<p>The details of please refer this <aclass="reference external"href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
@@ -835,18 +835,18 @@ Trailing new line (‘\n’) of each line will be removed.</p>
<h3>mnist<aclass="headerlink"href="#mnist"title="Permalink to this headline">¶</a></h3>
<p>MNIST dataset.</p>
<p>This module will download dataset from <aclass="reference external"href="http://yann.lecun.com/exdb/mnist/">http://yann.lecun.com/exdb/mnist/</a> and
parse train set and test set into paddle reader creators.</p>
parse training set and test set into paddle reader creators.</p>
<aclass="reference external"href="https://www.cs.toronto.edu/~kriz/cifar.html">https://www.cs.toronto.edu/~kriz/cifar.html</a> and parse train/test set into
paddle reader creators.</p>
<p>The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test
images.</p>
<p>The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
containing 600 images each. There are 500 training images and 100 testing
<aclass="reference external"href="http://ai.stanford.edu/%7Eamaas/data/sentiment/">http://ai.stanford.edu/%7Eamaas/data/sentiment/</a>. This dataset contains a set
of 25,000 highly polar movie reviews for training, and 25,000 for testing.
Besides, this module also provides API for building dictionary.</p>
<aclass="reference external"href="http://www.fit.vutbr.cz/~imikolov/rnnlm/">http://www.fit.vutbr.cz/~imikolov/rnnlm/</a> and parse training set and test set
<h3>movielens<aclass="headerlink"href="#movielens"title="Permalink to this headline">¶</a></h3>
<p>Movielens 1-M dataset.</p>
<p>TODO(yuyang18): Complete comments.</p>
<p>Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000
movies, which was collected by GroupLens Research. This module will download
Movielens 1-M dataset from
<aclass="reference external"href="http://files.grouplens.org/datasets/movielens/ml-1m.zip">http://files.grouplens.org/datasets/movielens/ml-1m.zip</a> and parse training
@@ -925,12 +1212,91 @@ parse train set and test set into paddle reader creators.</p>
<divclass="section"id="uci-housing">
<h3>uci_housing<aclass="headerlink"href="#uci-housing"title="Permalink to this headline">¶</a></h3>
<p>UCI Housing dataset.</p>
<p>TODO(yuyang18): Complete comments.</p>
<p>This module will download dataset from
<aclass="reference external"href="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/">https://archive.ics.uci.edu/ml/machine-learning-databases/housing/</a> and
parse training set and test set into paddle reader creators.</p>
<h3>wmt14<aclass="headerlink"href="#wmt14"title="Permalink to this headline">¶</a></h3>
<p>UCI Housing dataset.</p>
<p>TODO(yuyang18): Complete comments.</p>
<p>WMT14 dataset.
The original WMT14 dataset is too large and a small set of data for set is
provided. This module will download dataset from
<aclass="reference external"href="http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz">http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz</a> and
parse training set and test set into paddle reader creators.</p>
<li><strong>reader</strong> (<em>collections.Iterable</em>) – A reader that reads and yeilds data items. Usually we use a
batched reader to do mini-batch training.</li>
<li><strong>num_passes</strong>– The total train passes.</li>
<li><strong>event_handler</strong> (<em></em><em>(</em><em>BaseEvent</em><em>) </em><em>=> None</em>) – Event handler. A method will be invoked when event
occurred.</li>
<li><strong>feeding</strong> (<em>dict|list</em>) – Feeding is a map of neural network input name and array
<p>The details of please refer this <aclass="reference external"href="https://arxiv.org/abs/1412.6980">Adam: A Method for Stochastic Optimization</a></p>
<p>This module will download dataset from <aclass="reference external"href="http://yann.lecun.com/exdb/mnist/">http://yann.lecun.com/exdb/mnist/</a> and
parse train set and test set into paddle reader creators.</p>
parse training set and test set into paddle reader creators.</p>
<aclass="reference external"href="https://www.cs.toronto.edu/~kriz/cifar.html">https://www.cs.toronto.edu/~kriz/cifar.html</a> and parse train/test set into
paddle reader creators.</p>
<p>The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test
images.</p>
<p>The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
containing 600 images each. There are 500 training images and 100 testing
<aclass="reference external"href="http://ai.stanford.edu/%7Eamaas/data/sentiment/">http://ai.stanford.edu/%7Eamaas/data/sentiment/</a>. This dataset contains a set
of 25,000 highly polar movie reviews for training, and 25,000 for testing.
Besides, this module also provides API for building dictionary.</p>
<aclass="reference external"href="http://www.fit.vutbr.cz/~imikolov/rnnlm/">http://www.fit.vutbr.cz/~imikolov/rnnlm/</a> and parse training set and test set
<p>Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000
movies, which was collected by GroupLens Research. This module will download
Movielens 1-M dataset from
<aclass="reference external"href="http://files.grouplens.org/datasets/movielens/ml-1m.zip">http://files.grouplens.org/datasets/movielens/ml-1m.zip</a> and parse training
<aclass="reference external"href="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/">https://archive.ics.uci.edu/ml/machine-learning-databases/housing/</a> and
parse training set and test set into paddle reader creators.</p>
The original WMT14 dataset is too large and a small set of data for set is
provided. This module will download dataset from
<aclass="reference external"href="http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz">http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz</a> and
parse training set and test set into paddle reader creators.</p>
<li><strong>reader</strong> (<em>collections.Iterable</em>) – A reader that reads and yeilds data items. Usually we use a
batched reader to do mini-batch training.</li>
<li><strong>num_passes</strong>– The total train passes.</li>
<li><strong>event_handler</strong> (<em></em><em>(</em><em>BaseEvent</em><em>) </em><em>=> None</em>) – Event handler. A method will be invoked when event
occurred.</li>
<li><strong>feeding</strong> (<em>dict|list</em>) – Feeding is a map of neural network input name and array
