使用Keras建立模型并訓練等一系列操作方式
更新時間：2020年07月02日 12:03:24 作者：夏洛的網
這篇文章主要介紹了使用Keras建立模型并訓練等一系列操作方式，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧
由于Keras是一種建立在已有深度學習框架上的二次框架，其使用起來非常方便，其后端實現(xiàn)有兩種方法，theano和tensorflow。由于自己平時用tensorflow，所以選擇后端用tensorflow的Keras，代碼寫起來更加方便。
1、建立模型
Keras分為兩種不同的建模方式，
Sequential models：這種方法用于實現(xiàn)一些簡單的模型。你只需要向一些存在的模型中添加層就行了。
Functional API：Keras的API是非常強大的，你可以利用這些API來構造更加復雜的模型，比如多輸出模型，有向無環(huán)圖等等。
這里采用sequential models方法。
構建序列模型。
def define_model():
model = Sequential()
# setup first conv layer
model.add(Conv2D(32, (3, 3), activation="relu",
input_shape=(120, 120, 3), padding='same')) # [10, 120, 120, 32]
# setup first maxpooling layer
model.add(MaxPooling2D(pool_size=(2, 2))) # [10, 60, 60, 32]
# setup second conv layer
model.add(Conv2D(8, kernel_size=(3, 3), activation="relu",
padding='same')) # [10, 60, 60, 8]
# setup second maxpooling layer
model.add(MaxPooling2D(pool_size=(3, 3))) # [10, 20, 20, 8]
# add bianping layer, 3200 = 20 * 20 * 8
model.add(Flatten()) # [10, 3200]
# add first full connection layer
model.add(Dense(512, activation='sigmoid')) # [10, 512]
# add dropout layer
model.add(Dropout(0.5))
# add second full connection layer
model.add(Dense(4, activation='softmax')) # [10, 4]
return model
可以看到定義模型時輸出的網絡結構。
2、準備數(shù)據(jù)
def load_data(resultpath):
datapath = os.path.join(resultpath, "data10_4.npz")
if os.path.exists(datapath):
data = np.load(datapath)
X, Y = data["X"], data["Y"]
else:
X = np.array(np.arange(432000)).reshape(10, 120, 120, 3)
Y = [0, 0, 1, 1, 2, 2, 3, 3, 2, 0]
X = X.astype('float32')
Y = np_utils.to_categorical(Y, 4)
np.savez(datapath, X=X, Y=Y)
print('Saved dataset to dataset.npz.')
print('X_shape:{}\nY_shape:{}'.format(X.shape, Y.shape))
return X, Y
3、訓練模型
def train_model(resultpath):
model = define_model()
# if want to use SGD, first define sgd, then set optimizer=sgd
sgd = SGD(lr=0.001, decay=1e-6, momentum=0, nesterov=True)
# select loss\optimizer\
model.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
model.summary()
# draw the model structure
plot_model(model, show_shapes=True,
to_file=os.path.join(resultpath, 'model.png'))
# load data
X, Y = load_data(resultpath)
# split train and test data
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=0.2, random_state=2)
# input data to model and train
history = model.fit(X_train, Y_train, batch_size=2, epochs=10,
validation_data=(X_test, Y_test), verbose=1, shuffle=True)
# evaluate the model
loss, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', loss)
print('Test accuracy:', acc)
可以看到訓練時輸出的日志。因為是隨機數(shù)據(jù)，沒有意義，這里訓練的結果不必計較，只是練習而已。
保存下來的模型結構：
4、保存與加載模型并測試
有兩種保存方式
4.1 直接保存模型h5
保存：
def my_save_model(resultpath): model = train_model(resultpath) # the first way to save model model.save(os.path.join(resultpath, 'my_model.h5'))
加載：
def my_load_model(resultpath):
# test data
X = np.array(np.arange(86400)).reshape(2, 120, 120, 3)
Y = [0, 1]
X = X.astype('float32')
Y = np_utils.to_categorical(Y, 4)
# the first way of load model
model2 = load_model(os.path.join(resultpath, 'my_model.h5'))
model2.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
test_loss, test_acc = model2.evaluate(X, Y, verbose=0)
print('Test loss:', test_loss)
print('Test accuracy:', test_acc)
y = model2.predict_classes(X)
print("predicct is: ", y)
4.2 分別保存網絡結構和權重
保存：
def my_save_model(resultpath): model = train_model(resultpath) # the secon way : save trained network structure and weights model_json = model.to_json() open(os.path.join(resultpath, 'my_model_structure.json'), 'w').write(model_json) model.save_weights(os.path.join(resultpath, 'my_model_weights.hd5'))
加載：
def my_load_model(resultpath):
# test data
X = np.array(np.arange(86400)).reshape(2, 120, 120, 3)
Y = [0, 1]
X = X.astype('float32')
Y = np_utils.to_categorical(Y, 4)
# the second way : load model structure and weights
model = model_from_json(open(os.path.join(resultpath, 'my_model_structure.json')).read())
model.load_weights(os.path.join(resultpath, 'my_model_weights.hd5'))
model.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
test_loss, test_acc = model.evaluate(X, Y, verbose=0)
print('Test loss:', test_loss)
print('Test accuracy:', test_acc)
y = model.predict_classes(X)
print("predicct is: ", y)
可以看到，兩次的結果是一樣的。
5、完整代碼
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout
from keras.losses import categorical_crossentropy
from keras.optimizers import Adam
from keras.utils.vis_utils import plot_model
from keras.optimizers import SGD
from keras.models import model_from_json
from keras.models import load_model
from keras.utils import np_utils
import numpy as np
import os
from sklearn.model_selection import train_test_split
def load_data(resultpath):
datapath = os.path.join(resultpath, "data10_4.npz")
if os.path.exists(datapath):
data = np.load(datapath)
X, Y = data["X"], data["Y"]
else:
X = np.array(np.arange(432000)).reshape(10, 120, 120, 3)
Y = [0, 0, 1, 1, 2, 2, 3, 3, 2, 0]
X = X.astype('float32')
Y = np_utils.to_categorical(Y, 4)
np.savez(datapath, X=X, Y=Y)
print('Saved dataset to dataset.npz.')
