使用CNN做文本分类 from __future__ import division, print_function, absolute_import import tensorflow as tf import tflearn from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_1d, global_max_pool from tflearn.layers.merge_ops import merge from tflearn.layers.estimator import regression from tflearn.data_utils import to_categorical, pad_sequences from tflearn.datasets import imdb import pickle import numpy as np """ 还是加载imdb.pkl数据 """ train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.1) trainX, trainY = train testX, testY = test """ 转化为固定长度的向量,这里固定长度为100 """ trainX = pad_sequences(trainX, maxlen=100, value=0.) testX = pad_sequences(testX, maxlen=100, value=0.) """ 二值化向量 """ trainY = to_categorical(trainY, nb_classes=2) testY = to_categorical(testY, nb_classes=2) """ 构建卷积神经网络,这里卷积神经网网络为1d卷积 """ network = input_data(shape=[None, 100], name='input') network = tflearn.embedding(network, input_dim=10000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') """ 训练开始 """ model = tflearn.DNN(network, tensorboard_verbose=0) model.fit(trainX, trainY, n_epoch = 1, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32) """ 模型保存 """ model.save("cnn.model") """ 做测试使用 """ test=np.linspace(1,101,100).reshape(1,100) print("测试结果:",model.predict(test)) 模型训练结果以及模型保存情况: Training Step: 697 | total loss: 0.40838 | time: 79.960s | Adam | epoch: 001 | loss: 0.40838 - acc: 0.8247 -- iter: 22304/22500 Training Step: 698 | total loss: 0.39128 | time: 80.112s | Adam | epoch: 001 | loss: 0.39128 - acc: 0.8329 -- iter: 22336/22500 Training Step: 699 | total loss: 0.38896 | time: 80.298s | Adam | epoch: 001 | loss: 0.38896 - acc: 0.8402 -- iter: 22368/22500 Training Step: 700 | total loss: 0.39468 | time: 80.456s | Adam | epoch: 001 | loss: 0.39468 - acc: 0.8343 -- iter: 22400/22500 Training Step: 701 | total loss: 0.39380 | time: 80.640s | Adam | epoch: 001 | loss: 0.39380 - acc: 0.8353 -- iter: 22432/22500 Training Step: 702 | total loss: 0.38980 | time: 80.787s | Adam | epoch: 001 | loss: 0.38980 - acc: 0.8392 -- iter: 22464/22500 Training Step: 703 | total loss: 0.39020 | time: 80.970s | Adam | epoch: 001 | loss: 0.39020 - acc: 0.8397 -- iter: 22496/22500 Training Step: 704 | total loss: 0.38543 | time: 82.891s | Adam | epoch: 001 | loss: 0.38543 - acc: 0.8370 | val_loss: 0.44625 - val_acc: 0.7880 -- iter: 22500/22500 -- 测试结果: [[ 0.77064246 0.2293576 ]] 加载模型并做预测: import tensorflow as tf import numpy as np import tflearn from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_1d, global_max_pool from tflearn.layers.merge_ops import merge from tflearn.layers.estimator import regression """ 跟训练模型的网络结构一样 """ network = input_data(shape=[None, 100], name='input') network = tflearn.embedding(network, input_dim=10000, output_dim=128) branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2") branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2") branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2") network = merge([branch1, branch2, branch3], mode='concat', axis=1) network = tf.expand_dims(network, 2) network = global_max_pool(network) network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') """ 加载模型做预测 """ model = tflearn.DNN(network) model.load("cnn.model") test=np.linspace(1,101,100).reshape(1,100) # Predict [[ 0.7725634 0.22743654]] prediction = model.predict(test) print("模型预测结果",prediction) 结果: 2017-10-15 19:35:14.940689: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 模型预测结果 [[ 0.77064246 0.2293576 ]] Process finished with exit code 0 基于tflearn高阶api怎么做文本分类基本上完成
本站文章如无特殊说明,均为本站原创,如若转载,请注明出处:使用CNN做文本分类——将图像2维卷积换成1维 - Python技术站