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nateraw/keras-model-card-demo.ipynb

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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/nateraw/2ffbd8baf8caecbbcfbdf78cda3a7090/keras-model-card-demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "EnzIkuOiebVK"
},
"outputs": [],
"source": [
"%%capture\n",
"! pip install git+https://github.com/huggingface/huggingface_hub.git@keras-model-card --upgrade\n",
"! apt install git-lfs\n",
"! git config --global credential.helper store"
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 332,
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"text": [
"Login successful\n",
"Your token has been saved to /root/.huggingface/token\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"from huggingface_hub import push_to_hub_keras\n",
"\n",
"model = tf.keras.models.Sequential()\n",
"model.add(tf.keras.layers.Dense(2, activation=\"relu\"))\n",
"model.compile(optimizer=\"adam\", loss=\"mse\")\n",
"\n",
"# Your model must be built in order to push it!\n",
"model.build((None, 2))\n",
"\n",
"push_to_hub_keras(model, 'keras-dummy-sequential-demo-with-card-2')"
],
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"base_uri": "https://localhost:8080/",
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{
"output_type": "stream",
"name": "stderr",
"text": [
"Cloning https://huggingface.co/nateraw/keras-dummy-sequential-demo-with-card-2 into local empty directory.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO:tensorflow:Assets written to: keras-dummy-sequential-demo-with-card-2/assets\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d949d83f029d4aaf9e31b615ac3bb34f",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Upload file saved_model.pb: 10%|# | 3.37k/33.4k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Upload file keras_metadata.pb: 100%|##########| 3.29k/3.29k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"To https://huggingface.co/nateraw/keras-dummy-sequential-demo-with-card-2\n",
" c1c7d9b..75fc958 main -> main\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'https://huggingface.co/nateraw/keras-dummy-sequential-demo-with-card-2/commit/75fc958d002d11471338cf8c7a3f78bda35f1e67'"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"source": [
"# Full run"
],
"metadata": {
"id": "V9IBBuSEf7YE"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.datasets import mnist\n",
"from tensorflow.keras.models import Model"
],
"metadata": {
"id": "fPgpVDumfVun"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def preprocess(array):\n",
" \"\"\"\n",
" Normalizes the supplied array and reshapes it into the appropriate format.\n",
" \"\"\"\n",
"\n",
" array = array.astype(\"float32\") / 255.0\n",
" array = np.reshape(array, (len(array), 28, 28, 1))\n",
" return array\n",
"\n",
"\n",
"def noise(array):\n",
" \"\"\"\n",
" Adds random noise to each image in the supplied array.\n",
" \"\"\"\n",
"\n",
" noise_factor = 0.4\n",
" noisy_array = array + noise_factor * np.random.normal(\n",
" loc=0.0, scale=1.0, size=array.shape\n",
" )\n",
"\n",
" return np.clip(noisy_array, 0.0, 1.0)\n",
"\n",
"\n",
"def display(array1, array2):\n",
" \"\"\"\n",
" Displays ten random images from each one of the supplied arrays.\n",
" \"\"\"\n",
"\n",
" n = 10\n",
"\n",
" indices = np.random.randint(len(array1), size=n)\n",
" images1 = array1[indices, :]\n",
" images2 = array2[indices, :]\n",
"\n",
" plt.figure(figsize=(20, 4))\n",
" for i, (image1, image2) in enumerate(zip(images1, images2)):\n",
" ax = plt.subplot(2, n, i + 1)\n",
" plt.imshow(image1.reshape(28, 28))\n",
" plt.gray()\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
"\n",
" ax = plt.subplot(2, n, i + 1 + n)\n",
" plt.imshow(image2.reshape(28, 28))\n",
" plt.gray()\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
"\n",
" plt.show()"
],
"metadata": {