print('X_shape:{}\nY_shape:{}'.format(X.shape, Y.shape))
return X, Y
def define_model():
model = Sequential()
# setup first conv layer
model.add(Conv2D(32, (3, 3), activation="relu",
input_shape=(120, 120, 3), padding='same')) # [10, 120, 120, 32]
# setup first maxpooling layer
model.add(MaxPooling2D(pool_size=(2, 2))) # [10, 60, 60, 32]
# setup second conv layer
model.add(Conv2D(8, kernel_size=(3, 3), activation="relu",
padding='same')) # [10, 60, 60, 8]
# setup second maxpooling layer
model.add(MaxPooling2D(pool_size=(3, 3))) # [10, 20, 20, 8]
# add bianping layer, 3200 = 20 * 20 * 8
model.add(Flatten()) # [10, 3200]
# add first full connection layer
model.add(Dense(512, activation='sigmoid')) # [10, 512]
# add dropout layer
model.add(Dropout(0.5))
# add second full connection layer
model.add(Dense(4, activation='softmax')) # [10, 4]
return model
def train_model(resultpath):
model = define_model()
# if want to use SGD, first define sgd, then set optimizer=sgd
sgd = SGD(lr=0.001, decay=1e-6, momentum=0, nesterov=True)
# select loss\optimizer\
model.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
model.summary()
# draw the model structure
plot_model(model, show_shapes=True,
to_file=os.path.join(resultpath, 'model.png'))
# load data
X, Y = load_data(resultpath)
# split train and test data
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=0.2, random_state=2)
# input data to model and train
history = model.fit(X_train, Y_train, batch_size=2, epochs=10,
validation_data=(X_test, Y_test), verbose=1, shuffle=True)
# evaluate the model
loss, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', loss)
print('Test accuracy:', acc)
return model
def my_save_model(resultpath):
model = train_model(resultpath)
# the first way to save model
model.save(os.path.join(resultpath, 'my_model.h5'))
# the secon way : save trained network structure and weights
model_json = model.to_json()
open(os.path.join(resultpath, 'my_model_structure.json'), 'w').write(model_json)
model.save_weights(os.path.join(resultpath, 'my_model_weights.hd5'))
def my_load_model(resultpath):
# test data
X = np.array(np.arange(86400)).reshape(2, 120, 120, 3)
Y = [0, 1]
X = X.astype('float32')
Y = np_utils.to_categorical(Y, 4)
# the first way of load model
model2 = load_model(os.path.join(resultpath, 'my_model.h5'))
model2.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
test_loss, test_acc = model2.evaluate(X, Y, verbose=0)
print('Test loss:', test_loss)
print('Test accuracy:', test_acc)
y = model2.predict_classes(X)
print("predicct is: ", y)
# the second way : load model structure and weights
model = model_from_json(open(os.path.join(resultpath, 'my_model_structure.json')).read())
model.load_weights(os.path.join(resultpath, 'my_model_weights.hd5'))
model.compile(loss=categorical_crossentropy,
optimizer=Adam(), metrics=['accuracy'])
test_loss, test_acc = model.evaluate(X, Y, verbose=0)
print('Test loss:', test_loss)
print('Test accuracy:', test_acc)
y = model.predict_classes(X)
print("predicct is: ", y)
def main():
resultpath = "result"
#train_model(resultpath)
#my_save_model(resultpath)
my_load_model(resultpath)
if __name__ == "__main__":
main()
以上這篇使用Keras建立模型并訓練等一系列操作方式就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持腳本之家。
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