final edits

# Using TensorGraph

Tensorgraph is the newest version of DeepChem's Tensorflow model building framework.

Tensorgraph is designed to be incredibly flexible for users attempting research as well as being easy to learn for Deep Learning beginners.

TensorGraph is the newest version of DeepChem's TensorFlow model building framework.

TensorGraph is designed to be incredibly flexible for users attempting research as well as being easy to learn for beginners to deep learning.

## Vocabulary

### TensorGraph

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. 

A TensorGraph model is about pipeing around 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 pickling 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 create_tensor function.  create_tensor takes in a list of input 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.

A Layer is a node in a graph.  A Layer class has a "name" and a create_tensor function.  create_tensor takes in a list of input 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):

  def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):

    parent_out = in_layers[0].out_tensor

    parent_out = self._get_input_tensors(in_layers[0], True)

    out_tensor = tf.reverse(parent_out, axis=0)

    if set_tensors:

    	self.out_tensor = out_tensor

    return out_tensor

```

You can combine these layers to form a Graph of computation by combining these layers on a tensorgraph.

``` python

tg = TensorGraph()

feature = Input(shape=(None, 5))

reverse = Reverse(in_layers=[feature]) # From Above

```

Now during computation, the reverse layer will reverse the feature layer.

### Features

Features are how we feed input data into our model.  During training or predicting, Tensorgraph will set the values of feature layers from Dataset.X.  We have to manually tell TensorGraph which layers we want to feed data into.

Features are how we feed input data into our model.  During training or predicting, TensorGraph will set the values of feature layers from Dataset.X.  We have to manually tell TensorGraph which layers we want to feed data into.

We can then form a computation graph by combining layers on a TensorGraph. 

``` python

tg = TensorGraph()

# Convention is to set batch size as 0th axis.

# None means any batch size is allowed.

feature = Feature(shape=(None, 5))

reverse = Reverse(in_layers=[feature]) #From Above

```

Now during computation, the reverse layer will reverse the feature layer.

### Labels

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

```

```

### 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()

# a is labels for a classification problem

# b is our guesses for the classification problem

smce = SoftMaxCrossEntropy(in_layers=[a,b])

redmean = ReduceMean(in_layers=[smce])

tg.set_loss(redmean)

red_mean = ReduceMean(in_layers=[smce])

tg.set_loss(red_mean)

```

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.

Outputs are your predictions for tasks.  A TensorGraph can have multiple outputs, such is the case in a multi-task regression problem.

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.

5. Set Loss

## Using TensorBoard with TensorGraph

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

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

# a is a Feature tensor

dense = Dense(in_layers=[a])

dense.set_summary(summary_op='histogram')

```

``` bash

tensorboard --logdir=example_dir

```

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

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 on the MNIST](MNIST.md)

TensorGraph for Bypass MultiTask Classification