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sroener/IRIS_TFRecord.ipynb

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IRIS_TFRecord.ipynb
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"metadata": {
"id": "_u2Z9JEkpbOl"
},
"source": [
"#general\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"#\n",
"import pandas as pd\n",
"from sklearn.datasets import load_iris\n",
"# for model\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from tensorflow.keras import Sequential\n",
"from tensorflow.keras.layers import Dense"
],
"execution_count": 26,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Jq2Gfgz1phJh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7b15c663-4d2b-45d6-9b2f-0b5cb474d207"
},
"source": [
"print(\"TensorFlow version: {}\".format(tf.__version__))\n",
"\n"
],
"execution_count": 27,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow version: 2.4.1\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Yx3_m_7eqAfe"
},
"source": [
"X, y = load_iris(return_X_y=True)\n",
"X = X.astype('float32')"
],
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "l8hK_XWWqRh9",
"outputId": "d9b4ebc0-efc1-4526-a93e-1d03c597f84a"
},
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
"print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
],
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"text": [
"(100, 4) (50, 4) (100,) (50,)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RDFxQwKzp2gC"
},
"source": [
"def _bytes_feature(value):\n",
" \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n",
" value=tf.io.serialize_tensor(value) #serialize array\n",
" if isinstance(value, type(tf.constant(0))):\n",
" value = value.numpy() # BytesList won't unpack a string from an EagerTensor.\n",
" return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))"
],
"execution_count": 32,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Rzbu1FXGp6wd"
},
"source": [
"def serialize_example(label, feature):\n",
" \"\"\"\n",
" Creates a tf.train.Example message ready to be written to a file.\n",
" \"\"\"\n",
" # Create a dictionary mapping the feature name to the tf.train.Example-compatible\n",
" # data type.\n",
" feature = {\n",
" 'feature': _bytes_feature(label),\n",
" 'label': _bytes_feature(feature),\n",
" }\n",
" \n",
" # Create a Features message using tf.train.Example.\n",
" \n",
" example_proto = tf.train.Example(features=tf.train.Features(feature=feature))\n",
" return example_proto.SerializeToString()"
],
"execution_count": 33,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uOzlEP2oqkp4"
},
"source": [
"with tf.io.TFRecordWriter(\"iris_train.tfrecord\") as writer:\n",
" for X,y in zip(X_train,y_train):\n",
" example = serialize_example(X,y)\n",
" writer.write(example)"
],
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zy6CCyDuqpu9"
},
"source": [
"# load tfrecord(s) as dataset\n",
"train_data = tf.data.TFRecordDataset(\"iris_train.tfrecord\")"
],
"execution_count": 35,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_ajIt7V2yjhE"
},
"source": [
"import tensorflow_datasets as tfds"
],
"execution_count": 50,
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},
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},
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"source": [
"ds = tfds.load('iris', split='train')"
],
"execution_count": 51,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1mDownloading and preparing dataset iris/2.0.0 (download: 4.44 KiB, generated: Unknown size, total: 4.44 KiB) to /root/tensorflow_datasets/iris/2.0.0...\u001b[0m\n"
],
"name": "stdout"
},
{
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"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
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}
},
{
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"metadata": {
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},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/iris/2.0.0.incompleteW7JADB/iris-train.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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]
},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\u001b[1mDataset iris downloaded and prepared to /root/tensorflow_datasets/iris/2.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
"\r"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "x4l97lVcyvmT",
"outputId": "b8d17138-283f-4fb5-b284-9f51e3309e62"
},
"source": [
"ds"
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PrefetchDataset shapes: {features: (4,), label: ()}, types: {features: tf.float32, label: tf.int64}>"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0ZFarg7qqpeF"
},
"source": [
"def _parse_function(example_proto):\n",
" feature_description={\n",
" \"feature\":tf.io.FixedLenFeature([], tf.string),\n",
" \"label\":tf.io.FixedLenFeature([], tf.string),\n",
" }\n",
" # Parse the input tf.train.Example proto using the dictionary above.\n",
" example = tf.io.parse_example(example_proto, feature_description)\n",
" feature = tf.io.parse_tensor(example[\"feature\"], tf.float32)\n",
" label = tf.io.parse_tensor(example[\"label\"], tf.int64)\n",
" return feature,label"
],
"execution_count": 49,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mTkIEI-mtn6v"
},
"source": [
"parsed_train_dataset = train_data.map(_parse_function)"
],
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dcJBHfVWqpMG"
},
"source": [
"# define model\n",
"model = Sequential()\n",
"model.add(Dense(10, activation='relu', kernel_initializer='he_normal', input_shape=(4,)))\n",
"model.add(Dense(8, activation='relu', kernel_initializer='he_normal'))\n",
"model.add(Dense(3, activation='softmax'))"
],
"execution_count": 41,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "B1e1exq4r0H4"
},
"source": [
"model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
],
"execution_count": 45,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 306
},
"id": "AHUtJVqirlke",
"outputId": "fdede0f6-5eea-4b93-a682-573f63bb2a23"
},
"source": [
"history = model.fit(ds)"
],
"execution_count": 53,
"outputs": [
{
"output_type": "error",
"ename": "TypeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-53-0f390d64cea4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\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/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)\u001b[0m\n\u001b[1;32m 1098\u001b[0m _r=1):\n\u001b[1;32m 1099\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-> 1100\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\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 1101\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 1102\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/python3.7/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 826\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_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[0m\n\u001b[1;32m 827\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\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 829\u001b[0m \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 830\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_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[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/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 853\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\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;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 856\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[1;32m 857\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\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: 'NoneType' object is not callable"
]
}
]
}
]
}
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