mfcc-dnn.py 文件源码

def seq(x_train,y_train,x_val,y_val,x_test,y_test):
        #Defining the structure of the neural network
        #Creating a Network, with 2 Convolutional layers
        model=Sequential()
        # model.add(Conv2D(128,(3,5),activation='relu',input_shape=(1,39,40)))
        # model.add(Conv2D(64,(3,5)))
        # model.add(MaxPooling2D((2,2)))
        # model.add(Flatten())
        model.add(Dense(512,activation='relu',input_shape=(780,)))
        model.add(Dense(512,activation='relu')) #Fully connected layer 1
        # model.add(Dropout(0.5))
        model.add(Dense(2,activation='softmax')) #Output Layer
        model.summary()
        # f=open('/home/siddharthm/scd/scores/'+common_save+'-complete.txt','rb+')
        # print f >> model.summary()
        data_saver(str(model.to_json()))
        # f.close()
        sgd=SGD(lr=0.1)
        early_stopping=EarlyStopping(monitor='val_loss',patience=4)
        reduce_lr=ReduceLROnPlateau(monitor='val_loss',patience=4,factor=0.5,min_lr=0.0000001)
        #Compilation region: Define optimizer, cost function, and the metric?
        model.compile(optimizer=sgd,loss='binary_crossentropy',metrics=['accuracy'])

        #Fitting region:Get to fit the model, with training data
        checkpointer=ModelCheckpoint(filepath=direc+common_save+'.json',monitor='val_acc',save_best_only=True,save_weights_only=True)

        #Doing the training[fitting]
        model.fit(x_train,y_train,epochs=EPOCH,batch_size=batch,validation_data=(x_val,y_val),callbacks=[checkpointer,early_stopping,reduce_lr])
        model.save_weights(direc+common_save+'-weights'+'.json') #Saving the weights from the model
        model.save(direc+common_save+'-model'+'.json')#Saving the model as is in its state

        ### SAVING THE VALIDATION DATA ###
        scores=model.predict(x_val,batch_size=batch)
        sio.savemat(direc+name_val+'.mat',{'scores':scores,'ytest':y_val}) #These are the validation scores.
        classes=model.predict_classes(x_train,batch_size=batch)
        ### ------------- ###

        ### SAVING THE TESTING DATA ###
        #scores_test=model.predict(x_test,batch_size=batch)
        #sio.savemat(direc+name_test+'.mat',{'scores':scores_test,'ytest':y_test})
        ### ------------- ###
        # print model.evaluate(x_test,y_test,batch_size=batch)

        #predictions=model.predict(x_val,batch_size=batch)
        #print "Shape of predictions: ", predictions.shape
        #print "Shape of y_test: ",y_test.shape
        return classes

#Non-function section

#y_test,predictions,classes=seq(x_train,y_train,x_val,y_val,x_test,y_test) #Calling the seq model, with 2 hidden layers