"id": "tzGsFDxOf8_N"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Since we only need images from the dataset to encode and decode, we\n",
"# won't use the labels.\n",
"(train_data, _), (test_data, _) = mnist.load_data()\n",
"\n",
"# Normalize and reshape the data\n",
"train_data = preprocess(train_data)\n",
"test_data = preprocess(test_data)\n",
"\n",
"# Create a copy of the data with added noise\n",
"noisy_train_data = noise(train_data)\n",
"noisy_test_data = noise(test_data)\n",
"\n",
"# Display the train data and a version of it with added noise\n",
"display(train_data, noisy_train_data)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
},
"id": "Xbse88rzgDLa",
"outputId": "b0cec2c9-c384-4fe8-92b3-a58933c59a7d"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
"11493376/11490434 [==============================] - 0s 0us/step\n",
"11501568/11490434 [==============================] - 0s 0us/step\n"
]
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1440x288 with 20 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"input = layers.Input(shape=(28, 28, 1))\n",
"\n",
"# Encoder\n",
"x = layers.Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")(input)\n",
"x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)\n",
"x = layers.Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")(x)\n",
"x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)\n",
"\n",
"# Decoder\n",
"x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
"x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
"x = layers.Conv2D(1, (3, 3), activation=\"sigmoid\", padding=\"same\")(x)\n",
"\n",
"# Autoencoder\n",
"autoencoder = Model(input, x)\n",
"autoencoder.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")\n",
"autoencoder.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sF3pNkZbgE9N",
"outputId": "6804115d-9f3c-4972-a5ad-7cd4d06c4b20"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"model\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" input_1 (InputLayer) [(None, 28, 28, 1)] 0 \n",
" \n",
" conv2d (Conv2D) (None, 28, 28, 32) 320 \n",
" \n",
" max_pooling2d (MaxPooling2D (None, 14, 14, 32) 0 \n",
" ) \n",
" \n",
" conv2d_1 (Conv2D) (None, 14, 14, 32) 9248 \n",
" \n",
" max_pooling2d_1 (MaxPooling (None, 7, 7, 32) 0 \n",
" 2D) \n",
" \n",
" conv2d_transpose (Conv2DTra (None, 14, 14, 32) 9248 \n",
" nspose) \n",
" \n",
" conv2d_transpose_1 (Conv2DT (None, 28, 28, 32) 9248 \n",
" ranspose) \n",
" \n",
" conv2d_2 (Conv2D) (None, 28, 28, 1) 289 \n",
" \n",
"=================================================================\n",
"Total params: 28,353\n",
"Trainable params: 28,353\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"autoencoder.fit(\n",
" x=noisy_train_data,\n",
" y=train_data,\n",
" epochs=10,\n",
" batch_size=128,\n",
" shuffle=True,\n",
" validation_data=(noisy_test_data, test_data),\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "175xG-ZigHd-",
"outputId": "19c314d0-5c4a-44dd-d126-dbcc7d7fd3ea"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n",
"469/469 [==============================] - 13s 8ms/step - loss: 0.1736 - val_loss: 0.1044\n",
"Epoch 2/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.1010 - val_loss: 0.0970\n",
"Epoch 3/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0965 - val_loss: 0.0944\n",
"Epoch 4/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0944 - val_loss: 0.0925\n",
"Epoch 5/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0928 - val_loss: 0.0912\n",
"Epoch 6/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0915 - val_loss: 0.0903\n",
"Epoch 7/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0905 - val_loss: 0.0894\n",
"Epoch 8/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0897 - val_loss: 0.0887\n",
"Epoch 9/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0892 - val_loss: 0.0882\n",
"Epoch 10/10\n",
