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MervinPraison/AIA006_tf_DNNClassifier.ipynb

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Created June 20, 2019 10:44
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AIA006_tf_DNNClassifier.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Copyright 2017 Google Inc.\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.3.0\n",
"INFO:tensorflow:Using default config.\n",
"INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/iris_model', '_save_summary_steps': 100}\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Saving checkpoints for 1 into /tmp/iris_model/model.ckpt.\n",
"INFO:tensorflow:loss = 174.354, step = 1\n",
"INFO:tensorflow:global_step/sec: 551.517\n",
"INFO:tensorflow:loss = 11.9108, step = 101 (0.185 sec)\n",
"INFO:tensorflow:global_step/sec: 566.986\n",
"INFO:tensorflow:loss = 11.0975, step = 201 (0.176 sec)\n",
"INFO:tensorflow:global_step/sec: 831.443\n",
"INFO:tensorflow:loss = 7.06128, step = 301 (0.117 sec)\n",
"INFO:tensorflow:global_step/sec: 786.033\n",
"INFO:tensorflow:loss = 6.75643, step = 401 (0.127 sec)\n",
"INFO:tensorflow:global_step/sec: 878.983\n",
"INFO:tensorflow:loss = 4.96682, step = 501 (0.114 sec)\n",
"INFO:tensorflow:global_step/sec: 900.933\n",
"INFO:tensorflow:loss = 4.99079, step = 601 (0.111 sec)\n",
"INFO:tensorflow:global_step/sec: 899.166\n",
"INFO:tensorflow:loss = 4.42463, step = 701 (0.112 sec)\n",
"INFO:tensorflow:global_step/sec: 888.867\n",
"INFO:tensorflow:loss = 6.84585, step = 801 (0.112 sec)\n",
"INFO:tensorflow:global_step/sec: 897.738\n",
"INFO:tensorflow:loss = 4.07034, step = 901 (0.113 sec)\n",
"INFO:tensorflow:Saving checkpoints for 1000 into /tmp/iris_model/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 3.99844.\n",
"fit done\n",
"INFO:tensorflow:Starting evaluation at 2017-09-28-17:45:46\n",
"INFO:tensorflow:Restoring parameters from /tmp/iris_model/model.ckpt-1000\n",
"INFO:tensorflow:Evaluation [1/100]\n",
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"INFO:tensorflow:Finished evaluation at 2017-09-28-17:45:46\n",
"INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.966667, average_loss = 0.0652359, global_step = 1000, loss = 1.95708\n",
"\n",
"Accuracy: 0.966667\n",
"INFO:tensorflow:Restoring parameters from /tmp/iris_model/model.ckpt-1000\n",
"INFO:tensorflow:Assets added to graph.\n",
"INFO:tensorflow:No assets to write.\n",
"INFO:tensorflow:SavedModel written to: /tmp/iris_model/export/1506620748/saved_model.pb\n"
]
},
{
"data": {
"text/plain": [
"'/tmp/iris_model/export/1506620748'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"\n",
"print(tf.__version__)\n",
"\n",
"from tensorflow.contrib.learn.python.learn.datasets import base\n",
"\n",
"# Data files\n",
"IRIS_TRAINING = \"iris_training.csv\"\n",
"IRIS_TEST = \"iris_test.csv\"\n",
"\n",
"# Load datasets.\n",
"training_set = base.load_csv_with_header(filename=IRIS_TRAINING,\n",
" features_dtype=np.float32,\n",
" target_dtype=np.int)\n",
"test_set = base.load_csv_with_header(filename=IRIS_TEST,\n",
" features_dtype=np.float32,\n",
" target_dtype=np.int)\n",
"\n",
"# Specify that all features have real-value data\n",
"feature_name = \"flower_features\"\n",
"feature_columns = [tf.feature_column.numeric_column(feature_name, \n",
" shape=[4])]\n",
"classifier = tf.estimator.DNNClassifier(\n",
" feature_columns=feature_columns,\n",
" n_classes=3,\n",
" model_dir=\"/tmp/iris_model\",\n",
" hidden_units=[100, 70, 50, 25])\n",
"\n",
"def input_fn(dataset):\n",
" def _fn():\n",
" features = {feature_name: tf.constant(dataset.data)}\n",
" label = tf.constant(dataset.target)\n",
" return features, label\n",
" return _fn\n",
"\n",
"# Fit model.\n",
"classifier.train(input_fn=input_fn(training_set),\n",
" steps=1000)\n",
"print('fit done')\n",
"\n",
"# Evaluate accuracy.\n",
"accuracy_score = classifier.evaluate(input_fn=input_fn(test_set), \n",
" steps=100)[\"accuracy\"]\n",
"print('\\nAccuracy: {0:f}'.format(accuracy_score))\n",
"\n",
"# Export the model for serving\n",
"feature_spec = {'flower_features': tf.FixedLenFeature(shape=[4], dtype=np.float32)}\n",
"\n",
"serving_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)\n",
"\n",
"classifier.export_savedmodel(export_dir_base='/tmp/iris_model' + '/export', \n",
" serving_input_receiver_fn=serving_fn)\n"
]
}
],
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"kernelspec": {
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