如何从致密层中提取激活物

Question

我正在尝试实现这个纸的预处理代码(存储库中的代码)。本文描述了预处理代码：

“使用卷积神经网络(Kim，2014)从语音记录中提取文本特征。我们使用单一的卷积层，然后是最大池和完全连接的层来获得语音的特征表示。这个网络的输入是300维预先训练的840 b GloVe矢量(Pennington等人，2014年)。我们使用大小为3，4和5的过滤器，每个过滤器有50个特征地图。这些复杂的特性是最大的集合，窗口大小为2，然后是ReLU激活(Nair和Hinton，2010年)。，然后将这些连接到一个100维完全连接的层，它的激活形成了话语的表示。这个网络是用情感标签在话语层次上训练的。“

本文作者指出，在此存储库中可以找到CNN特征提取代码。但是，此代码用于执行序列分类的完整模型。除了粗体部分之外，它做以上引号中的所有事情(并进一步完成do分类)。我希望编辑代码来构建连接和输入到100 d层的代码，然后提取激活。要培训的数据可以在repo (其IMDB数据集)中找到。

每个序列的输出应该是一个(100，)张量。

以下是CNN模型的代码：

import tensorflow as tf
import numpy as np


class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
    """
    def __init__(
      self, sequence_length, num_classes, vocab_size,
      embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):

        # Placeholders for input, output and dropout
        self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
        self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
        self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

        # Keeping track of l2 regularization loss (optional)
        l2_loss = tf.constant(0.0)

        # Embedding layer
        with tf.device('/cpu:0'), tf.name_scope("embedding"):
            self.W = tf.Variable(
                tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
                name="W")
            self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

        # Create a convolution + maxpool layer for each filter size
        pooled_outputs = []
        for i, filter_size in enumerate(filter_sizes):
            with tf.name_scope("conv-maxpool-%s" % filter_size):
                # Convolution Layer
                filter_shape = [filter_size, embedding_size, 1, num_filters]
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                conv = tf.nn.conv2d(
                    self.embedded_chars_expanded,
                    W,
                    strides=[1, 1, 1, 1],
                    padding="VALID",
                    name="conv")
                # Apply nonlinearity
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                # Maxpooling over the outputs
                pooled = tf.nn.max_pool(
                    h,
                    ksize=[1, sequence_length - filter_size + 1, 1, 1],
                    strides=[1, 1, 1, 1],
                    padding='VALID',
                    name="pool")
                pooled_outputs.append(pooled)

        # Combine all the pooled features
        num_filters_total = num_filters * len(filter_sizes)
        self.h_pool = tf.concat(pooled_outputs, 3)
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])

        # Add dropout
        with tf.name_scope("dropout"):
            self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)

        # Final (unnormalized) scores and predictions
        with tf.name_scope("output"):
            W = tf.get_variable(
                "W",
                shape=[num_filters_total, num_classes],
                initializer=tf.contrib.layers.xavier_initializer())
            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
            l2_loss += tf.nn.l2_loss(W)
            l2_loss += tf.nn.l2_loss(b)
            self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
            self.predictions = tf.argmax(self.scores, 1, name="predictions")

        # Calculate mean cross-entropy loss
        with tf.name_scope("loss"):
            losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
            self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss

        # Accuracy
        with tf.name_scope("accuracy"):
            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")

我想把连接到100 d层中以获得激活，我在第59行(就在底部附近的# Add Dropout部分之前，然后注释掉它下面的其余部分)。我该怎么做？

Answer 1

你想要实现的卷积神经网络是NLP领域的一个很好的基线。这是纸首次推出这款手机(Kim，2014年)。

我发现您报告的代码非常有用，但可能比我们需要的要复杂。我试图用简单的keras重写网络(我只错过了正则化)。

def TextCNN(sequence_length, num_classes, vocab_size, 
            embedding_size, filter_sizes, num_filters, 
            embedding_matrix):

    sequence_input = Input(shape=(sequence_length,), dtype='int32')

    embedding_layer = Embedding(vocab_size,
                                embedding_size,
                                weights=[embedding_matrix],
                                input_length=sequence_length,
                                trainable=False)

    embedded_sequences = embedding_layer(sequence_input)

    convs = []
    for fsz in filter_sizes:
        x = Conv1D(num_filters, fsz, activation='relu', padding='same')(embedded_sequences)
        x = MaxPooling1D(pool_size=2)(x)
        convs.append(x)

    x = Concatenate(axis=-1)(convs)
    x = Flatten()(x)
    x = Dropout(0.5)(x)
    output = Dense(num_classes, activation='softmax')(x)

    model = Model(sequence_input, output)
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])

    return model

初始嵌入是通过在手套中学习权重来设置的。您可以使用其他技术(Word2Vec或FastText)上载它们或学习新的嵌入表示，并将它们上传。拟合值与往常一样计算。

我要强调的是，以上是网络的原始代表。如果您想在输出之前插入一个100密层，那么可以这样简单地修改它(这里是一个代码参考)：

def TextCNN(sequence_length, num_classes, vocab_size, 
            embedding_size, filter_sizes, num_filters, 
            embedding_matrix):

    sequence_input = Input(shape=(sequence_length,), dtype='int32')

    embedding_layer = Embedding(vocab_size,
                                embedding_size,
                                weights=[embedding_matrix],
                                input_length=sequence_length,
                                trainable=False)

    embedded_sequences = embedding_layer(sequence_input)

    convs = []
    for fsz in filter_sizes:
        x = Conv1D(num_filters, fsz, activation='relu', padding='same')(embedded_sequences)
        x = MaxPooling1D(pool_size=2)(x)
        convs.append(x)

    x = Concatenate(axis=-1)(convs)
    x = Flatten()(x)
    x = Dense(100, activation='relu', name='extractor')(x)
    x = Dropout(0.5)(x)
    output = Dense(num_classes, activation='softmax')(x)

    model = Model(sequence_input, output)
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])

    return model

model = TextCNN(sequence_length=50, num_classes=10, vocab_size=3333, 
        embedding_size=100, filter_sizes=[3,4,5], num_filters=50, 
        embedding_matrix)

model.fit(....)

为了提取我们感兴趣的特性，我们需要我们的Dense100 (我们命名为“提取器”)的输出。我还建议使用本教程进行过滤和特征提取。

extractor = Model(model.input, model.get_layer('extractor').output)
representation = extractor.predict(np.random.randint(0,200, (1000,50)))

representation将是一个形状数组(n_sample，100)