Changed Conv2d to Conv2D in test_mnist.py

%% Cell type:code id: tags:

``` python

from tensorflow.examples.tutorials.mnist import input_data

```

%% Cell type:code id: tags:

``` python

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

```

%% Output

    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

%% Cell type:code id: tags:

``` python

import sys

sys.path.append('/home/dibya/deepchem')

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

```

%% Output

    /home/dibya/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.

      "This module will be removed in 0.20.", DeprecationWarning)

    /home/dibya/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.

      DeprecationWarning)

%% Cell type:code id: tags:

``` python

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

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

```

%% Output

    ---------------------------------------------------------------------------

    NameError                                 Traceback (most recent call last)

    <ipython-input-4-98299b294029> in <module>()

    ----> 1 train = dc.data.NumpyDataset(mnist.train.images, mnist.train.labels)

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

    NameError: name 'dc' is not defined

%% Cell type:code id: tags:

``` python

dc

```

%% Output

    ---------------------------------------------------------------------------

    NameError                                 Traceback (most recent call last)

    <ipython-input-5-57180b2a4988> in <module>()

    ----> 1 dc

    NameError: name 'dc' is not defined

%% Cell type:code id: tags:

``` 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)

```

%% Output

    AttributeErrorTraceback (most recent call last)

    <ipython-input-5-2b44f7ddca0a> in <module>()

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

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

    ----> 3 tg.add_layer(feature)

          4 tg.add_feature(feature)

          5

    AttributeError: 'TensorGraph' object has no attribute 'add_layer'

%% Cell type:code id: tags:

``` python

tg.fit(train, nb_epoch=10)

tg.save()

```

%% Output

    Training for 10 epochs

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    Ending epoch 0: Average loss 0.198305

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    Ending epoch 1: Average loss 0.0536864

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    Ending epoch 2: Average loss 0.0425871

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    Ending epoch 3: Average loss 0.0336403

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    Ending epoch 5: Average loss 0.0257894

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    Ending epoch 6: Average loss 0.0187417

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    Ending epoch 7: Average loss 0.01757

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    Ending epoch 8: Average loss 0.0132135

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    Loggin

    Ending epoch 9: Average loss 0.0121854

    TIMING: model fitting took 77.903 s

%% Cell type:code id: tags:

``` 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]))

```

%% Output

    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

%% Cell type:code id: tags:

``` python

```

%% Cell type:code id: tags:

``` python

```