實驗資料MNIST

初次訓練模型並儲存

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

# 載入資料
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
 

print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
# 換one hot格式
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

# 建立模型，輸入784個神經元，輸出10個神經元
 

model = Sequential([
        Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')
    ])

# 定義優化器
sgd = SGD(lr=0.2)

# 定義優化器，loss function，訓練過程中計算準確率
model.compile(
    optimizer = sgd,
    loss = 'mse',
    metrics=['accuracy'],
)

# 訓練模型
model.fit(x_train,y_train,batch_size=64,epochs=5
 
)

# 評估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('\ntest loss',loss)
print('accuracy',accuracy)

# 儲存模型
model.save('model.h5')   # HDF5檔案，pip install h5py

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載入初次訓練的模型，再訓練

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from keras.models import load_model
# 載入資料
(x_train,y_train),(x_test,y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:',x_train.shape)
# (60000)
print('y_shape:',y_train.shape)
# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test = x_test.reshape(x_test.shape[0],-1)/255.0
# 換one hot格式
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

# 載入模型
model = load_model('model.h5')

# 評估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('\ntest loss',loss)
print('accuracy',accuracy)

# 訓練模型
model.fit(x_train,y_train,batch_size=64,epochs=2)

# 評估模型
loss,accuracy = model.evaluate(x_test,y_test)

print('\ntest loss',loss)
print('accuracy',accuracy)

# 儲存引數，載入引數
model.save_weights('my_model_weights.h5')
model.load_weights('my_model_weights.h5')
# 儲存網路結構，載入網路結構
from keras.models import model_from_json
json_string = model.to_json()
model = model_from_json(json_string)

print(json_string)

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