使用Keras完成逻辑回归
Keras是一个开源的深度学习框架，能够高效地实现神经网络和深度学习模型。它由纽约大学的Francois Chollet开发，旨在提供一个简单易用的高层次API，以便开发人员能够快速搭建模型，从而节省时间和精力。Keras能够兼容各种底层深度学习框架，如TensorFlow、Theano和CNTK等。它已经成为深度学习领域中最受欢迎的框架之一，因为它既容易上手又具有灵活性。
Keras的设计初衷是让深度学习变得更容易，更快速地实现从数据到模型的过程。在使用Keras进行深度学习时，您无需编写多行代码来定义神经层、激活函数、优化器和损失函数等超参数，只需一行代码即可。此外，Keras还提供了丰富的预训练模型，可用于处理图像分类、自然语言处理、文本分类和序列分析等任务，从而大大减少了深度学习模型的开发和训练时间。
Keras还具有以下特点：
- 简单易用：Keras使用Python编写，提供了简单的API接口，让用户更加关注模型的设计和调整。
- 易于扩展：Keras可以兼容多种深度学习框架，如TensorFlow、Theano和CNTK等，能够利用它们的计算能力进行高效的训练和推理。
- 快速实现：Keras提供了多种预训练模型，无需从头开始开发模型，快速构建出高质量的深度学习模型。
- 支持多种语言：Keras不仅支持Python编程语言，还支持R和Java等其他编程语言。
- 开源社区：Keras在GitHub上有庞大的开源社区，拥有丰富的教程和示例，以便开发人员更好地学习和使用。
总之，Keras是一个简单易用、高效实现深度学习模型的框架，能够大大提升深度学习模型的开发和实现效率。
1. 导入Keras库
# 导入相关库 from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from sklearn import datasets import numpy as np import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore')
Using TensorFlow backend.
2. 生成数据集
# 生成样本数据集，两个特征列，两个分类二分类不需要onehot编码，直接将类别转换为0和1，分别代表正样本的概率。 X, y = datasets.make_classification(n_samples = 200, n_features = 2, n_informative = 2, n_redundant = 0, n_repeated = 0, n_classes = 2, n_clusters_per_class = 1) X, y
(array([[ 0.26364611, 0.77250816], [ 0.91698377, 0.9802208 ], [ 0.82634329, 0.9821341 ], [-0.83833456, 0.88223515], [ 1.11509338, 0.98632275], [ 1.04196821, 0.97892474], [ 0.77695264, 1.06320914], [-2.16804253, 0.15267335], [-1.96973867, 0.99244728], [-1.35368845, 1.25840447], [-0.52455148, 2.2351536 ], [ 1.08554563, 1.03795405], [ 0.88261697, 0.97793289], [-1.03718795, 0.53830131], [ 0.94628633, 0.96289949], [ 1.16190683, 1.01806263], [-2.07795249, 0.32376505], [ 0.9370119 , 1.01060097], [ 0.92750449, 0.98713143], [-0.35800128, 1.4498587 ], [-0.96709704, 1.77632874], [-0.55995817, 1.58782776], [ 0.88919948, 1.00133032], [ 1.16465115, 1.05117935], [-1.6969619 , 1.80088135], [ 1.06292602, 1.04594288], [-0.07792111, 0.98391779], [-1.05188451, 1.26871626], [-0.83494005, 0.93958161], [ 1.10371115, 1.03558148], [ 0.98674372, 1.04567265], [-1.08345028, 1.18601788], [-2.06487683, 0.17118219], [ 1.02734931, 0.99326938], [-0.11345441, 1.08515199], [ 0.97705823, 1.01751506], [-0.10872522, 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0.93526008], [-1.78345269, 1.53378105], [ 0.77408528, 1.01387371], [-1.47669576, 1.43472266], [ 1.19417792, 1.0440538 ], [ 1.15595665, 0.96823244], [ 0.84068935, 1.01792225], [ 1.11747629, 1.05722511], [ 0.23722569, 1.54396395], [-1.24609914, 0.30094681], [-0.18745572, 1.04657197], [ 0.90607352, 0.96120285], [-2.02612 , 0.44082817], [ 0.8762596 , 1.00607109], [ 0.98791921, 1.02441508], [-0.65307666, 1.22493946], [ 0.94162298, 1.28044258], [ 0.8622878 , 0.99707326], [-0.27590245, 1.1547649 ], [ 0.99268975, 1.02885589], [ 1.0635428 , 1.03445117], [-2.1378345 , 0.62797163], [-1.40559883, 0.26079323], [ 1.07732353, 1.01373432], [-1.74785838, 1.25425571], [-0.51461996, 1.2583831 ], [ 1.02632384, 1.00203908], [ 0.84413823, 2.99872324], [ 1.10319604, 0.9615482 ], [ 0.95870127, 1.0461775 ], [-1.61872726, 0.55348188], [ 1.22219183, 1.00893646], [-0.04807925, 1.69061295], [-3.86851327, -0.36829707], [-0.84318558, 