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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/03/28 17:55:30 UTC
[GitHub] [incubator-mxnet] abhinavs95 commented on a change in pull request
#14547: [MXNet-1375][Fit API]Added RNN integration test for fit() API
abhinavs95 commented on a change in pull request #14547: [MXNet-1375][Fit API]Added RNN integration test for fit() API
URL: https://github.com/apache/incubator-mxnet/pull/14547#discussion_r270127302
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File path: tests/nightly/estimator/test_sentiment_rnn.py
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Gluon Text Sentiment Classification Example using RNN/CNN
+Example modified from below link:
+https://github.com/d2l-ai/d2l-en/blob/master/chapter_natural-language-processing/sentiment-analysis-rnn.md
+https://github.com/d2l-ai/d2l-en/blob/master/chapter_natural-language-processing/sentiment-analysis-cnn.md"""
+
+import argparse
+import os
+import tarfile
+import random
+import collections
+import mxnet as mx
+from mxnet import nd
+from mxnet.contrib import text
+from mxnet.gluon import data as gdata, loss as gloss, utils as gutils, nn, rnn
+from mxnet.gluon.estimator import estimator as est
+
+
+class TextCNN(nn.Block):
+ def __init__(self, vocab, embed_size, kernel_sizes, num_channels,
+ **kwargs):
+ super(TextCNN, self).__init__(**kwargs)
+ self.embedding = nn.Embedding(len(vocab), embed_size)
+ # The embedding layer does not participate in training
+ self.constant_embedding = nn.Embedding(len(vocab), embed_size)
+ self.dropout = nn.Dropout(0.5)
+ self.decoder = nn.Dense(2)
+ # The max-over-time pooling layer has no weight, so it can share an
+ # instance
+ self.pool = nn.GlobalMaxPool1D()
+ # Create multiple one-dimensional convolutional layers
+ self.convs = nn.Sequential()
+ for c, k in zip(num_channels, kernel_sizes):
+ self.convs.add(nn.Conv1D(c, k, activation='relu'))
+
+ def forward(self, inputs):
+ # Concatenate the output of two embedding layers with shape of
+ # (batch size, number of words, word vector dimension) by word vector
+ embeddings = nd.concat(
+ self.embedding(inputs), self.constant_embedding(inputs), dim=2)
+ # According to the input format required by Conv1D, the word vector
+ # dimension, that is, the channel dimension of the one-dimensional
+ # convolutional layer, is transformed into the previous dimension
+ embeddings = embeddings.transpose((0, 2, 1))
+ # For each one-dimensional convolutional layer, after max-over-time
+ # pooling, an NDArray with the shape of (batch size, channel size, 1)
+ # can be obtained. Use the flatten function to remove the last
+ # dimension and then concatenate on the channel dimension
+ encoding = nd.concat(*[nd.flatten(
+ self.pool(conv(embeddings))) for conv in self.convs], dim=1)
+ # After applying the dropout method, use a fully connected layer to
+ # obtain the output
+ outputs = self.decoder(self.dropout(encoding))
+ return outputs
+
+
+class BiRNN(nn.Block):
+ def __init__(self, vocab, embed_size, num_hiddens, num_layers, **kwargs):
+ super(BiRNN, self).__init__(**kwargs)
+ self.embedding = nn.Embedding(len(vocab), embed_size)
+ # Set Bidirectional to True to get a bidirectional recurrent neural
+ # network
+ self.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers,
+ bidirectional=True, input_size=embed_size)
+ self.decoder = nn.Dense(2)
+
+ def forward(self, inputs):
+ # The shape of inputs is (batch size, number of words). Because LSTM
+ # needs to use sequence as the first dimension, the input is
+ # transformed and the word feature is then extracted. The output shape
+ # is (number of words, batch size, word vector dimension).
+ embeddings = self.embedding(inputs.T)
+ # The shape of states is (number of words, batch size, 2 * number of
+ # hidden units).
+ states = self.encoder(embeddings)
+ # Concatenate the hidden states of the initial time step and final
+ # time step to use as the input of the fully connected layer. Its
+ # shape is (batch size, 4 * number of hidden units)
+ encoding = nd.concat(states[0], states[-1])
+ outputs = self.decoder(encoding)
+ return outputs
+
+
+def download_imdb(data_dir='./data'):
+ '''
+ Download and extract the IMDB dataset
+ '''
+ url = ('http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz')
+ sha1 = '01ada507287d82875905620988597833ad4e0903'
+ if not os.path.exists(data_dir):
+ os.makedirs(data_dir)
+ file_path = os.path.join(data_dir, 'aclImdb_v1.tar.gz')
+ if not os.path.isfile(file_path):
+ file_path = gutils.download(url, data_dir, sha1_hash=sha1)
+ with tarfile.open(file_path, 'r') as f:
+ f.extractall(data_dir)
+
+
+def read_imdb(folder='train'):
+ '''
+ Read the IMDB dataset
+ '''
+ data = []
+ for label in ['pos', 'neg']:
+ folder_name = os.path.join('./data/aclImdb/', folder, label)
+ for file in os.listdir(folder_name):
+ with open(os.path.join(folder_name, file), 'rb') as f:
+ review = f.read().decode('utf-8').replace('\n', '').lower()
+ data.append([review, 1 if label == 'pos' else 0])
+ random.shuffle(data)
+ return data
+
+
+def get_tokenized_imdb(data):
+ '''
+ Tokenized the words
+ '''
+
+ def tokenizer(text):
+ return [tok.lower() for tok in text.split(' ')]
+
+ return [tokenizer(review) for review, _ in data]
+
+
+def get_vocab_imdb(data):
+ '''
+ Get the indexed tokens
+ '''
+ tokenized_data = get_tokenized_imdb(data)
+ counter = collections.Counter([tk for st in tokenized_data for tk in st])
+ return text.vocab.Vocabulary(counter, min_freq=5)
+
+
+def preprocess_imdb(data, vocab):
+ '''
+ Make the length of each comment 500 by truncating or adding 0s
+ '''
+ max_l = 500
+
+ def pad(x):
+ return x[:max_l] if len(x) > max_l else x + [0] * (max_l - len(x))
+
+ tokenized_data = get_tokenized_imdb(data)
+ features = nd.array([pad(vocab.to_indices(x)) for x in tokenized_data])
+ labels = nd.array([score for _, score in data])
+ return features, labels
+
+
+def test_estimator_cpu():
+ '''
+ Test estimator by doing one pass over each model with synthetic data
+ '''
+ models = ['TextCNN', 'BiRNN']
+ context = mx.cpu()
+ batch_size = 64
+ num_epochs = 1
+ lr = 0.01
+ embed_size = 100
+
+ train_data = mx.nd.random.randint(low=0, high=100, shape=(2 * batch_size, 500))
+ train_label = mx.nd.random.randint(low=0, high=2, shape=(2 * batch_size,))
+ val_data = mx.nd.random.randint(low=0, high=100, shape=(batch_size, 500))
+ val_label = mx.nd.random.randint(low=0, high=2, shape=(batch_size,))
+
+ train_dataloader = gdata.DataLoader(dataset=gdata.ArrayDataset(train_data, train_label),
+ batch_size=batch_size, shuffle=True)
+ val_dataloader = gdata.DataLoader(dataset=gdata.ArrayDataset(val_data, val_label),
+ batch_size=batch_size)
+ vocab_list = mx.nd.zeros(shape=(100,))
+
+ # Get the model
+ for model in models:
+ if model == 'TextCNN':
Review comment:
To avoid splitting logic here we can have a get_model() function which returns the net and related items, and use it for both the tests
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