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generate_keras_model.py
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from keras.metrics import top_k_categorical_accuracy
from keras.utils.vis_utils import plot_model
from keras.utils import to_categorical
from keras.callbacks import Callback
import os
from sklearn.metrics import confusion_matrix,f1_score,roc_curve,auc
import matplotlib.pyplot as plt
from deal_data.cut_label import cut_letter
from deal_data.load_data import load_data
import sys
import keras as K
from keras import backend as Kb
import tensorflow as tf
from pandas import read_csv
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.ensemble import RandomForestClassifier
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution1D, MaxPooling1D,BatchNormalization
import numpy as np
from keras.layers import *
from keras.models import Model,load_model
from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,History,TensorBoard,CSVLogger
from deal_data.generate_load_data_SignalAddLabel import get_three_generater
def BLOCK(seq, filters): # 定义网络的Block
cnn = Conv1D(filters*2, 3, padding='SAME', dilation_rate=1, activation='relu')(seq)
cnn = BatchNormalization(axis=1)(cnn)
cnn = Lambda(lambda x: x[:,:,:filters] + x[:,:,filters:])(cnn)
cnn = Conv1D(filters*2, 3, padding='SAME', dilation_rate=2, activation='relu')(cnn)
cnn = BatchNormalization(axis=1)(cnn)
cnn = Lambda(lambda x: x[:,:,:filters] + x[:,:,filters:])(cnn)
cnn = Conv1D(filters*2, 3, padding='SAME', dilation_rate=4, activation='relu')(cnn)
cnn = BatchNormalization(axis=1)(cnn)
cnn = Lambda(lambda x: x[:,:,:filters] + x[:,:,filters:])(cnn)
if int(seq.shape[-1]) != filters:
seq = Conv1D(filters, 1, padding='SAME')(seq)
seq = add([seq, cnn])
return seq
def resnet_model(trainFilePath,testFilePath,batch_size,epochs,name,lr,key_file,which_line,which_letter,load_weight=False,weight_path = None,evalONtest = True,validation_proportion=0.1,test_num = 100,rm_already_folder = True):
input_tensor = Input(shape=(35000, 1))
seq = input_tensor
seq = BLOCK(seq, 64)
seq = BatchNormalization(axis=1)(seq)
seq = MaxPooling1D(2)(seq)
seq = BLOCK(seq, 64)
seq = BatchNormalization(axis=1)(seq)
seq = MaxPooling1D(2)(seq)
seq = BLOCK(seq, 128)
seq = BatchNormalization(axis=1)(seq)
seq = MaxPooling1D(2)(seq)
seq = BLOCK(seq, 128)
seq = BatchNormalization(axis=1)(seq)
seq = MaxPooling1D(2)(seq)
seq = BLOCK(seq, 256)
seq = BatchNormalization(axis=1)(seq)
seq = MaxPooling1D(2)(seq)
seq = BLOCK(seq, 256)
seq = BatchNormalization(axis=1)(seq)
seq = MaxPooling1D(2)(seq)
seq = BLOCK(seq, 512)
seq = BatchNormalization(axis=1)(seq)
seq = MaxPooling1D(2)(seq)
seq = BLOCK(seq, 512)
seq = BatchNormalization(axis=1)(seq)
seq = Dropout(0.1)(seq)
seq = GlobalMaxPooling1D()(seq)
output_tensor = Dense(16, activation='softmax')(seq)
model = Model(inputs=[input_tensor], outputs=[output_tensor])
model.summary()
if load_weight==True:
model.load_weights(weight_path,by_name=True)
else:
pass
from keras.optimizers import Adam
model.compile(loss='categorical_crossentropy', # 交叉熵作为loss
optimizer=Adam(lr),
metrics=['accuracy'])
if evalONtest == True:
test_model = Model(inputs=[input_tensor], outputs=[output_tensor])
test_model.compile(loss='categorical_crossentropy', # 交叉熵作为loss
optimizer=Adam(lr),
metrics=['accuracy'])
CSV_FILE_PATH2 = testFilePath
data_to_test = load_data(CSV_FILE_PATH2)
# train_x2, test_x2, train_y2, test_y2, Class_dict2
# train_x2 = np.expand_dims(train_x2, axis=2)
# test_x2 = np.expand_dims(test_x2, axis=2)
else:
pass
print('开始加载数据')
b_size = batch_size
max_epochs = epochs
train_generater,val_generater,test_generater,train_data_num,val_data_num = \
get_three_generater(trainFilePath,key_file,which_line,which_letter,
validation_proportion=validation_proportion,
test_num=test_num,batch_size=b_size,rm_already_folder = rm_already_folder)
print("Starting training ")
learnratedecay = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=8, verbose=1, mode='auto',
epsilon=0.0001, cooldown=0, min_lr=0)
os.makedirs('/data/wuchenxi/allmodel/new_simeck_model/'+name+'/model',exist_ok=True)
os.makedirs('/data/wuchenxi/allmodel/new_simeck_model/'+name+'/csvlog',exist_ok=True)
os.makedirs('/data/wuchenxi/allmodel/new_simeck_model/'+name+'/tensorboard',exist_ok=True)
checkpointer = ModelCheckpoint(monitor='val_loss',
filepath='/data/wuchenxi/allmodel/new_simeck_model/'+name+'/model/' + name + '.hdf5',
verbose=1, save_best_only=True)
picture_output = TensorBoard(
log_dir='/data/wuchenxi/allmodel/new_simeck_model/'+name+'/tensorboard/' + name + '_log',
histogram_freq=0,
write_graph=True,
write_grads=True,
write_images=True, )
csvlog = CSVLogger(filename='/data/wuchenxi/allmodel/new_simeck_model/'+name+'/csvlog/' + name + '.csv',
separator=',', append=False)
if evalONtest == True:
pass
# callback = [checkpointer, picture_output, csvlog, learnratedecay,
# EvaluateInputTensor(test_model, train_x2, train_y2,
# '/data/wuchenxi/allmodel/simeck_key_model/'+name+'/csvlog/' + name + '_test.csv')]
else:
callback = [checkpointer, picture_output, csvlog, learnratedecay]
step_per_epoch_default = int(train_data_num/batch_size)
step_per_epoch_default_val = int(val_data_num/batch_size)
h = model.fit_generator(train_generater,
steps_per_epoch=step_per_epoch_default, epochs=max_epochs,
verbose=1,callbacks=callback,
validation_data=val_generater,validation_steps=step_per_epoch_default_val)
resnet_model('/data/wuchenxi/new_simeck_data/signal108800_circle/signal108800',
key_file='/data/wuchenxi/new_simeck_data/signal108800_circle/new_key_108800.txt',
which_line=0,which_letter=2,
testFilePath=None,
batch_size=16,epochs=20,name='108800_0_2_v1_circle_generatermode',#'6000_7.16_multlabel_changehwplace'
lr=1e-4,evalONtest=False,
load_weight=True,
weight_path='/data/wuchenxi/allmodel/new_simeck_model/54080_circle_v1_0_0/model/54080_circle_v1_0_0.hdf5',
rm_already_folder=False)