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Unverified Commit 43178d7f authored by Ayushman Kumar's avatar Ayushman Kumar Committed by GitHub
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Merge pull request #1 from tensorflow/master 

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samples/core/get_started/_index.ipynb deleted 100644 → 0
View file @ 8b47aa3d
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"nbformat": 4,
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"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
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"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
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"source": [
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb\">\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /><span>Run in Google Colab</span></a> \n",
"</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/_index.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /><span>View source on GitHub</span></a></td></table>"
]
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\ No newline at end of file
samples/core/get_started/custom_estimator.py deleted 100644 → 0
View file @ 8b47aa3d
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""An Example of a custom Estimator for the Iris dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import tensorflow as tf
import iris_data
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=1000, type=int,
help='number of training steps')
def my_model(features, labels, mode, params):
"""DNN with three hidden layers and learning_rate=0.1."""
# Create three fully connected layers.
net = tf.feature_column.input_layer(features, params['feature_columns'])
for units in params['hidden_units']:
net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
# Compute logits (1 per class).
logits = tf.layers.dense(net, params['n_classes'], activation=None)
# Compute predictions.
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predicted_classes[:, tf.newaxis],
'probabilities': tf.nn.softmax(logits),
'logits': logits,
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Compute loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Compute evaluation metrics.
accuracy = tf.metrics.accuracy(labels=labels,
predictions=predicted_classes,
name='acc_op')
metrics = {'accuracy': accuracy}
tf.summary.scalar('accuracy', accuracy[1])
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=metrics)
# Create training op.
assert mode == tf.estimator.ModeKeys.TRAIN
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
def main(argv):
args = parser.parse_args(argv[1:])
# Fetch the data
(train_x, train_y), (test_x, test_y) = iris_data.load_data()
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.Estimator(
model_fn=my_model,
params={
'feature_columns': my_feature_columns,
# Two hidden layers of 10 nodes each.
'hidden_units': [10, 10],
# The model must choose between 3 classes.
'n_classes': 3,
})
# Train the Model.
classifier.train(
input_fn=lambda:iris_data.train_input_fn(train_x, train_y, args.batch_size),
steps=args.train_steps)
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=lambda:iris_data.eval_input_fn(test_x, test_y, args.batch_size))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
# Generate predictions from the model
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
predictions = classifier.predict(
input_fn=lambda:iris_data.eval_input_fn(predict_x,
labels=None,
batch_size=args.batch_size))
for pred_dict, expec in zip(predictions, expected):
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
class_id = pred_dict['class_ids'][0]
probability = pred_dict['probabilities'][class_id]
print(template.format(iris_data.SPECIES[class_id],
100 * probability, expec))
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main)
samples/core/get_started/eager.ipynb deleted 100644 → 0
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"cells": [
{
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"id": "rX8mhOLljYeM",
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"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
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"metadata": {
"id": "BZSlp3DAjdYf",
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"colab": {},
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"source": [
"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "3wF5wszaj97Y",
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"<table class=\"tfo-notebook-buttons\" align=\"left\"><td>\n",
"<a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/eager/custom_training_walkthrough.ipynb\">\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /><span>Run in Google Colab</span></a> \n",
"</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/eager/custom_training_walkthrough.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /><span>View source on GitHub</span></a></td></table>"
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\ No newline at end of file
samples/core/get_started/estimator_test.py deleted 100644 → 0
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A simple smoke test that runs these examples for 1 training iteraton."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import pandas as pd
from six.moves import StringIO
import iris_data
import custom_estimator
import premade_estimator
FOUR_LINES = "\n".join([
"1,52.40, 2823,152,2",
"164, 99.80,176.60,66.20,1",
"176,2824, 136,3.19,0",
"2,177.30,66.30, 53.10,1",])
def four_lines_data():
text = StringIO(FOUR_LINES)
df = pd.read_csv(text, names=iris_data.CSV_COLUMN_NAMES)
xy = (df, df.pop("Species"))
return xy, xy
class RegressionTest(tf.test.TestCase):
"""Test the regression examples in this directory."""
