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"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, | |
"outputs": [] | |
}, | |
{ | |
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"\n", | |
"\n" | |
], | |
"name": "stdout" | |
}, | |
{ | |
"output_type": "display_data", | |
"data": { | |
"application/vnd.jupyter.widget-view+json": { | |
"model_id": "2f74c2fc78dc49c387e3d1119a5525a9", | |
"version_minor": 0, | |
"version_major": 2 | |
}, | |
"text/plain": [ | |
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
}, | |
{ | |
"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", | |
"data": { | |
"application/vnd.jupyter.widget-view+json": { | |
"model_id": "3133306725c5485393ac79757bf6b2da", | |
"version_minor": 0, | |
"version_major": 2 | |
}, | |
"text/plain": [ | |
"HBox(children=(FloatProgress(value=0.0, max=150.0), HTML(value='')))" | |
] | |
}, | |
"metadata": { | |
"tags": [] | |
} | |
}, | |
{ | |
"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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