Keras中的两种模型:Sequential和Model用法

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

layers = [Dense(32, input_shape = (784,)),
   Activation('relu'),
   Dense(10),
   Activation('softmax')]

model = Sequential(layers)

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

model = Sequential()
model.add(Dense(32, input_shape = (784,)))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))

from keras.layers import Input, Dense
from keras.models import Model

# 定义输入层，确定输入维度
input = input(shape = (784, ))
# 2个隐含层，每个都有64个神经元，使用relu激活函数，且由上一层作为参数
x = Dense(64, activation='relu')(input)
x = Dense(64, activation='relu')(x)
# 输出层
y = Dense(10, activation='softmax')(x)
# 定义模型，指定输入输出
model = Model(input=input, output=y)
# 编译模型，指定优化器，损失函数，度量
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
# 模型拟合，即训练
model.fit(data, labels)

from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation
from keras.optimizers import SGD
import keras #abd
 
# Generate dummy data
import numpy as np
 
x_train=np.random.random((1000,20))
y_train=keras.utils.to_categorical(np.random.randint(10,size=(1000,1)),num_classes=10)
x_test=np.random.random((100,20))
y_test=keras.utils.to_categorical(np.random.randint(10,size=(100,1)),num_classes=10)
 
model=Sequential()
 
# Dense(64) is a fully-connected Layer with 64 hidden units.
# in the first layer ,you must specify the expected input data shape;
# here,20-dimensional vectors.
 
model.add(Dense(64,activation='relu',input_dim=20))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10,activation='softmax'))
 
sgd=SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
 
model.compile(loss='categorical_crossentropy', # 损失函数
    optimizer=sgd, # 
    metrics=['accuracy'] # 精确度，评估模型在训练和测试时的网络性能的指标。
    ) 
 
model.fit(x_train,y_train,epochs=20,batch_size=128) 
 
# batch_size 整数，指定进行梯度下降时每个批次包含的样本数训练时一个批次的样本
# 会被计算一次梯度下降，使目标函数进行一步优化
# epochs；训练20次，整数，训练终止时候的epoch值
score=model.evaluate(x_test,y_test,batch_size=128)
 
# 评估函数 ，本函数返回一个测试误差的标量值（如果模型没有其他评价指标）。

import numpy as np
from keras.models import Sequential
from keras.layers import Dense,Dropout
 
x_train=np.random.random((1000,20))
y_train=keras.utils.to_categorical(np.random.randint(10,size=(1000,1)),num_classes=10)
x_test=np.random.random((100,20))
y_test=keras.utils.to_categorical(np.random.randint(10,size=(100,1)),num_classes=10)
 
model=Sequential()
model.add(Dense(64,input_dim=20,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))
 
model.compile(loss='binary_crossentropy',
    optimizer='rmsprop',
    metrics=['accuracy'])
 
model.fit(x_train,y_train,
   epoches=20,
   batch_size=128)
 
score=model.evaluate(x_test,y_test,batch_size=128)