@tf.test.mock.patch.dict(premade_estimator.__dict__,
{"load_data": four_lines_data})
def test_premade_estimator(self):
premade_estimator.main([None, "--train_steps=1"])
@tf.test.mock.patch.dict(custom_estimator.__dict__,
{"load_data": four_lines_data})
def test_custom_estimator(self):
custom_estimator.main([None, "--train_steps=1"])
if __name__ == "__main__":
tf.test.main()
samples/core/get_started/iris_data.py deleted 100644 → 0
View file @ 8b47aa3d
import pandas as pd
import tensorflow as tf
TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']
def maybe_download():
train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
return train_path, test_path
def load_data(y_name='Species'):
"""Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
train_path, test_path = maybe_download()
train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
train_x, train_y = train, train.pop(y_name)
test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
test_x, test_y = test, test.pop(y_name)
return (train_x, train_y), (test_x, test_y)
def train_input_fn(features, labels, batch_size):
"""An input function for training"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle, repeat, and batch the examples.
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
# Return the dataset.
return dataset
def eval_input_fn(features, labels, batch_size):
"""An input function for evaluation or prediction"""
features=dict(features)
if labels is None:
# No labels, use only features.
inputs = features
else:
inputs = (features, labels)
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices(inputs)
# Batch the examples
assert batch_size is not None, "batch_size must not be None"
dataset = dataset.batch(batch_size)
# Return the dataset.
return dataset
# The remainder of this file contains a simple example of a csv parser,
# implemented using the `Dataset` class.
# `tf.parse_csv` sets the types of the outputs to match the examples given in
# the `record_defaults` argument.
CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0]]
def _parse_line(line):
# Decode the line into its fields
fields = tf.decode_csv(line, record_defaults=CSV_TYPES)
# Pack the result into a dictionary
features = dict(zip(CSV_COLUMN_NAMES, fields))
# Separate the label from the features
label = features.pop('Species')
return features, label
def csv_input_fn(csv_path, batch_size):
# Create a dataset containing the text lines.
dataset = tf.data.TextLineDataset(csv_path).skip(1)
# Parse each line.
dataset = dataset.map(_parse_line)
# Shuffle, repeat, and batch the examples.
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
# Return the dataset.
return dataset
samples/core/get_started/premade_estimator.py deleted 100644 → 0
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""An Example of a DNNClassifier for the Iris dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import tensorflow as tf
import iris_data
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=1000, type=int,
help='number of training steps')
def main(argv):
args = parser.parse_args(argv[1:])
# Fetch the data
(train_x, train_y), (test_x, test_y) = iris_data.load_data()
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[10, 10],
# The model must choose between 3 classes.
n_classes=3)
# Train the Model.
classifier.train(
input_fn=lambda:iris_data.train_input_fn(train_x, train_y,
args.batch_size),
steps=args.train_steps)
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=lambda:iris_data.eval_input_fn(test_x, test_y,
args.batch_size))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
# Generate predictions from the model
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
predictions = classifier.predict(
input_fn=lambda:iris_data.eval_input_fn(predict_x,
labels=None,
batch_size=args.batch_size))
template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
for pred_dict, expec in zip(predictions, expected):
class_id = pred_dict['class_ids'][0]
probability = pred_dict['probabilities'][class_id]
print(template.format(iris_data.SPECIES[class_id],
100 * probability, expec))
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main)
samples/core/guide/autograph.ipynb deleted 100644 → 0
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"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://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."
],
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"outputs": []
},
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"metadata": {
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"source": [
"# AutoGraph: Easy control flow for graphs "
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" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/guide/autograph\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/autograph.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/guide/autograph.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
}
]
}
samples/core/tutorials/eager/custom_training_walkthrough.ipynb deleted 100644 → 0
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"metadata": {
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"cellView": "form"
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"source": [
"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
},
{
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},
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"source": [
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"\n",
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" </td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/eager/custom_training_walkthrough.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/eager/custom_training_walkthrough.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
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]
}
\ No newline at end of file
samples/core/tutorials/estimators/linear.ipynb deleted 100644 → 0
View file @ 8b47aa3d
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{
"metadata": {
"id": "MWW1TyjaecRh",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"metadata": {
"id": "mOtR1FzCef-u",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Zr7KpBhMcYvE",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Build a linear model with Estimators"
]
},
{
"metadata": {
"id": "gkkpAk4sEvQR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This file has moved."
