reshape = Reshape((MAX_LENGTH, embedding_dim, 1))(embedding) conv_0 = Conv2D(num_filters, kernel_size=(filter_sizes[0], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape) conv_1 = Conv2D(num_filters, kernel_size=(filter_sizes[1], embedding_dim...
def simple_img_conv_pool(input, filter_size, num_filters, pool_size, name=None, pool_type=None, act=None, groups=1, conv_stride=1, conv_padding=0, bias_attr=None,num_channel=None, param_attr=None,shared_bias=True, conv_layer_attr=None, pool_stride=1,pool_padding=0,pool_layer_attr...
num_filters,l2_reg_lambda=0.0):""":param sequence_length:The lengthofour sentences:param num_classes:Numberofclassesinthe outputlayer(pos and neg):param vocab_size:The sizeofour vocabulary:param embedding_size:The dimensionalityofour embeddings.:param filter_sizes:The numberofwords we want our ...
使用多个filter_window_size(原因是,这样不同的kernel可以获取不同范围内词的关系,获得的是纵向的差异信息,即类似于n-gram,也就是在一个句子中不同范围的词出现会带来什么信息。比如可以使用3,4,5个词数分别作为卷积核的大小),每个filter_window_size又有num_filters个卷积核(原因是卷积神经网络学习的是卷积核中...
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],...
(num_filters,kernel_size=(filter_sizes[2],embedding_dim),padding='valid',kernel_initializer='normal',activation='relu')(reshape)maxpool_0=MaxPool2D(pool_size=(sequence_length-filter_sizes[0]+1,1),strides=(1,1),padding='valid')(conv_0)maxpool_1=MaxPool2D(pool_size=(sequence_length-...
1defconvolutional_neural_network_org(img):2# first conv layer3conv_pool_1 = paddle.networks.simple_img_conv_pool(4input=img,5filter_size=3,6num_filters=20,7num_channel=1,8pool_size=2,9pool_stride=2,10act=paddle.activation.Relu())11# second conv layer12conv_pool_2 = paddle.networks...
num_classes=20 #类别数量 num_filters=128 #卷积核数量 filter_size=2 #卷积核大小 keep_prob=0.5 #dropout lr= 1e-3 #学习率 num_epochs=10 #epochs batch_size=64 #batch_size train_dir=r'E:\data\train.txt' #train data test_dir=r'E:\data\test.txt' #test data ...
num_filters: 本文设置为100,即不同kernel_size的卷积核,取100个。通过不同初始化参数,来达到多角度,更加全面的获取相对应ngram的信息。 x_size:暂时没用到,略。 l2_reg_lambda: L2,正则化惩罚系数。目的,防止模型因参数过于复杂:出现过拟合现象。通常,会在损失函数上,加上参数的惩罚,本文暂时取0,可根据模型...
def down_sample(num_filters): """stack two Conv-BatchNorm-Relu blocks and then a pooling layer to halve the feature size""" out=nn.HybridSequential() for_inrange(2): out.add(nn.Conv2D(num_filters,3,strides=1,padding=1)) out.add(nn.BatchNorm(in_channels=num_filters)) ...