Ndtyjky

I have a strange issue, the DNN.predict method is quite slower when I load my model's weight than when I train with the fit method. I've also noted that when I run a prediction over a batch of images, it's getting faster and faster to predict.

Here is my code

class Reseau(object):



def init(self, img_size, lr=-1, activation=" "):

tf.logging.set_verbosity(tf.logging.ERROR)

self.lr = lr

self.activation = activation

self.img_size = img_size

self.alreadySaved = 0



def setting(self, X, Y, test_x, test_y, nbEpoch):

tflearn.init_graph(num_cores=32, gpu_memory_fraction=1)

with tf.device("/device:GPU:0"):

convnet = input_data(shape=[None, self.img_size, self.img_size, 3], name='input')



        convnet = conv_2d(convnet, 32, 5, activation=self.activation)

        convnet = max_pool_2d(convnet, 5)



        convnet = conv_2d(convnet, 64, 5, activation=self.activation)

        convnet = max_pool_2d(convnet, 5)



        convnet = conv_2d(convnet, 128, 5, activation=self.activation)

        convnet = max_pool_2d(convnet, 5)



        convnet = conv_2d(convnet, 64, 5, activation=self.activation)

        convnet = max_pool_2d(convnet, 5)



        convnet = conv_2d(convnet, 32, 5, activation=self.activation)

        convnet = max_pool_2d(convnet, 5)



        convnet = flatten(convnet)



        convnet = fully_connected(convnet, 1024, activation=self.activation, name='last')

        convnet = fully_connected(convnet, 1024, activation=self.activation, name='last')

        convnet = fully_connected(convnet, 1024, activation=self.activation, name='last')

        convnet = fully_connected(convnet, 1024, activation=self.activation, name='last')

        convnet = dropout(convnet, 0.8)



        convnet = fully_connected(convnet, 2, activation='softmax')

        convnet = regression(convnet, optimizer='adam', learning_rate=self.lr, loss='categorical_crossentropy', name='targets')



        self.model = tflearn.DNN(convnet, tensorboard_dir='log')



        if self.alreadySaved == 0:

            self.model.fit({'input': X}, {'targets': Y}, n_epoch=nbEpoch, validation_set=({'input': test_x}, {'targets': test_y}), snapshot_step=500, show_metric=True, run_id="model")

            self.model.save("./model")

        else:

            self.model.load("./model", weights_only=True)

    return self.model



def predire(self, img, label):

image = array(img).reshape(1, self.img_size,self.img_size,3)

model_out = self.model.predict(image)

rep = 0



    if np.argmax(model_out) == np.argmax(label): rep = 1

    else: rep = 0



    return rep

Here is a part of my main

reseau.setting(X, Y, test_x, test_y, NB_EPOCH)



X = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,3)

Y = [i[1] for i in test]

cpt = 0



vrai = 0

start_time = time.time()



for i in range(20):

    cpt = 0



    vrai = 0

    start_time = time.time()

    for img in tqdm(X):

        prediction = reseau.predire(img, Y[cpt])

        cpt += 1

        if prediction == 1:

            vrai += 1

As you can see, I predict the same batch of images 20 times. The first time is always slower than the other ones (without fitting, I predict 82 images the first and then 340 a second, with fitting, it's 255 images the first time and 340 a seconde then).

I'm really out of idea to fix this.