]
},
{
"metadata": {
"id": "uJl4gaPFzxQz",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/estimators/linear\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/estimators/linear.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/estimators/linear.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
}
]
}
\ No newline at end of file
samples/core/tutorials/keras/basic_classification.ipynb deleted 100644 → 0
View file @ 8b47aa3d
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "basic_classification.ipynb",
"version": "0.3.2",
"provenance": [],
"private_outputs": true,
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"metadata": {
"id": "MhoQ0WE77laV",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"metadata": {
"id": "_ckMIh7O7s6D",
"colab_type": "code",
"colab": {},
"cellView": "form"
},
"cell_type": "code",
"source": [
"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "vasWnqRgy1H4",
"colab_type": "code",
"colab": {},
"cellView": "form"
},
"cell_type": "code",
"source": [
"#@title MIT License\n",
"#\n",
"# Copyright (c) 2017 François Chollet\n",
"#\n",
"# Permission is hereby granted, free of charge, to any person obtaining a\n",
"# copy of this software and associated documentation files (the \"Software\"),\n",
"# to deal in the Software without restriction, including without limitation\n",
"# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
"# and/or sell copies of the Software, and to permit persons to whom the\n",
"# Software is furnished to do so, subject to the following conditions:\n",
"#\n",
"# The above copyright notice and this permission notice shall be included in\n",
"# all copies or substantial portions of the Software.\n",
"#\n",
"# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
"# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
"# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
"# DEALINGS IN THE SOFTWARE."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "jYysdyb-CaWM",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Train your first neural network: basic classification"
]
},
{
"metadata": {
"id": "-VrEyagzFU63",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This file has moved."
]
},
{
"metadata": {
"id": "S5Uhzt6vVIB2",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/basic_classification\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
}
]
}
\ No newline at end of file
samples/core/tutorials/keras/basic_regression.ipynb deleted 100644 → 0
View file @ 8b47aa3d
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "basic-regression.ipynb",
"version": "0.3.2",
"provenance": [],
"private_outputs": true,
"collapsed_sections": [
"FhGuhbZ6M5tl"
],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"metadata": {
"id": "FhGuhbZ6M5tl",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"metadata": {
"id": "AwOEIRJC6Une",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "KyPEtTqk6VdG",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"#@title MIT License\n",
"#\n",
"# Copyright (c) 2017 François Chollet\n",
"#\n",
"# Permission is hereby granted, free of charge, to any person obtaining a\n",
"# copy of this software and associated documentation files (the \"Software\"),\n",
"# to deal in the Software without restriction, including without limitation\n",
"# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
"# and/or sell copies of the Software, and to permit persons to whom the\n",
"# Software is furnished to do so, subject to the following conditions:\n",
"#\n",
"# The above copyright notice and this permission notice shall be included in\n",
"# all copies or substantial portions of the Software.\n",
"#\n",
"# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
"# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
"# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
"# DEALINGS IN THE SOFTWARE."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "EIdT9iu_Z4Rb",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Predict house prices: regression"
]
},
{
"metadata": {
"id": "dmtJAyM7FwUf",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This file has moved."
]
},
{
"metadata": {
"id": "bBIlTPscrIT9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/basic_regression\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_regression.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_regression.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
}
]
}
\ No newline at end of file
samples/core/tutorials/keras/basic_text_classification.ipynb deleted 100644 → 0
View file @ 8b47aa3d
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "basic-text-classification.ipynb",
"version": "0.3.2",
"provenance": [],
"private_outputs": true,
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"metadata": {
"id": "Ic4_occAAiAT",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"metadata": {
"id": "ioaprt5q5US7",
"colab_type": "code",
"colab": {},
"cellView": "form"
},
"cell_type": "code",
"source": [
"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "yCl0eTNH5RS3",
"colab_type": "code",
"colab": {},
"cellView": "form"
},
"cell_type": "code",
"source": [
"#@title MIT License\n",
"#\n",
"# Copyright (c) 2017 François Chollet\n",
"#\n",
"# Permission is hereby granted, free of charge, to any person obtaining a\n",
"# copy of this software and associated documentation files (the \"Software\"),\n",
"# to deal in the Software without restriction, including without limitation\n",
"# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
"# and/or sell copies of the Software, and to permit persons to whom the\n",
"# Software is furnished to do so, subject to the following conditions:\n",
"#\n",
"# The above copyright notice and this permission notice shall be included in\n",
"# all copies or substantial portions of the Software.\n",
"#\n",
"# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
"# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
"# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
"# DEALINGS IN THE SOFTWARE."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ItXfxkxvosLH",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Text classification with movie reviews"
]
},
{
"metadata": {
"id": "NBM1gMpEGN_d",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This file has moved."
