Merge pull request #2066 from nd-02110114/fix-install-script

%% Cell type:markdown id: tags:

# Tutorial Part 2: Learning MNIST Digit Classifiers

In the previous tutorial, we learned some basics of how to load data into DeepChem and how to use the basic DeepChem objects to load and manipulate this data. In this tutorial, you'll put the parts together and learn how to train a basic image classification model in DeepChem. You might ask, why are we bothering to learn this material in DeepChem? Part of the reason is that image processing is an increasingly important part of AI for the life sciences. So learning how to train image processing models will be very useful for using some of the more advanced DeepChem features.

The MNIST dataset contains handwritten digits along with their human annotated labels. The learning challenge for this dataset is to train a model that maps the digit image to its true label. MNIST has been a standard benchmark for machine learning for decades at this point.

![MNIST](https://github.com/deepchem/deepchem/blob/master/examples/tutorials/mnist_examples.png?raw=1)

## Colab

This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/02_Learning_MNIST_Digit_Classifiers.ipynb)

## Setup

We recommend running this tutorial on Google colab. You'll need to run the following cell of installation commands on Colab to get your environment set up. If you'd rather run the tutorial locally, make sure you don't run these commands (since they'll download and install a new Anaconda python setup)

%% Cell type:code id: tags:

``` 

%tensorflow_version 1.x

!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py

import deepchem_installer

%time deepchem_installer.install(version='2.3.0')

!curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py

import conda_installer

conda_installer.install()

!/root/miniconda/bin/conda info -e

```

%% Output

    TensorFlow 1.x selected.

      % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current

                                     Dload  Upload   Total   Spent    Left  Speed

    100  3477  100  3477    0     0   9934      0 --:--:-- --:--:-- --:--:--  9934

    
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  3489  100  3489    0     0  28598      0 --:--:-- --:--:-- --:--:-- 28598

    add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH

    python version: 3.6.9

    fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh

    done

    installing miniconda to /root/miniconda

    done

    installing deepchem

    done

    /usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.

      warnings.warn(msg, category=FutureWarning)

    WARNING:tensorflow:

    The TensorFlow contrib module will not be included in TensorFlow 2.0.

    For more information, please see:

      * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md

      * https://github.com/tensorflow/addons

      * https://github.com/tensorflow/io (for I/O related ops)

    If you depend on functionality not listed there, please file an issue.

    all packages is already installed

    # conda environments:

    #

    base                  *  /root/miniconda

    deepchem-2.3.0 installation finished!

%% Cell type:code id: tags:

``` 

!pip install --pre deepchem

import deepchem

deepchem.__version__

```

%% Output

    Requirement already satisfied: deepchem in /usr/local/lib/python3.6/dist-packages (2.4.0rc1.dev20200805150209)

    Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from deepchem) (0.22.2.post1)

    Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from deepchem) (1.4.1)

    Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from deepchem) (1.18.5)

    Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from deepchem) (1.0.5)

    Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from deepchem) (0.16.0)

    Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->deepchem) (2018.9)

    Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->deepchem) (2.8.1)

    Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->deepchem) (1.15.0)

    CPU times: user 2.54 s, sys: 520 ms, total: 3.06 s

    Wall time: 1min 58s

    '2.4.0-rc1.dev'

%% Cell type:code id: tags:

``` 

from tensorflow.examples.tutorials.mnist import input_data

# from tensorflow.examples.tutorials.mnist import input_data

```

%% Cell type:code id: tags:

``` 

# TODO: This is deprecated. Let's replace with a DeepChem native loader for maintainability.

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

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

```

%% Output

    WARNING:tensorflow:From <ipython-input-3-227956e9c9c1>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.

    Instructions for updating:

    Please use alternatives such as official/mnist/dataset.py from tensorflow/models.

    WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.

    Instructions for updating:

    Please write your own downloading logic.

    WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.

    Instructions for updating:

    Please use urllib or similar directly.

    Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.

    WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.

    Instructions for updating:

    Please use tf.data to implement this functionality.

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

    Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.

    WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.

    Instructions for updating:

    Please use tf.data to implement this functionality.

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

    WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.

    Instructions for updating:

    Please use tf.one_hot on tensors.

    Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.

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

    Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.

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

    WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.

    Instructions for updating:

    Please use alternatives such as official/mnist/dataset.py from tensorflow/models.

%% Cell type:code id: tags:

``` 

import deepchem as dc

import tensorflow as tf

from tensorflow.keras.layers import Reshape, Conv2D, Flatten, Dense, Softmax

# import deepchem as dc

# import tensorflow as tf

# from tensorflow.keras.layers import Reshape, Conv2D, Flatten, Dense, Softmax

```

%% Cell type:code id: tags:

``` 

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

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

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

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

```

%% Cell type:code id: tags:

``` 

keras_model = tf.keras.Sequential([

    Reshape((28, 28, 1)),

    Conv2D(filters=32, kernel_size=5, activation=tf.nn.relu),

    Conv2D(filters=64, kernel_size=5, activation=tf.nn.relu),

    Flatten(),

    Dense(1024, activation=tf.nn.relu),

    Dense(10),

    Softmax()

])

model = dc.models.KerasModel(keras_model, dc.models.losses.CategoricalCrossEntropy())

# keras_model = tf.keras.Sequential([

#     Reshape((28, 28, 1)),

#     Conv2D(filters=32, kernel_size=5, activation=tf.nn.relu),

#     Conv2D(filters=64, kernel_size=5, activation=tf.nn.relu),

#     Flatten(),

#     Dense(1024, activation=tf.nn.relu),

#     Dense(10),

#     Softmax()

# ])

# model = dc.models.KerasModel(keras_model, dc.models.losses.CategoricalCrossEntropy())

```

%% Cell type:code id: tags:

``` 

model.fit(train, nb_epoch=2)

# model.fit(train, nb_epoch=2)

```

%% Output

    WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

    WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.

    WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

    WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

    WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:200: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

    WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.

    Instructions for updating:

    If using Keras pass *_constraint arguments to layers.

    0.0

%% Cell type:code id: tags:

``` 

from sklearn.metrics import roc_curve, auc

import numpy as np

# from sklearn.metrics import roc_curve, auc

# import numpy as np

print("Validation")

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

# print("Validation")

# prediction = np.squeeze(model.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]))

# 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]))

```

%% Output

    Validation

    class 0:auc=0.9999482812520925

    class 1:auc=0.9999327470315621

    class 2:auc=0.9999223382455529

    class 3:auc=0.9999378924197698

    class 4:auc=0.999804920932277

    class 5:auc=0.9997608046652174

    class 6:auc=0.9999347825797615

    class 7:auc=0.9997099080694587

    class 8:auc=0.999882187740275

    class 9:auc=0.9996286953889618

%% Cell type:markdown id: tags:

# Congratulations! Time to join the Community!

Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:

## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)

This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.

## Join the DeepChem Gitter

The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!