"469/469 [==============================] - 3s 7ms/step - loss: 0.0887 - val_loss: 0.0878\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f4ea40f6b10>"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"predictions = autoencoder.predict(noisy_test_data)\n",
"display(noisy_test_data, predictions)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 247
},
"id": "CDIVMqfUgK7G",
"outputId": "3b3fcb1c-b8bc-4d46-b3c3-642d19baddae"
},
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<Figure size 1440x288 with 20 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import push_to_hub_keras\n",
"\n",
"push_to_hub_keras(autoencoder, 'autoencoder-keras-mnist-demo-with-card-2')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 192,
"referenced_widgets": [
"7438cd3ec80340dda22b9d1eee01f0fd",
"03028dadc4b049fcb56094eff61dfc8a",
"5127628c07714aa9aa3df0aeb26c4b54",
"9a4d52a52477485b9d27772e32c6c7a0",
"24c6ad55fada4970b5a21ffaafc626bb",
"ee72a345a2564b018ba793d0d4819282",
"10ff54832f214581b056f5ecfee7a374",
"69c8b202b91a4084994194cb93531efb",
"9637adef30df431b83b8151499155059",
"861f2af8d0144150bc24bc4fac014b68",
"41fb5864537046a5bc8a6ac8292ee6ac",
"6ae53e644fd04d3aacde144dc382bef7",
"9a6c2ac9cf0d413a97c9e55b5a0161c9",
"870997cabfee4dd4962d103f2e95d150",
"9e731ee00ce048de9a810fff12462272",
"2e08296a23b5495c9196a0d2f1ac706d",
"645ef98712dd483a8a8594e72ce61909",
"a469ee3e95504ff6b2052f9cdd77fde5",
"b5b0c229aaba4cf1b49ed3ac18471f80",
"a8b440228c1e42eaa53a633b9821ef5b",
"f3303dde888f463083c1ad1197c3bd01",
"82c09a31477b486f96ca2f4fe89712c0"
]
},
"id": "RDN99Gyng8Id",
"outputId": "28b40f79-46dd-465e-ced4-633f4fb6e18c"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Cloning https://huggingface.co/nateraw/autoencoder-keras-mnist-demo-with-card-2 into local empty directory.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO:tensorflow:Assets written to: autoencoder-keras-mnist-demo-with-card-2/assets\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7438cd3ec80340dda22b9d1eee01f0fd",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Upload file saved_model.pb: 3%|2 | 3.37k/132k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ae53e644fd04d3aacde144dc382bef7",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Upload file keras_metadata.pb: 18%|#7 | 3.37k/19.2k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"To https://huggingface.co/nateraw/autoencoder-keras-mnist-demo-with-card-2\n",
" 6047f57..a943a56 main -> main\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'https://huggingface.co/nateraw/autoencoder-keras-mnist-demo-with-card-2/commit/a943a56ebe125814b1f00a3cba86e69801cd9a48'"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"autoencoder.history.history"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "94QGFNZmhTQB",
"outputId": "dcba2a21-e416-42b1-bc9c-e6277b476062"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{}"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"autoencoder.history.__dict__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FS1I-JbBhb60",
"outputId": "1ad4ff62-ccf6-4f45-b955-195c5f6cd8eb"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'_chief_worker_only': None,\n",
" '_supports_tf_logs': False,\n",
" 'history': {},\n",
" 'model': <keras.engine.functional.Functional at 0x7fec2050bf90>,\n",
" 'params': {'epochs': 1, 'steps': 313, 'verbose': 0},\n",
" 'validation_data': None}"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"tf.__version__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "qtpqbhdGhqNH",
"outputId": "bd89e00c-e025-42f8-bbb5-cae1c673ae89"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'2.8.0'"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "oHy2Y1BmiWOt"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import KerasModelHubMixin\n",
"\n",
"\n",
"class DummyModel(tf.keras.Model, KerasModelHubMixin):\n",
" def __init__(self, **kwargs):\n",
" super().__init__()\n",