]
},
{
"metadata": {
"id": "hKY4XMc9o8iB",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/basic_text_classification\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_text_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_text_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
}
]
}
\ No newline at end of file
samples/core/tutorials/keras/overfit_and_underfit.ipynb deleted 100644 → 0
View file @ 8b47aa3d
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "overfit-and-underfit.ipynb",
"version": "0.3.2",
"provenance": [],
"private_outputs": true,
"collapsed_sections": [
"fTFj8ft5dlbS"
],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"metadata": {
"id": "fTFj8ft5dlbS",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"##### Copyright 2018 The TensorFlow Authors."
]
},
{
"metadata": {
"id": "lzyBOpYMdp3F",
"colab_type": "code",
"colab": {},
"cellView": "form"
},
"cell_type": "code",
"source": [
"#@title 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",
"# https://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."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "m_x4KfSJ7Vt7",
"colab_type": "code",
"colab": {},
"cellView": "form"
},
"cell_type": "code",
"source": [
"#@title MIT License\n",
"#\n",
"# Copyright (c) 2017 François Chollet\n",
"#\n",
"# Permission is hereby granted, free of charge, to any person obtaining a\n",
"# copy of this software and associated documentation files (the \"Software\"),\n",
"# to deal in the Software without restriction, including without limitation\n",
"# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
"# and/or sell copies of the Software, and to permit persons to whom the\n",
"# Software is furnished to do so, subject to the following conditions:\n",
"#\n",
"# The above copyright notice and this permission notice shall be included in\n",
"# all copies or substantial portions of the Software.\n",
"#\n",
"# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
"# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
"# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
"# DEALINGS IN THE SOFTWARE."
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "C9HmC2T4ld5B",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Explore overfitting and underfitting"
]
},
{
"metadata": {
"id": "Xy5k1IC0GrV9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This file has moved."
]
},
{
"metadata": {
"id": "kRTxFhXAlnl1",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/overfit_and_underfit\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/overfit_and_underfit.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/overfit_and_underfit.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
}
]
}
\ No newline at end of file
samples/languages/java/README.md deleted 100644 → 0
View file @ 8b47aa3d
# TensorFlow for Java: Examples
These have moved.
See: The [java repository](https://github.com/tensorflow/java), and the [Java models repository](https://github.com/tensorflow/java-models)
samples/outreach/blogs/blog_custom_estimators.py deleted 100644 → 0
View file @ 8b47aa3d
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# This is the complete code for the following blogpost:
# https://developers.googleblog.com/2017/12/creating-custom-estimators-in-tensorflow.html
import tensorflow as tf
import os
import sys
import six.moves.urllib.request as request
from distutils.version import StrictVersion
tf.logging.set_verbosity(tf.logging.INFO)
# Check that we have correct TensorFlow version installed
tf_version = tf.__version__
tf.logging.info("TensorFlow version: {}".format(tf_version))
assert StrictVersion("1.4") <= StrictVersion(tf_version), "TensorFlow r1.4 or later is needed"
# Windows users: You only need to change PATH, rest is platform independent
PATH = "/tmp/tf_custom_estimators"
# Fetch and store Training and Test dataset files
PATH_DATASET = PATH + os.sep + "dataset"
FILE_TRAIN = PATH_DATASET + os.sep + "iris_training.csv"
FILE_TEST = PATH_DATASET + os.sep + "iris_test.csv"
URL_TRAIN = "http://download.tensorflow.org/data/iris_training.csv"
URL_TEST = "http://download.tensorflow.org/data/iris_test.csv"
def downloadDataset(url, file):
if not os.path.exists(PATH_DATASET):
os.makedirs(PATH_DATASET)
if not os.path.exists(file):
data = request.urlopen(url).read()
with open(file, "wb") as f:
f.write(data)
f.close()
downloadDataset(URL_TRAIN, FILE_TRAIN)
downloadDataset(URL_TEST, FILE_TEST)
# The CSV features in our training & test data
feature_names = [
'SepalLength',
'SepalWidth',
'PetalLength',
'PetalWidth']
# Create an input function reading a file using the Dataset API
# Then provide the results to the Estimator API
def my_input_fn(file_path, repeat_count=1, shuffle_count=1):
def decode_csv(line):
parsed_line = tf.decode_csv(line, [[0.], [0.], [0.], [0.], [0]])
label = parsed_line[-1] # Last element is the label
del parsed_line[-1] # Delete last element
