【笔记】机器学习 - 李宏毅 - 9 - Hello World of Deep Learning

3.1 configuration
3.2 寻找最优网络参数

代码示例：

from keras.models import Sequential
from keras.layers import Dense, Activation

# 1.Step 1
model = Sequential()
model.add(Dense(input_dim=28*28, output_dim=500)) # Dense是全连接
model.add(Activation('sigmoid'))
model.add(Dense(output_dim=500))
model.add(Activation('sigmoid'))
model.add(Dense(output_dim=10))
model.add(Activation('softmax'))

# Step 2
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Step 3
model.fit(x_train, y_train, batch_size=100, nb_epoch=20)

# 模型保存
#case1：测试集正确率
score = model.evaluate(x_test,y_test)
print("Total loss on Testing Set:", score[0])
print("Accuracy of Testing Set:", score[1])

#case2：模型预测
result = model.predict(x_test)

x_train, y_train解释

小批量梯度下降，速度更快的原因是因为可以并行计算。

【笔记】机器学习 - 李宏毅 - 9 - Hello World of Deep Learning

原文：https://www.cnblogs.com/yanqiang/p/11382214.html