Keras学习

1. 回归

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
np.random.seed(2018)

#构造数据
X=np.linspace(-1,1,200)
np.random.shuffle(X)
Y=0.5*X+2+np.random.normal(0,0.05,X.shape)

N=160
X_train,Y_train=X[:N],Y[:N]
X_test,Y_test=X[N:],Y[N:]

#构建模型
model=Sequential()
model.add(Dense(output_dim=1,input_dim=1))

#损失函数和优化器选择
model.compile(loss='mse',optimizer='sgd')

for i in range(301):
    cost=model.train_on_batch(X_train,Y_train)
    if i%100==0:
        print("Train cost:",cost)

cost=model.evaluate(X_test,Y_test)
print("Test cost:",cost)

w,b=model.layers[0].get_weights()
print("Weights:",w,"Bias:",b)

Y_pred=model.predict(X_test)
plt.scatter(X_test,Y_test)
plt.plot(X_test,Y_pred,color='r')
plt.show()
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2. 分类

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Activation

(X_train,Y_train),(X_test,Y_test)=mnist.load_data()
print("(X_train,Y_train),(X_test,Y_test):",
      X_train.shape,Y_train.shape,X_test.shape,Y_test.shape)

X_train,X_test=X_train.reshape(X_train.shape[0],-1)/255,X_test.reshape(X_test.shape[0],-1)/255 #一定要归一化，不然最后很可能不收敛！
Y_train,Y_test=np_utils.to_categorical(Y_train,nb_classes=10),np_utils.to_categorical(Y_test,nb_classes=10)
print("(X_train,Y_train),(X_test,Y_test):",
      X_train.shape,Y_train.shape,X_test.shape,Y_test.shape)

model=Sequential()
model.add(Dense(output_dim=32,input_dim=X_train.shape[1]))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))

model.compile(#optimizer=Adade,
              optimizer='adadelta',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
################################Training..............############################################
epoch=10
batchsize=32
N_train=X_train.shape[0]
N_test=X_test.shape[0]

def get_batch(j,batchsize,N,X,Y):
    start_index,end_index=j*batchsize,j*batchsize+batchsize
    if end_index>N:
        end_index=N
    return X[start_index:end_index,:],Y[start_index:end_index,:]

for i in range(epoch):
    for j in range(N_train//batchsize):
        X_batch,Y_batch=get_batch(j,batchsize,N_train,X_train,Y_train)
        # print('X_batch,Y_batch:',X_batch.shape,Y_batch.shape)
        cost,accuracy= model.train_on_batch(X_batch,Y_batch)
        if j%100==0:
            print('Trainging-------epoch:',i,' batch:', j, 'loss:',cost,' accuracy:',accuracy)
            # print('Trainging-------epoch:', i, ' loss:', cost)

loss, accuracy =model.evaluate(X_test,Y_test)
print('Testing------- loss:',loss,' accuracy:',accuracy)
####################################################################################################

#或者直接用下面的keras的训练模型
# model.fit(X_train,Y_train)
# loss, accuracy =model.evaluate(X_test,Y_test)
# print('Testing------- loss:',loss,' accuracy:',accuracy)
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Testing------- loss: 0.1500041989080608  accuracy: 0.9558

3.CNN分类

from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense,Activation,Convolution2D,MaxPooling2D,Flatten

(X_train,Y_train),(X_test,Y_test)=mnist.load_data()
X_train,X_test=X_train.reshape(-1,1,28,28)/255,X_test.reshape(-1,1,28,28)/255
Y_train,Y_test=np_utils.to_categorical(Y_train,nb_classes=10),np_utils.to_categorical(Y_test,nb_classes=10)

model=Sequential()
model.add(Convolution2D(nb_filter=32,nb_row=5,nb_col=5,border_mode='same',input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode='same'))

model.add(Convolution2D(64,5,5,border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D((2,2),(2,2),'same'))

model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))

model.compile(optimizer='adadelta',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

################################Training..............############################################
def get_batch(j,batchsize,N,X,Y):
    start_index,end_index=j*batchsize,j*batchsize+batchsize
    if end_index>N:
        end_index=N
    return X[start_index:end_index,:],Y[start_index:end_index,:]

def my_model_fit(X_train,Y_train,epoch=10,batchsize=32):
    N_train = X_train.shape[0]
    for i in range(epoch):
        for j in range(N_train//batchsize):
            X_batch,Y_batch=get_batch(j,batchsize,N_train,X_train,Y_train)
            # print('X_batch,Y_batch:',X_batch.shape,Y_batch.shape)
            cost,accuracy= model.train_on_batch(X_batch,Y_batch)
            if j%100==0:
                print('Training-------epoch:',i,' batch:', j, 'loss:',cost,' accuracy:',accuracy)
                # print('Trainging-------epoch:', i, ' loss:', cost)
####################################################################################################