features = parsed_line # Everything but last elements are the features
d = dict(zip(feature_names, features)), label
return d
dataset = (tf.data.TextLineDataset(file_path) # Read text file
.skip(1) # Skip header row
.map(decode_csv, num_parallel_calls=4) # Decode each line
.cache() # Warning: Caches entire dataset, can cause out of memory
.shuffle(shuffle_count) # Randomize elems (1 == no operation)
.repeat(repeat_count) # Repeats dataset this # times
.batch(32)
.prefetch(1) # Make sure you always have 1 batch ready to serve
)
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
return batch_features, batch_labels
def my_model_fn(
features, # This is batch_features from input_fn
labels, # This is batch_labels from input_fn
mode): # And instance of tf.estimator.ModeKeys, see below
if mode == tf.estimator.ModeKeys.PREDICT:
tf.logging.info("my_model_fn: PREDICT, {}".format(mode))
elif mode == tf.estimator.ModeKeys.EVAL:
tf.logging.info("my_model_fn: EVAL, {}".format(mode))
elif mode == tf.estimator.ModeKeys.TRAIN:
tf.logging.info("my_model_fn: TRAIN, {}".format(mode))
# All our inputs are feature columns of type numeric_column
feature_columns = [
tf.feature_column.numeric_column(feature_names[0]),
tf.feature_column.numeric_column(feature_names[1]),
tf.feature_column.numeric_column(feature_names[2]),
tf.feature_column.numeric_column(feature_names[3])
]
# Create the layer of input
input_layer = tf.feature_column.input_layer(features, feature_columns)
# Definition of hidden layer: h1
# We implement it as a fully-connected layer (tf.layers.dense)
# Has 10 neurons, and uses ReLU as the activation function
# Takes input_layer as input
h1 = tf.layers.Dense(10, activation=tf.nn.relu)(input_layer)
# Definition of hidden layer: h2 (this is the logits layer)
# Similar to h1, but takes h1 as input
h2 = tf.layers.Dense(10, activation=tf.nn.relu)(h1)
# Output 'logits' layer is three number = probability distribution
# between Iris Setosa, Versicolor, and Viginica
logits = tf.layers.Dense(3)(h2)
# class_ids will be the model prediction for the class (Iris flower type)
# The output node with the highest value is our prediction
predictions = { 'class_ids': tf.argmax(input=logits, axis=1) }
# 1. Prediction mode
# Return our prediction
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Evaluation and Training mode
# Calculate the loss
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Calculate the accuracy between the true labels, and our predictions
accuracy = tf.metrics.accuracy(labels, predictions['class_ids'])
# 2. Evaluation mode
# Return our loss (which is used to evaluate our model)
# Set the TensorBoard scalar my_accurace to the accuracy
# Obs: This function only sets value during mode == ModeKeys.EVAL
# To set values during training, see tf.summary.scalar
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode,
loss=loss,
eval_metric_ops={'my_accuracy': accuracy})
# If mode is not PREDICT nor EVAL, then we must be in TRAIN
assert mode == tf.estimator.ModeKeys.TRAIN, "TRAIN is only ModeKey left"
# 3. Training mode
# Default optimizer for DNNClassifier: Adagrad with learning rate=0.05
# Our objective (train_op) is to minimize loss
# Provide global step counter (used to count gradient updates)
optimizer = tf.train.AdagradOptimizer(0.05)
train_op = optimizer.minimize(
loss,
global_step=tf.train.get_global_step())
# Set the TensorBoard scalar my_accuracy to the accuracy
# Obs: This function only sets the value during mode == ModeKeys.TRAIN
# To set values during evaluation, see eval_metrics_ops
tf.summary.scalar('my_accuracy', accuracy[1])
# Return training operations: loss and train_op
return tf.estimator.EstimatorSpec(
mode,
loss=loss,
train_op=train_op)
# Create a custom estimator using my_model_fn to define the model
tf.logging.info("Before classifier construction")
classifier = tf.estimator.Estimator(
model_fn=my_model_fn,
model_dir=PATH) # Path to where checkpoints etc are stored
tf.logging.info("...done constructing classifier")
# 500 epochs = 500 * 120 records [60000] = (500 * 120) / 32 batches = 1875 batches
# 4 epochs = 4 * 30 records = (4 * 30) / 32 batches = 3.75 batches
# Train our model, use the previously function my_input_fn
# Input to training is a file with training example
# Stop training after 8 iterations of train data (epochs)
tf.logging.info("Before classifier.train")
classifier.train(
input_fn=lambda: my_input_fn(FILE_TRAIN, 500, 256))
tf.logging.info("...done classifier.train")
# Evaluate our model using the examples contained in FILE_TEST
# Return value will contain evaluation_metrics such as: loss & average_loss
tf.logging.info("Before classifier.evaluate")
evaluate_result = classifier.evaluate(
input_fn=lambda: my_input_fn(FILE_TEST, 4))
tf.logging.info("...done classifier.evaluate")
tf.logging.info("Evaluation results")
for key in evaluate_result:
tf.logging.info(" {}, was: {}".format(key, evaluate_result[key]))
