Unverified Commit 2d497b1c authored by Bharath Ramsundar's avatar Bharath Ramsundar Committed by GitHub
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Merge pull request #1865 from deepchem/documentation_overhaul 

Overhauling DeepChem Documentation
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docs/MNIST.md

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```python

from tensorflow.examples.tutorials.mnist import input_data

```





```python

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

```



    Extracting MNIST_data/train-images-idx3-ubyte.gz

    Extracting MNIST_data/train-labels-idx1-ubyte.gz

    Extracting MNIST_data/t10k-images-idx3-ubyte.gz

    Extracting MNIST_data/t10k-labels-idx1-ubyte.gz







```python

import deepchem as dc

import tensorflow as tf

from deepchem.models.tensorgraph.layers import Layer, Input, Reshape, Flatten

from deepchem.models.tensorgraph.layers import Dense, SoftMaxCrossEntropy, ReduceMean, SoftMax

```





```python

train = dc.data.NumpyDataset(mnist.train.images, mnist.train.labels)

valid = dc.data.NumpyDataset(mnist.validation.images, mnist.validation.labels)

```





```python

tg = dc.models.TensorGraph(tensorboard=True, model_dir='/tmp/mnist')

feature = Input(shape=(None, 784))

tg.add_layer(feature)

tg.add_feature(feature)



# Images are square 28x28 (batch, height, width, channel)

make_image = Reshape(shape=(-1, 28, 28, 1))

tg.add_layer(make_image, parents=[feature])



class Conv2d(Layer):

    def __init__(self, num_outputs, kernel_size=5, **kwargs):

        self.num_outputs = num_outputs

        self.kernel_size = kernel_size

        super(Conv2d, self).__init__(**kwargs)

    def __call__(self, *parents):

        parent_tensor = parents[0].out_tensor

        out_tensor = tf.contrib.layers.conv2d(parent_tensor,

                                              num_outputs=self.num_outputs,

                                              kernel_size = self.kernel_size,

                                              padding="SAME",

                                              activation_fn=tf.nn.relu,

                                              normalizer_fn=tf.contrib.layers.batch_norm)

        self.out_tensor = tf.nn.max_pool(out_tensor,

                                         ksize=[1, 2, 2, 1],

                                         strides=[1, 2, 2, 1],

                                         padding='SAME')

        return self.out_tensor

conv2d_1 = Conv2d(num_outputs=32)

tg.add_layer(conv2d_1, parents=[make_image])



conv2d_2 = Conv2d(num_outputs=64)

tg.add_layer(conv2d_2, parents=[conv2d_1])



flatten = Flatten()

tg.add_layer(flatten, parents=[conv2d_2])



dense1 = Dense(out_channels=1024, activation_fn=tf.nn.relu)

tg.add_layer(dense1, parents=[flatten])



dense2 = Dense(out_channels=10)

tg.add_layer(dense2, parents=[dense1])



label = Input(shape=(None, 10))

tg.add_layer(label, parents=list())

tg.add_label(label)



smce = SoftMaxCrossEntropy()

tg.add_layer(smce, parents=[label, dense2])



loss = ReduceMean()

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

tg.set_loss(loss)



output = SoftMax()

tg.add_layer(output, parents=[dense2])

tg.add_output(output)

```





```python

tg.fit(train, nb_epoch=10)

tg.save()

```



    Training for 10 epochs

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    Ending epoch 0: Average loss 0.198305

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    Loggin

    Ending epoch 9: Average loss 0.0121854

    TIMING: model fitting took 77.903 s







```python

from sklearn.metrics import roc_curve, auc

import numpy as np



print("Validation")

prediction = np.squeeze(tg.predict_on_batch(valid.X))



fpr = dict()

tpr = dict()

roc_auc = dict()

for i in range(10):

    fpr[i], tpr[i], thresh = roc_curve(valid.y[:, i], prediction[:, i])

    roc_auc[i] = auc(fpr[i], tpr[i])

    print("class %s:auc=%s" % (i, roc_auc[i]))

```



    Validation

    class 0:auc=0.99987578265

    class 1:auc=0.999975981083

    class 2:auc=0.99987010958

    class 3:auc=0.999980197583

    class 4:auc=0.999985348139

    class 5:auc=0.999889990331

    class 6:auc=0.999762644083

    class 7:auc=0.999936670072

    class 8:auc=0.999936085657

    class 9:auc=0.999968609514







```python



```





```python



```

docs/PDBBIND.md

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# Setting up PDBBind example



First, ensure that you are in the correct environment, and then download the pdbbind reference database:

```bash

source activate deepchem

(deepchem) user@server:~/deepchem/examples/pdbbind$ ./get_pdbbind.sh

```



You will see a large number of directories created with PDB (protein) and SDF (ligand) files, for instance:

```bash

v2015/3d1g/3d1g_ligand.mol2

v2015/3d1g/3d1g_pocket.pdb

v2015/3d1g/3d1g_protein.pdb

v2015/3d1g/3d1g_ligand.sdf

```



Next, you can train the Random Forest-based classifier via:

```bash

(deepchem) ubuntu@ip-172-31-12-186:~/deepchem/examples/pdbbind$ python pdbbind_rf.py 

```



TODO: Add notes about how to expand the v2015 dataset, e.g. to add protein 14HR as associated with NF2 to the analysis

docs/README.md

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# DeepChem Docs Overview



This directory contains the DeepChem docs. DeepChem's docs aim to

serve as another resource to complement our collection of tutorials

and examples.



## Building the Documentation



To build the docs, you can use the `Makefile` that's been added to

this directory. (Note that `deepchem` must be installed first.) To

generate docs in html, run



```

pip install -r requirements.txt

make html

open _build/html/index.html

```



You can generate docs in other formats as well if you like. To clean up past builds run



```

make clean

```

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