# my_model_fit(X_train,Y_train)
model.fit(X_train,Y_train)
cost,accuracy=model.evaluate(X_test,Y_test)
print('
Testing--------------  cost:',cost,'accuracy:',accuracy)
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4.RNN和LSTM分类

from keras.models import Sequential
from keras.datasets import mnist
from keras.utils import np_utils
from keras.layers import Activation,SimpleRNN,Dense

(X_train,Y_train),(X_test,Y_test)=mnist.load_data()
print('(X_train,Y_train),(X_test,Y_test)',X_train.shape,Y_train.shape,X_test.shape,Y_test.shape)
X_train,X_test=X_train.reshape(-1,28,28)/255,X_test.reshape(-1,28,28)/255
print('(X_train,Y_train),(X_test,Y_test)',X_train.shape,Y_train.shape,X_test.shape,Y_test.shape)
Y_train,Y_test=np_utils.to_categorical(Y_train,nb_classes=10),np_utils.to_categorical(Y_test,nb_classes=10)

N_hidden=50
N_out=50
bathsize=32
N_time=28
N_data_dimension=28

model=Sequential()
model.add(SimpleRNN(output_dim=50,
                    input_shape=(N_time,N_data_dimension)))
model.add(Dense(10))
model.add(Activation('softmax'))

model.compile(optimizer='adadelta',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model.fit(X_train,Y_train)

loss,accuracy=model.evaluate(X_test,Y_test)
print("
Testing-----------loss:%f  accuracy:%f"%(loss,accuracy))
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RNN结果：                                                                                                                          LSTM结果：

      

5. Autoencoder自编码

from keras.models import Model
from keras.datasets import mnist
from keras.utils import np_utils
from keras.layers import Input,Activation,Dense
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(2018)

(X_train,_),(X_test,Y_test)=mnist.load_data()
X_train,X_test=X_train.reshape(-1,784)/255-0.5,X_test.reshape(-1,784)/255-0.5

N_1,N_2,N_3=128,64,4
inputs=Input(shape=(784,))
encoder=Dense(N_1,activation='relu')(inputs)
encoder=Dense(N_2,activation='relu')(encoder)
encoder=Dense(N_3,activation='relu')(encoder)
encoder=Dense(2,activation='relu')(encoder)

decoder=Dense(N_3,activation='relu')(encoder)
decoder=Dense(N_2,activation='relu')(decoder)
decoder=Dense(N_1,activation='relu')(decoder)
decoder=Dense(784,activation='tanh')(decoder)

Autoencoder=Model(input=inputs,output=decoder)
Encoder=Model(input=inputs,output=encoder)
Autoencoder.compile(optimizer='adadelta',loss='mse')
Autoencoder.fit(X_train,X_train,nb_epoch=20)

img_reduced=Encoder.predict(X_test)
mse=Autoencoder.evaluate(X_test,X_test)
print("
Testing---------MSE::",mse)
plt.scatter(img_reduced[:,0],img_reduced[:,1],c=Y_test)
plt.show()
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6. 模型存储

import numpy as np
from keras.models import Sequential,load_model
from keras.layers import Dense

np.random.seed(2018)

#构造数据
X=np.linspace(-1,1,200)
np.random.shuffle(X)
Y=0.5*X+2+np.random.normal(0,0.05,X.shape)

N=160
X_train,Y_train=X[:N],Y[:N]
X_test,Y_test=X[N:],Y[N:]

#构建模型
model=Sequential()
model.add(Dense(output_dim=1,input_dim=1))

#损失函数和优化器选择
model.compile(loss='mse',optimizer='sgd')

for i in range(301):
    cost=model.train_on_batch(X_train,Y_train)
    if i%100==0:
        print("Train cost:",cost)

cost=model.evaluate(X_test,Y_test,batch_size=40)
print("Test cost:",cost)

w,b=model.layers[0].get_weights()
print("Weights:",w,"Bias:",b)

Y_pred=model.predict(X_test[0:4])
print('Y_pred:',Y_pred)
model.save('my_model.h5')
del model
model=load_model('my_model.h5')
Y_pred=model.predict(X_test[0:4])
print('Y_pred:',Y_pred)
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Key: from keras.models import load_model 和  h5数据格式
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原文地址：https://www.cnblogs.com/skykill/p/8649468.html