# Predict the type of some Iris flowers.
# Let's predict the examples in FILE_TEST, repeat only once.
predict_results = classifier.predict(
input_fn=lambda: my_input_fn(FILE_TEST, 1))
tf.logging.info("Prediction on test file")
for prediction in predict_results:
# Will print the predicted class, i.e: 0, 1, or 2 if the prediction
# is Iris Setosa, Vericolor, Virginica, respectively.
tf.logging.info("...{}".format(prediction["class_ids"]))
# Let create a dataset for prediction
# We've taken the first 3 examples in FILE_TEST
prediction_input = [[5.9, 3.0, 4.2, 1.5], # -> 1, Iris Versicolor
[6.9, 3.1, 5.4, 2.1], # -> 2, Iris Virginica
[5.1, 3.3, 1.7, 0.5]] # -> 0, Iris Setosa
def new_input_fn():
def decode(x):
x = tf.split(x, 4) # Need to split into our 4 features
return dict(zip(feature_names, x)) # To build a dict of them
dataset = tf.data.Dataset.from_tensor_slices(prediction_input)
dataset = dataset.map(decode)
iterator = dataset.make_one_shot_iterator()
next_feature_batch = iterator.get_next()
return next_feature_batch, None # In prediction, we have no labels
# Predict all our prediction_input
predict_results = classifier.predict(input_fn=new_input_fn)
# Print results
tf.logging.info("Predictions on memory")
for idx, prediction in enumerate(predict_results):
type = prediction["class_ids"] # Get the predicted class (index)
if type == 0:
tf.logging.info("...I think: {}, is Iris Setosa".format(prediction_input[idx]))
elif type == 1:
tf.logging.info("...I think: {}, is Iris Versicolor".format(prediction_input[idx]))
else:
tf.logging.info("...I think: {}, is Iris Virginica".format(prediction_input[idx]))
samples/outreach/blogs/blog_estimators_dataset.py deleted 100644 → 0
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# This is the complete code for the following blogpost:
# https://developers.googleblog.com/2017/09/introducing-tensorflow-datasets.html
# (https://goo.gl/Ujm2Ep)
import os
import six.moves.urllib.request as request
import tensorflow as tf
from distutils.version import StrictVersion
# Check that we have correct TensorFlow version installed
tf_version = tf.__version__
print("TensorFlow version: {}".format(tf_version))
assert StrictVersion("1.4") <= StrictVersion(tf_version), "TensorFlow r1.4 or later is needed"
# Windows users: You only need to change PATH, rest is platform independent
PATH = "/tmp/tf_dataset_and_estimator_apis"
# Fetch and store Training and Test dataset files
PATH_DATASET = PATH + os.sep + "dataset"
FILE_TRAIN = PATH_DATASET + os.sep + "iris_training.csv"
FILE_TEST = PATH_DATASET + os.sep + "iris_test.csv"
URL_TRAIN = "http://download.tensorflow.org/data/iris_training.csv"
URL_TEST = "http://download.tensorflow.org/data/iris_test.csv"
def download_dataset(url, file):
if not os.path.exists(PATH_DATASET):
os.makedirs(PATH_DATASET)
if not os.path.exists(file):
data = request.urlopen(url).read()
with open(file, "wb") as f:
f.write(data)
f.close()
download_dataset(URL_TRAIN, FILE_TRAIN)
download_dataset(URL_TEST, FILE_TEST)
tf.logging.set_verbosity(tf.logging.INFO)
# The CSV features in our training & test data
feature_names = [
'SepalLength',
'SepalWidth',
'PetalLength',
'PetalWidth']
# Create an input function reading a file using the Dataset API
# Then provide the results to the Estimator API
def my_input_fn(file_path, perform_shuffle=False, repeat_count=1):
def decode_csv(line):
parsed_line = tf.decode_csv(line, [[0.], [0.], [0.], [0.], [0]])
label = parsed_line[-1] # Last element is the label