0.71791949], [ 0.95549697, 1.02457587], [ 0.15484069, 0.80992914], [ 1.1947279 , 1.02301068], [-0.88323476, 1.52212056], [ 0.82715121, 0.99856576], [-0.97808876, 2.01262021], [-1.66906556, 0.70668215], [ 1.29672679, 0.64929896], [-0.45096669, 1.88364922], [-2.70110985, 0.36698604], [ 1.0795718 , 1.02443886], [ 0.99150574, 0.98348741], [-0.65205587, 1.86131659], [-0.56754302, 1.87827013], [ 1.12356817, 1.06645171], [-2.72752499, 0.43018586], [-2.74061782, -0.08021407], [-0.3200331 , 1.09683115], [ 1.0768664 , 1.0085724 ], [-3.6325113 , 0.67221516], [ 0.25830215, 0.79172286], [ 1.07796662, 1.00493526], [ 0.89606453, 0.98028498], [-0.94518278, 1.52377526], [ 0.90935946, 0.90695147], [ 1.0148515 , 1.06783713], [ 1.16686534, 0.99312304], [-1.31640844, 0.32636521], [-1.39485695, 0.47605367], [-0.50763796, 2.04039346], [-0.58489137, 1.16215935], [-1.21643673, 1.16555051], [-2.9813908 , -0.02123246], [ 1.05056765, 1.0129612 ], [ 1.01961575, 1.03539024], [ 1.01227271, 0.96751672], [ 0.12444867, 1.38342266], [ 0.99713663, 0.96095512], [ 0.98185855, 0.9941474 ], [ 0.92998157, 1.03644759], [-0.18646788, 2.02399395], [-1.79776907, 0.97067984], [-3.23433111, 0.54897531], [-2.18617596, 0.33414794], [ 1.16844027, 1.01821873], [ 1.0428281 , 1.01154471], [ 0.9159169 , 1.02463567], [-1.3578118 , 0.67183832], [-0.58824562, 1.08975919], [ 1.01775857, 1.00733938], [ 1.14847576, 1.01783862], [-1.1115874 , 0.42278247], [ 0.84772713, 0.99733494], [ 1.00417018, 0.93763177], [ 0.56134549, 1.20390517]]), array([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1]))
3. 构造神经网络模型
# 构建神经网络模型 model = Sequential() model.add(Dense(input_dim = 2, units = 1)) model.add(Activation('sigmoid'))
# 选定loss函数和优化器 model.compile(loss = 'binary_crossentropy', optimizer = 'sgd')
WARNING:tensorflow:From /home/nlp/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where
4. 训练模型
# 训练过程 print("Training ----------") for step in range(501): cost = model.train_on_batch(X, y) if step % 50 == 0: print("After %d trainings, the cost: %f" % (step, cost))
Training ---------- After 0 trainings, the cost: 0.370295 After 50 trainings, the cost: 0.349558 After 100 trainings, the cost: 0.331982 After 150 trainings, the cost: 0.316872 After 200 trainings, the cost: 0.303725 After 250 trainings, the cost: 0.292170 After 300 trainings, the cost: 0.281923 After 350 trainings, the cost: 0.272767 After 400 trainings, the cost: 0.264532 After 450 trainings, the cost: 0.257079 After 500 trainings, the cost: 0.250299
5. 测试模型
# 测试过程 print("Testing ----------") cost = model.evaluate(X, y, batch_size = 40) print("test cost:", cost) W, b = model.layers[0].get_weights() print('Weights = ', W, '\nbiases = ', b)
Testing ---------- 200/200 [==============================] - 0s 53us/step test cost: 0.25016908943653104 Weights = [[-1.7198342 ] [-0.18482684]] biases = [0.47288144]
6. 分析模型
# 将训练结果绘出 Y_pred = model.predict(X) # 将概率转化为类标号，概率在0-0.5时，转为0，概率在0.5-1时转为1 Y_pred = (Y_pred*2).astype('int')
# 绘制散点图 参数：x横轴 y纵轴 plt.subplot(2,1,1).scatter(X[:,0], X[:,1], c=Y_pred[:,0]) plt.subplot(2,1,2).scatter(X[:,0], X[:,1], c=y) plt.show()
