如何使用Keras建立模型并训练等一系列操作

发布时间：2020-07-02 14:26:21 作者：清晨
来源：亿速云阅读：184

这篇文章将为大家详细讲解有关如何使用Keras建立模型并训练等一系列操作，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。

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。

如何使用Keras建立模型并训练等一系列操作

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

如何使用Keras建立模型并训练等一系列操作

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'))

  # 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。

如何使用Keras建立模型并训练等一系列操作

保存下来的模型结构：

如何使用Keras建立模型并训练等一系列操作

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h6

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.h6'))

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.h6'))
  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)

如何使用Keras建立模型并训练等一系列操作

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)

如何使用Keras建立模型并训练等一系列操作

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'))

  # 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.h6'))

  # 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.h6'))
  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()

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如何使用Keras建立模型并训练等一系列操作

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