del parsed_line[-1] # Delete last element
features = parsed_line # Everything but last elements are the features
d = dict(zip(feature_names, features)), label
return d
dataset = (tf.data.TextLineDataset(file_path) # Read text file
.skip(1) # Skip header row
.map(decode_csv)) # Transform each elem by applying decode_csv fn
if perform_shuffle:
# Randomizes input using a window of 256 elements (read into memory)
dataset = dataset.shuffle(buffer_size=256)
dataset = dataset.repeat(repeat_count) # Repeats dataset this # times
dataset = dataset.batch(32) # Batch size to use
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
return batch_features, batch_labels
next_batch = my_input_fn(FILE_TRAIN, True) # Will return 32 random elements
# Create the feature_columns, which specifies the input to our model
# All our input features are numeric, so use numeric_column for each one
feature_columns = [tf.feature_column.numeric_column(k) for k in feature_names]
# Create a deep neural network regression classifier
# Use the DNNClassifier pre-made estimator
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns, # The input features to our model
hidden_units=[10, 10], # Two layers, each with 10 neurons
n_classes=3,
model_dir=PATH) # Path to where checkpoints etc are stored
# Train our model, use the previously defined function my_input_fn
# Input to training is a file with training example
# Stop training after 8 iterations of train data (epochs)
classifier.train(
input_fn=lambda: my_input_fn(FILE_TRAIN, True, 8))
# Evaluate our model using the examples contained in FILE_TEST
# Return value will contain evaluation_metrics such as: loss & average_loss
evaluate_result = classifier.evaluate(
input_fn=lambda: my_input_fn(FILE_TEST, False, 4))
print("Evaluation results")
for key in evaluate_result:
print(" {}, was: {}".format(key, evaluate_result[key]))
# Predict the type of some Iris flowers.
# Let's predict the examples in FILE_TEST, repeat only once.
predict_results = classifier.predict(
input_fn=lambda: my_input_fn(FILE_TEST, False, 1))
print("Predictions on test file")
for prediction in predict_results:
# Will print the predicted class, i.e: 0, 1, or 2 if the prediction
# is Iris Sentosa, Vericolor, Virginica, respectively.
print(prediction["class_ids"][0])
# Let create a dataset for prediction
# We've taken the first 3 examples in FILE_TEST
prediction_input = [[5.9, 3.0, 4.2, 1.5], # -> 1, Iris Versicolor
[6.9, 3.1, 5.4, 2.1], # -> 2, Iris Virginica
[5.1, 3.3, 1.7, 0.5]] # -> 0, Iris Sentosa
def new_input_fn():
def decode(x):
x = tf.split(x, 4) # Need to split into our 4 features
return dict(zip(feature_names, x)) # To build a dict of them
dataset = tf.data.Dataset.from_tensor_slices(prediction_input)
dataset = dataset.map(decode)
iterator = dataset.make_one_shot_iterator()
next_feature_batch = iterator.get_next()
return next_feature_batch, None # In prediction, we have no labels
# Predict all our prediction_input
predict_results = classifier.predict(input_fn=new_input_fn)
# Print results
print("Predictions:")
for idx, prediction in enumerate(predict_results):
type = prediction["class_ids"][0] # Get the predicted class (index)
if type == 0:
print(" I think: {}, is Iris Sentosa".format(prediction_input[idx]))
elif type == 1:
print(" I think: {}, is Iris Versicolor".format(prediction_input[idx]))
else:
print(" I think: {}, is Iris Virginica".format(prediction_input[idx]))
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