscriptable\u001b[0m\n\nArguments received by EncoderDecoder.call():\n • inputs=<tfp.distributions._TensorCoercible 'tensor_coercible' batch_shape=[32, 40] event_shape=[2] dtype=float32>",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-ed773d6be14e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mverify\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\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/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\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 125\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-5-758248108003>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\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 22\u001b[0m \u001b[0mencoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mdecoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoded\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 24\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecoded\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-5-758248108003>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcall\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[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdense1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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[0m\u001b[1;32m 12\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdense2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Exception encountered when calling EncoderDecoder.call().\n\n\u001b[1m'NoneType' object is not subscriptable\u001b[0m\n\nArguments received by EncoderDecoder.call():\n • inputs=<tfp.distributions._TensorCoercible 'tensor_coercible' batch_shape=[32, 40] event_shape=[2] dtype=float32>"
]
}
]
}
]
}
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