Move MNIST tests to examples

import unittest

import deepchem as dc

import tensorflow as tf

import numpy as np

import os

from nose.tools import assert_true

import deepchem as dc

from deepchem.data import NumpyDataset

from deepchem.data.datasets import Databag

from deepchem.models.tensorgraph.layers import Input, Dense, LossLayer, Flatten, ReduceSquareDifference

from deepchem.models.tensorgraph.layers import Layer, Input, Reshape, Flatten, Feature, Conv2d, MaxPool, Label

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

from deepchem.models.tensorgraph.layers import Feature, Label

from deepchem.models.tensorgraph.layers import ReduceSquareDifference

from deepchem.models.tensorgraph.tensor_graph import TensorGraph

from nose.tools import nottest, assert_true, assert_equals

from sklearn.metrics import roc_curve, auc

import numpy as np

class TestTensorGraph(unittest.TestCase):

      y_pred = prediction[:, i, :]

      assert_true(np.all(np.isclose(y_pred, y_real, atol=0.5)))

  def test_mnist(self):

    from tensorflow.examples.tutorials.mnist import input_data

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

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

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

                                 mnist.validation.labels)

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

    feature = Feature(shape=(None, 784), name="Feature")

    make_image = Reshape(shape=(-1, 28, 28, 1), in_layers=[feature])

    conv2d_1 = Conv2d(num_outputs=32, in_layers=[make_image])

    maxpool_1 = MaxPool(in_layers=[conv2d_1])

    conv2d_2 = Conv2d(num_outputs=64, in_layers=[maxpool_1])

    maxpool_2 = MaxPool(in_layers=[conv2d_2])

    flatten = Flatten(in_layers=[maxpool_2])

    dense1 = Dense(

        out_channels=1024, activation_fn=tf.nn.relu, in_layers=[flatten])

    dense2 = Dense(out_channels=10, in_layers=[dense1])

    label = Label(shape=(None, 10), name="Label")

    smce = SoftMaxCrossEntropy(in_layers=[label, dense2])

    loss = ReduceMean(in_layers=[smce])

    output = SoftMax(in_layers=[dense2])

    tg = dc.models.TensorGraph(

        model_dir='/tmp/mnist', batch_size=1000, use_queue=True)

    tg.add_output(output)

    tg.set_loss(loss)

    tg.fit(train, nb_epoch=2)

    prediction = np.squeeze(tg.predict_proba_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])

      assert_true(roc_auc[i] > 0.99)

  def test_no_queue(self):

    n_data_points = 20

    n_features = 2

from tensorflow.examples.tutorials.mnist import input_data

from deepchem.models import TensorGraph

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

import deepchem as dc

import tensorflow as tf

from deepchem.models.tensorgraph.layers import Layer, Input, Reshape, Flatten, Feature, Conv2d, MaxPool, Label

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

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

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

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

feature = Feature(shape=(None, 784), name="Feature")

make_image = Reshape(shape=(-1, 28, 28, 1), in_layers=[feature])

conv2d_1 = Conv2d(num_outputs=32, in_layers=[make_image])

maxpool_1 = MaxPool(in_layers=[conv2d_1])

conv2d_2 = Conv2d(num_outputs=64, in_layers=[maxpool_1])

maxpool_2 = MaxPool(in_layers=[conv2d_2])

flatten = Flatten(in_layers=[maxpool_2])

dense1 = Dense(out_channels=1024, activation_fn=tf.nn.relu, in_layers=[flatten])

dense2 = Dense(out_channels=10, in_layers=[dense1])

label = Label(shape=(None, 10), name="Label")

smce = SoftMaxCrossEntropy(in_layers=[label, dense2])

loss = ReduceMean(in_layers=[smce])

output = SoftMax(in_layers=[dense2])

tg = dc.models.TensorGraph(

    tensorboard=True, model_dir='/tmp/mnist', batch_size=1000, use_queue=True)

tg.add_output(output)

tg.set_loss(loss)

tg.fit(train, nb_epoch=2)

tg.fit(train, nb_epoch=2)

from sklearn.metrics import roc_curve, auc

import numpy as np

print("Validation")

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

print(prediction[0])

print(valid.y[0])

fpr = dict()

tpr = dict()

roc_auc = dict()

for i in range(10):

  l = valid.y[:, i]

  print(l.shape)

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

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

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

import unittest

import numpy as np

import tensorflow as tf

from nose.tools import assert_true

from sklearn.metrics import roc_curve, auc

import deepchem as dc

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

from deepchem.models.tensorgraph.layers import Reshape, Flatten, Feature, Conv2d, MaxPool, Label

class TestTensorGraphMNIST(unittest.TestCase):

  def test_mnist(self):

    from tensorflow.examples.tutorials.mnist import input_data

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

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

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

                                 mnist.validation.labels)

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

    feature = Feature(shape=(None, 784), name="Feature")

    make_image = Reshape(shape=(-1, 28, 28, 1), in_layers=[feature])

    conv2d_1 = Conv2d(num_outputs=32, in_layers=[make_image])

    maxpool_1 = MaxPool(in_layers=[conv2d_1])

    conv2d_2 = Conv2d(num_outputs=64, in_layers=[maxpool_1])

    maxpool_2 = MaxPool(in_layers=[conv2d_2])

    flatten = Flatten(in_layers=[maxpool_2])

    dense1 = Dense(

        out_channels=1024, activation_fn=tf.nn.relu, in_layers=[flatten])

    dense2 = Dense(out_channels=10, in_layers=[dense1])

    label = Label(shape=(None, 10), name="Label")

    smce = SoftMaxCrossEntropy(in_layers=[label, dense2])

    loss = ReduceMean(in_layers=[smce])

    output = SoftMax(in_layers=[dense2])

    tg = dc.models.TensorGraph(

        model_dir='/tmp/mnist', batch_size=1000, use_queue=True)

    tg.add_output(output)

    tg.set_loss(loss)

    tg.fit(train, nb_epoch=2)

    prediction = np.squeeze(tg.predict_proba_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])

      assert_true(roc_auc[i] > 0.99)