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KerasCNN.py
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KerasCNN.py
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from __future__ import print_function
from time import time
import keras
import numpy
from keras.callbacks import EarlyStopping
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Conv1D, MaxPooling1D
from keras.layers import Conv2D, MaxPooling2D
import os
from preprocessData import load_dataset_from_h5
def map_lable_dict(res):
map_dict = {'sing':1,'nosing':0}
map_res = [map_dict[lable] for lable in res]
return map_res
num_classes = 2
Retrain = True
time_step = 29
model_saved_path = 'model/cnn.model.h5'
model_weights_saved_path = model_saved_path.replace('.model', '.weights')
def get_step_data(dataX,dataY,step=time_step):
finalX =[]
finalY=[]
lenTX=len(dataX[0])
for i_y in range(0,len(dataY),step):
if list(dataY[i_y:i_y+step]).count(1)==step:
finalY.append(1)
finalX.append(dataX[i_y:i_y+step])
if list(dataY[i_y:i_y+step]).count(0)==step:
finalY.append(0)
finalX.append(dataX[i_y:i_y+step])
finalX=numpy.array(finalX)
finalY=numpy.array(finalY)
finalX=numpy.reshape(finalX,(-1,step,lenTX))
return finalX,finalY
def build_load_model(trainX,trainY,testX,testY, validX, validY ):
if not os.path.isfile('model/cnn.model.h5') or Retrain:
model = Sequential()
feat_dim = numpy.shape(trainX)[-1]
VERBOSE =1
# print(trainX)ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=2
# print('type Of trainX',type(trainX))
# print(trainX.shape[1:])
# print('type Of trainX', type(trainX.shape[1:]))
# print('type Of trainX', type(trainX.shape[1]))
# model.add(Conv1D((4,4),input_shape=(time_step,feat_dim),activation='relu')))#,padding='same',input_shape=trainX.shape[1:]
model.add(Conv1D(4, 4, input_shape=(time_step, feat_dim), activation='relu'))
# model.add(Activation('relu'))
# model.add(Conv2D(32,(3,3)))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2,2)))
# model.add(Dropout(0.25))
# model.add(Conv2D(64,(3,3),padding='same'))
# model.add(Activation('relu'))
# model.add(Conv2D(64,(3,3)))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2,2)))
# model.add(Dropout(0.25))
# model.add(Flatten())
# model.add(Dense(512))
# model.add(Activation('relu'))
# # model.add(Dropout(0.5))
# model.add(Dense(num_classes))
# model.add(Activation('softmax'))
#
#
# opt = keras.optimizers.rmsprop(lr=0.0001,decay=1e-6)
#
#
# model.compile(loss='binary_crossentropy',
# optimizer='sgd',
# metrics=['accuracy'])
# ,'precision','recall','fmesaure'
model.add(MaxPooling1D(2))
model.add(Conv1D(4, 4, activation='relu'))
model.add(MaxPooling1D(2))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
callbacks = [EarlyStopping(monitor='val_loss', patience=2, verbose=0)]
# trainX = trainX.astype('float32')
# testX = testX.astype('float32')
# trainX /= 255
# testX /= 255
model.fit(trainX,trainY,batch_size=128,nb_epoch=10000, callbacks=callbacks, validation_data=(validX, validY),
verbose=VERBOSE)
model.save('model/cnn.model.h5')
model.save_weights('model/cnn.weights.h5')
elif Retrain != True and os.path.isfile(model_saved_path) and os.path.isfile(model_weights_saved_path):
model= load_model('model/cnn.model.h5')
model.load_weights('model/cnn.weights.h5')
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
else:
model = 'null'
print('error')
loss_and_metrics = model.evaluate(testX, testY, batch_size=128, verbose=0)
predictY = model.predict_classes(testX)
print(loss_and_metrics)
return 0
if __name__ == '__main__':
start_time = time()
trainX, trainY, testX, testY, validX, validY = load_dataset_from_h5('data/datasetA.h5')
trainY = map_lable_dict(trainY)
testY = map_lable_dict(testY)
validY = map_lable_dict(validY)
trainX, trainY=get_step_data(trainX, trainY)
testX, testY = get_step_data(testX, testY)
validX, validY = get_step_data(validX, validY)
# trainY = keras.utils.to_categorical(trainY, num_classes)
# testY = keras.utils.to_categorical(testY, num_classes)
# validY = keras.utils.to_categorical(validY, num_classes)
build_load_model(trainX, trainY, testX, testY, validX, validY)
end_time=time()
print('it takes %.1f s' %(end_time-start_time))
# scores = model.evaluate(testX, testY, verbose=1)
# print('test loss:', scores[0])
# print('test accuraqcy:', scores[1])