update docs (#367)

examples/README.md

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+## Deprecated - Please Read
+
+Due to TensorFlow radically changing their API in v2, the examples index have been split between [v1](../tensorflow_v1) and [v2](../tensorflow_v2).
+
+The following examples are the original TF v1 examples, and will be deprecated entirely in favor of [tensorflow_v1](../tensorflow_v1) directory in a future release.

notebooks/README.md

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+## Deprecated - Please Read
+
+Due to TensorFlow radically changing their API in v2, the examples index have been split between [v1](../tensorflow_v1) and [v2](../tensorflow_v2).
+
+The following examples are the original TF v1 examples, and will be deprecated entirely in favor of [tensorflow_v1](../tensorflow_v1) directory in a future release.

tensorflow_v1/README.md

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+# TensorFlow v1 Examples
+
+All the following examples are the original TF v1 examples.
+
+*If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).*
+
+#### 0 - Prerequisite
+- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb).
+- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb).
+
+#### 1 - Introduction
+- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow.
+- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations.
+- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API.
+
+#### 2 - Basic Models
+- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow.
+- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API.
+- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow.
+- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API.
+- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow.
+- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow.
+- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow.
+- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow.
+- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow.
+
+#### 3 - Neural Networks
+##### Supervised
+
+- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation.
+- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.
+- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.
+- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.
+- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.
+- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.
+- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset.
+- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length.
+
+##### Unsupervised
+- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it.
+- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise.
+- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise.
+- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.
+
+#### 4 - Utilities
+- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow.
+- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss.
+- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more...
+
+#### 5 - Data Management
+- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file.
+- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline.
+- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...).
+- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them.
+- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training.
+
+#### 6 - Multi GPU
+- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow.
+- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs.
+
+## Installation
+
+To download all the examples, simply clone this repository:
+```
+git clone https://github.com/aymericdamien/TensorFlow-Examples
+```
+
+To run them, you also need the latest version of TensorFlow. To install it:
+```
+pip install tensorflow==1.15.0
+```
+
+or (with GPU support):
+```
+pip install tensorflow_gpu==1.15.0
+```
+
+For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://www.tensorflow.org/install/)
+
+## Dataset
+Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples.
+MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb).
+
+Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/).
+
+## More Examples
+The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api).
+
+### Tutorials
+- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier.
+
+### Examples
+- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn.

tensorflow_v1/examples/1_Introduction/basic_eager_api.py

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+'''
+Basic introduction to TensorFlow's Eager API.
+
+Author: Aymeric Damien
+Project: https://github.com/aymericdamien/TensorFlow-Examples/
+
+What is Eager API?
+" Eager execution is an imperative, define-by-run interface where operations are
+executed immediately as they are called from Python. This makes it easier to
+get started with TensorFlow, and can make research and development more
+intuitive. A vast majority of the TensorFlow API remains the same whether eager
+execution is enabled or not. As a result, the exact same code that constructs
+TensorFlow graphs (e.g. using the layers API) can be executed imperatively
+by using eager execution. Conversely, most models written with Eager enabled
+can be converted to a graph that can be further optimized and/or extracted
+for deployment in production without changing code. " - Rajat Monga
+
+'''
+from __future__ import absolute_import, division, print_function
+
+import numpy as np
+import tensorflow as tf
+import tensorflow.contrib.eager as tfe
+
+# Set Eager API
+print("Setting Eager mode...")
+tfe.enable_eager_execution()
+
+# Define constant tensors
+print("Define constant tensors")
+a = tf.constant(2)
+print("a = %i" % a)
+b = tf.constant(3)
+print("b = %i" % b)
+
+# Run the operation without the need for tf.Session
+print("Running operations, without tf.Session")
+c = a + b
+print("a + b = %i" % c)
+d = a * b
+print("a * b = %i" % d)
+
+
+# Full compatibility with Numpy
+print("Mixing operations with Tensors and Numpy Arrays")
+
+# Define constant tensors
+a = tf.constant([[2., 1.],
+                 [1., 0.]], dtype=tf.float32)
+print("Tensor:\n a = %s" % a)
+b = np.array([[3., 0.],
+              [5., 1.]], dtype=np.float32)
+print("NumpyArray:\n b = %s" % b)
+
+# Run the operation without the need for tf.Session
+print("Running operations, without tf.Session")
+
+c = a + b
+print("a + b = %s" % c)
+
+d = tf.matmul(a, b)
+print("a * b = %s" % d)
+
+print("Iterate through Tensor 'a':")
+for i in range(a.shape[0]):
+    for j in range(a.shape[1]):
+        print(a[i][j])
+