added tensorboard tutorial section

A TensorGraph model is pipeing around a series of layers.  Placing your Tensorflow code into a Tensorgraph means you don't have to worry about coding methods for fitting, training, and batching data, nor do you need to worry about writing picking and restoring functions. It also enables beautiful Tensorboard visualizations. 

### Layer

A Layer is a node in a graph.  A Layer class has a "name" and a __call__ function.  __call__ takes in a list of parent Layers, returns a tf.Tensor object, and sets the tf.Tensor object as self.out_tensor.  Below is an example layer which will reverse the order of the parent Tensor 0th axis.

A Layer is a node in a graph.  A Layer class has a "name" and a __call__ function.  __call__ takes in a list of parent Layers, returns a tf.Tensor object, and sets the tf.Tensor object as self.out_tensor.  Below is an example layer which will reverse the order of the parent Tensor's 0th axis.

``` python

class Reverse(Layer):

```

### Labels

Labels are the results we expect to get for each feature.  This is what we feed Dataset.y into when "fit"ing a model.

Labels are the results we expect to get for each feature.  We feed Dataset.y into a label when "fit"ing a model.

```

tf = TensorGraph()

label = Input(shape=(None, 2)) # Example Binary Classification label

```

### Loss

Loss is a scalar we wish to minimize for our network.  When training we will use tensorflow to intelligently minimize this value via gradient descent.

Loss is a scalar we wish to minimize for our network.  When training, we will use tensorflow to intelligently minimize this value via gradient descent.

``` python

tg = TensorGraph()

tg.add_layer(redmean, parents=[smce])

tg.set_loss(redmean)

```

Here in our classification problem we are attempting to reduce the mean of a SoftMaxCrossEntropy for a classification problem.

Here in our classification problem, we are attempting to reduce the mean of a SoftMaxCrossEntropy.

### Outputs

Outputs are your predictions for tasks.  A TensorGraph can have multiple outputs!  An example is a multi-task regression problem.

By convention for regression problems outputs are single values. Classification problem outputs are a probability vector,

By convention, regression problems' outputs are single values, while classification problems' outputs are probability vectors,

where each entry represents the probability of the input being in each of the classes.

``` python

4. Add Labels

5. Set Loss

## Using TensorBoard with TensorGraph

Visualizing layers is extremely easy with Tensorboard. After creating a layer, simply call the set_summary method. 

Tensorgraph currently supports the following summary ops: [histogram](https://www.tensorflow.org/api_docs/python/tf/summary/histogram), [scalar](https://www.tensorflow.org/api_docs/python/tf/summary/scalar), and [tensor_summary](https://www.tensorflow.org/api_docs/python/tf/summary/tensor_summary)

``` python

model_dir='example_dir'

tg = TensorGraph(model_dir=model_dir)

# a is Input tensor

dense = Dense(in_layers=[a])

dense.set_summary(summary_op='histogram')

```

Then after the model has finished training, simply run

``` bash

tensorboard --logdir=example_dir

```

Navigate to: [localhost:6006](localhost:6006) to view your visualizations. Tensorgraph will also have automatically constructed the graph visualization.

## Examples

TensorGraph on the MNIST

TensorGraph for Bypass MultiTask Classification

## Further Reading

Using Tensorboard with TensorGraph

Saving and Restoring TensorGraph

Experimenting on new architectures with TensorGraph