本实验分三步:
1. 建立数据集
2. 建立网络并训练
3. 可视化
import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.optimizers import SGD # 构建数据集 X_data = np.linspace(-1,1,100)[:, np.newaxis] noise = np.random.normal(0,0.05,X_data.shape) y_data = np.square(X_data) + noise + 0.5 # 构建神经网络 model = Sequential() model.add(Dense(10, input_dim=1, init='normal', activation='relu')) model.add(Dense(1, init='normal')) sgd = SGD(lr=0.1) model.compile(loss='mean_squared_error', optimizer=sgd) # 训练 model.fit(X_data, y_data, nb_epoch=1000, batch_size=100, verbose=0) # 在原数据上预测 y_predict=model.predict(X_data,batch_size=100,verbose=1) # 可视化 import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.scatter(X_data, y_data) ax.plot(X_data,y_predict,'r-',lw=5) plt.show()
训练结果:
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