" self.config = kwargs.pop(\"config\", None)\n",
" self.l1 = tf.keras.layers.Dense(2, activation=\"relu\")\n",
" dummy_batch_size = input_dim = 2\n",
" self.dummy_inputs = tf.ones([dummy_batch_size, input_dim])\n",
"\n",
" def call(self, x):\n",
" return self.l1(x)\n",
"\n",
"model = DummyModel()\n",
"\n",
"# Your model must be built!\n",
"# Here we use dummy inputs defined in the model itself, a recommended best practice taken from keras docs\n",
"model(model.dummy_inputs)\n",
"\n",
"model.push_to_hub('keras-dummy-model-mixin-demo-w-card', git_email='naterawdata@gmail.com', git_user='nateraw')"
],
"metadata": {
"id": "RrCTrYJHfZp1",
"outputId": "ccea028b-4fd8-43a4-a6e2-776f821486f5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 256,
"referenced_widgets": [
"a65a1b95a8dc4ff098d6ce7d87762624",
"efdf1d61a6484cf8a39f7470032584ad",
"656e249529934aebab77ab3f3a872381",
"f38804a42bb8478d9f06830f571d94bb",
"5e35c94a6024429f88a342ab2d218a32",
"b3f8339f87134f028e1ad082d030a7ea",
"620cccf298c54618b0afabce0f4465ba",
"43364cbfe0654fb9b4ea438aae3c6518",
"3cc6b19841d945f1af6fe66d1602e668",
"16506627826a4a2e8bc96bd424d923af",
"bd7f0053570a46ff815fd7b5bfb7745e",
"72ce3b65443a46fbb47accf127cb09f2",
"27c4a45194f742d79d6161804019935d",
"fa02ac30f3754915a30f4d3fe07dba87",
"1b93c43f9e3544aeb2a2b8bc71bee1f9",
"e0bae42ac48546169ea52c6754eb3695",
"782e9aa2726a40a9a0ed66778af1539e",
"11e1ebc065664755a7fb946c298e5cef",
"7720fb63fd614cf99321f85cff501116",
"ad71bee7417747d692b9f221b1ec9153",
"a0209f77c0724ec1a7954764d82c90b1",
"1cb109f657aa4ec7b95366c455f95745",
"3a42d6f247e84640b6e5106428d9ff93",
"ee414dea37394e7eb220a20be9d2e99c",
"6bf761a9ce6d461f97f8a84e61ea37c0",
"d64c34ec3ebd4b11b0e74c6db28af251",
"5340455f86f94d2a8e6a9a7fbe98fa1e",
"bfbc653550c34c079ef4a790a35a4654",
"b51b759a64ec4fbfb7c9b9ec82e429a4",
"e1caf5215a21455b9dcc9833ad3079a6",
"5748fc3bbb4944ebb93ee48e311605fb",
"daf9aa806a9f48e3903fedcbe9d84238",
"2e24d71da9154ce099ed8da70628b5f3",
"00a1b3a311b44a078481da23cb74b92c",
"85c8fb7d6d4b443e905585cdc2fffd80",
"c79a90f43b27432882ead7bda1f48f30",
"53086ca03b154cc38dbd35bb3c489c88",
"d26a90b2cf424ffda634a36b7d13bb53",
"63d5e1004a1c41608d03f2db6c8697b8",
"67f5fa500e6e473e924420d7fd8d693d",
"59a0d662ccb0451a8cf4274ce976caff",
"171a295478ee4585b57f6534239f6aa7",
"4c8c3dde596447a59fc325fe8a71ac51",
"9ee33a616b8249d2bfebed0e7941d81c"
]
}
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Cloning https://huggingface.co/nateraw/keras-dummy-model-mixin-demo-w-card into local empty directory.\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a65a1b95a8dc4ff098d6ce7d87762624",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Download file saved_model.pb: 11%|#1 | 2.83k/25.5k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "72ce3b65443a46fbb47accf127cb09f2",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Clean file saved_model.pb: 4%|3 | 1.00k/25.5k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO:tensorflow:Assets written to: keras-dummy-model-mixin-demo-w-card/assets\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a42d6f247e84640b6e5106428d9ff93",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Upload file saved_model.pb: 13%|#3 | 3.37k/25.4k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "00a1b3a311b44a078481da23cb74b92c",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Upload file keras_metadata.pb: 100%|##########| 1.66k/1.66k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"To https://huggingface.co/nateraw/keras-dummy-model-mixin-demo-w-card\n",
" 013aa50..c16dbea main -> main\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'https://huggingface.co/nateraw/keras-dummy-model-mixin-demo-w-card/commit/c16dbeada180326d38d748d309fe2b6d15c63499'"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "uQOBUJZnfdPf"
},
"execution_count": null,
"outputs": []
}
]
}
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