為您找到相關(guān)結(jié)果23個
解讀MaxPooling1D和GlobalMaxPooling1D的區(qū)別_python_腳本之家
y=keras.layers.GlobalMaxPool1D()(x) print("*"*20) print(y) ''' """Global average pooling operation for temporal data. Examples: >>> input_shape = (2, 3, 4) >>> x = tf.random.normal(input_shape) >>> y = tf.keras.
www.dbjr.com.cn/article/2702...htm 2025-5-21
卷積神經(jīng)網(wǎng)絡CharCNN實現(xiàn)中文情感分類任務_python_腳本之家
importnumpy as np fromkeras.layersimportActivation, Conv1D, Dense, Dropout, Embedding, Flatten, GlobalMaxPooling1D,Input fromkeras.modelsimportModel classCharCNN: def__init__(self, max_seq_length, num_classes, vocab_size, embedding_dim=128, filter_sizes=(1,2,3), num_filters=128, dropout_p...
www.dbjr.com.cn/article/2822...htm 2025-5-15
使用K.function()調(diào)試keras操作_python_腳本之家
model=Sequential() model.add(Embedding(nb_words,embedding_dims,input_length=maxlen)) model.add(Dropout(0.5)) model.add(Conv1D(cnn_filters, cnn_kernel_size,padding='valid', activation='relu')) model.add(GlobalMaxPooling1D()) model.add(Dense(dense_hidden_dims)) model.add(Dropout(0.5)) mod...
www.dbjr.com.cn/article/1888...htm 2025-5-28
如何利用Python開發(fā)一個簡單的猜數(shù)字游戲_python_腳本之家
fromkeras.layersimportConv2D, MaxPooling2D, GlobalAveragePooling2D # First, we need to import the 'random' module. # This module contains the functionality we need to be able to randomly select the winning number. importrandom # Now, we need to select a random number. # This line will se...
www.dbjr.com.cn/article/1705...htm 2025-5-15
利用python檢測文本相似性的三種方法_python_腳本之家
model.add(GlobalMaxPooling1D()) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 使用循環(huán)神經(jīng)網(wǎng)絡(RNN)進行文本查重 RNN可以捕捉文本之間的上下文信息。 1 2 3 4 5 6 7 from tensorflow.keras.layers import LSTM model =...
www.dbjr.com.cn/python/306076z...htm 2023-11-27
解讀tf.keras.layers模塊中的函數(shù)_python_腳本之家
from tensorflow.python.keras.layers.pooling import GlobalAveragePooling3D from tensorflow.python.keras.layers.pooling import GlobalAveragePooling3D as GlobalAvgPool3D from tensorflow.python.keras.layers.pooling import GlobalMaxPooling1D from tensorflow.python.keras.layers.pooling import GlobalMaxPooling1D as ...
www.dbjr.com.cn/article/2759...htm 2025-5-26
Python中6種中文文本情感分析的方法詳解_python_腳本之家
from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense # 加載訓練數(shù)據(jù) posdata = pd.read_excel('positive_data.xlsx', header=None)[0].tolist() negdata = pd.read_excel('negative_data.xlsx', header=None)[0].tolist() data = posdata + negdata labels = [1] * len(pos...
www.dbjr.com.cn/python/288758b...htm 2025-6-2
tensorflow2.10使用BERT實現(xiàn)Semantic Similarity過程解析_python_腳本之...
max_pool = tf.keras.layers.GlobalMaxPooling1D()(bi_lstm) concat = tf.keras.layers.concatenate([avg_pool, max_pool]) dropout = tf.keras.layers.Dropout(0.5)(concat) output = tf.keras.layers.Dense(3, activation="softmax")(dropout) model = tf.keras.models.Model(inputs=[input_ids, atten...
www.dbjr.com.cn/article/2808...htm 2025-5-28
TensorFlow2.0使用keras訓練模型的實現(xiàn)_python_腳本之家
x2 = layers.GlobalMaxPooling1D()(x2) x = layers.concatenate([x1, x2]) score_output = layers.Dense(1, name='score_output')(x) class_output = layers.Dense(5, activation='softmax', name='class_output')(x) model = keras.Model(inputs=[image_input, timeseries_input], outputs=[scor...
www.dbjr.com.cn/article/2059...htm 2025-5-14