def plot_roc():
y_preds = []
y_=[]
name_list = ['vgg+fc','densenet121+fc','iv3+fc','resnet50+fc']
vggroot = "/Users/lizhenhao/Desktop/helloworld/im/vgg"
vgg=fc.get_model(25088)
vgg.load_weights("/Users/lizhenhao/PycharmProjects/laserFilm/weights/freeze/vgg/epoch-19.h5")
test_f, test_y = getdata_fromtxt(vggroot + '/test.txt')
test_y_pred = vgg.predict(test_f)
test_y_pred = test_y_pred.reshape(-1)
y_preds.append(test_y_pred)
y_.append(test_y)
denseroot = "/Users/lizhenhao/Desktop/helloworld/im/densenet"
den = fc.get_model(50176)
den.load_weights("/Users/lizhenhao/PycharmProjects/laserFilm/weights/freeze/densenet/epoch-19.h5")
test_f, test_y = getdata_fromtxt(denseroot + '/test.txt')
test_y_pred = den.predict(test_f)
test_y_pred = test_y_pred.reshape(-1)
y_preds.append(test_y_pred)
y_.append(test_y)
iv3root = "/Users/lizhenhao/Desktop/helloworld/im/iv3"
iv3 = fc.get_model(51200)
iv3.load_weights("/Users/lizhenhao/PycharmProjects/laserFilm/weights/freeze/iv3/epoch-19.h5")
test_f, test_y = getdata_fromtxt(iv3root + '/test.txt')
test_y_pred = iv3.predict(test_f)
test_y_pred = test_y_pred.reshape(-1)
y_preds.append(test_y_pred)
y_.append(test_y)
resroot = "/Users/lizhenhao/Desktop/helloworld/im/resnet50"
res = fc.get_model(2048)
res.load_weights("/Users/lizhenhao/PycharmProjects/laserFilm/weights/freeze/resnet50/epoch-19.h5")
test_f, test_y = getdata_fromtxt(resroot + '/test.txt')
test_y_pred = res.predict(test_f)
test_y_pred = test_y_pred.reshape(-1)
y_preds.append(test_y_pred)
y_.append(test_y)
draw.draw_roc(y_, y_preds, name_list, 'roc.png')
def densenettrain():
print("densenet begin")
model = fc.get_model(50176)
root = '/Users/lizhenhao/Desktop/helloworld/im/densenet'
weights_savedir = "/Users/lizhenhao/PycharmProjects/laserFilm/weights/freeze/densenet"
acc_savepath = "/Users/lizhenhao/PycharmProjects/laserFilm/weights/freeze/densenet/acc.png"
loss_savepath = "/Users/lizhenhao/PycharmProjects/laserFilm/weights/freeze/densenet/loss.png"
train_f, train_y = getdata_fromtxt(root + '/train.txt')
vali_f, vali_y = getdata_fromtxt(root + '/vali.txt')
test_f, test_y = getdata_fromtxt(root + '/test.txt')
mycall = mycallback(test_f, test_y, weights_savedir)
model.compile(loss=tf.keras.losses.binary_crossentropy,
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
metrics=[tf.keras.metrics.binary_accuracy])
model.fit(train_f, train_y, epochs=20, shuffle=True, validation_data=(vali_f, vali_y), callbacks=[mycall])
draw.draw_acc(mycall.train_acc, mycall.vali_acc, mycall.test_acc, mycall.epoch, acc_savepath)
draw.draw_loss(mycall.train_loss, mycall.vali_loss, mycall.test_loss, mycall.epoch, loss_savepath)
print('densenet done')
return model