这篇文章主要介绍了使用Keras建立模型并训练等一系列操作方式，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
由于Keras是一种建立在已有深度学习框架上的二次框架，其使用起来非常方便，其后端实现有两种方法，theano和tensorflow。由于自己平时用tensorflow，所以选择后端用tensorflow的Keras，代码写起来更加方便。
1、建立模型
Keras分为两种不同的建模方式，
Sequential models：这种方法用于实现一些简单的模型。你只需要向一些存在的模型中添加层就行了。
Functional API：Keras的API是非常强大的，你可以利用这些API来构造更加复杂的模型，比如多输出模型，有向无环图等等。
这里采用sequential models方法。
构建序列模型。
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
可以看到定义模型时输出的网络结构。
2、准备数据
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、训练模型
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-600')) # 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)可以看到训练时输出的日志。因为是随机数据，没有意义，这里训练的结果不必计较，只是练习而已。
保存下来的模型结构：
4、保存与加载模型并测试
有两种保存方式
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'))
加载：
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 分别保存网络结构和权重
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'))
加载：
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，两次的结果是一样的。
5、完整代码
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-600')) # 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()以上就是使用Keras建立模型并训练等一系列操作方式的详细内容，更多请关注0133技术站其它相关文章！
