深度学习之简略分类

简略二元分类

制作数据

from sklearn.model_selection import train_test_splitfrom sklearn import datasetsimport matplotlib.pyplot as pltfrom tensorflow import kerasX,y = datasets.make_blobs(n_samples=1000,random_state=8,centers=2)plt.scatter(X[:,0],X[:,1],c=y)plt.show()

构建模型并训练

model = keras.models.Sequential([    keras.layers.Dense(32,input_shape=X.shape[1:]),    keras.layers.Dense(1,activation=keras.activations.sigmoid)])model.summary()model.compile(loss = keras.losses.binary_crossentropy,optimizer = keras.optimizers.RMSprop(learning_rate=0.1),metrics = [keras.metrics.Accuracy()])model.fit(X,y,validation_split=0.25,epochs = 20)

查看测试数据和预测数据

print(y[0:10])y_pre = model.predict(X[0:10])print(y_pre)[0 1 1 0 0 1 0 1 1 1][[0. 1. 1. 0. 0. 1. 0. 1. 1. 1.]]

多分类

制作数据

from sklearn.model_selection import train_test_splitfrom sklearn import datasetsimport matplotlib.pyplot as pltfrom tensorflow import kerasX,y = datasets.make_blobs(n_samples=1000,random_state=8,centers=3)plt.scatter(X[:,0],X[:,1],c=y)plt.show()

构建模型并训练

model = keras.models.Sequential([    keras.layers.Dense(32,input_shape=X.shape[1:], activation='relu'),    keras.layers.Dense(3,activation=keras.activations.softmax)])model.summary()model.compile(loss = keras.losses.sparse_categorical_crossentropy,              optimizer = keras.optimizers.Adam(),              metrics=['accuracy'])model.fit(X,y,validation_split=0.25,epochs = 20)

查看数据

print(y[0:10])y_pre = model.predict(X[0:10])import numpy as npprint(np.reshape(y_pre,[10,3]))[1 2 1 1 1 2 1 2 1 1][[4.50088549e-03 9.95355964e-01 1.43211320e-04] [3.52771860e-03 2.17666663e-03 9.94295657e-01] [5.39137749e-04 9.99391794e-01 6.91057867e-05] [3.10646836e-03 9.93669093e-01 3.22450022e-03] [1.59081508e-04 9.99381661e-01 4.59307077e-04] [3.76076205e-04 2.09796475e-03 9.97525990e-01] [1.03477845e-02 9.88485038e-01 1.16714649e-03] [8.82121618e-04 1.39025709e-04 9.98978853e-01] [2.29390264e-02 9.75332916e-01 1.72808918e-03] [4.69710241e-04 9.99316335e-01 2.13949577e-04]]