96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
import tensorflow as tf
|
|
from tensorflow.keras import layers, models
|
|
import numpy as np
|
|
from sklearn.utils import shuffle
|
|
from sklearn.model_selection import train_test_split
|
|
import cv2
|
|
import os
|
|
import time
|
|
import common
|
|
import dataset
|
|
|
|
batch_size=128
|
|
nbr_entrainement=20
|
|
|
|
tab_images=np.array([]).reshape(0, common.size, common.size, 3)
|
|
tab_labels=[]
|
|
|
|
tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size)
|
|
|
|
id=0
|
|
for image in tab_image_panneau:
|
|
lot=dataset.create_lot_img(image, 12000)
|
|
tab_images=np.concatenate((tab_images, lot))
|
|
tab_labels=np.concatenate([tab_labels, np.full(len(lot), id)])
|
|
id+=1
|
|
|
|
tab_panneau=np.array(tab_panneau)
|
|
tab_images=np.array(tab_images, dtype=np.float32)/255
|
|
tab_labels=np.array(tab_labels, dtype=np.float32).reshape([-1, 1])
|
|
|
|
train_images, test_images, train_labels, test_labels=train_test_split(tab_images, tab_labels, test_size=0.10)
|
|
|
|
train_ds=tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(batch_size)
|
|
test_ds=tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)
|
|
|
|
print("train_images", len(train_images))
|
|
print("test_images", len(test_images))
|
|
|
|
@tf.function
|
|
def train_step(images, labels):
|
|
with tf.GradientTape() as tape:
|
|
predictions=model_panneau(images)
|
|
loss=loss_object(labels, predictions)
|
|
gradients=tape.gradient(loss, model_panneau.trainable_variables)
|
|
optimizer.apply_gradients(zip(gradients, model_panneau.trainable_variables))
|
|
train_loss(loss)
|
|
train_accuracy(labels, predictions)
|
|
|
|
def train(train_ds, nbr_entrainement):
|
|
for entrainement in range(nbr_entrainement):
|
|
start=time.time()
|
|
for images, labels in train_ds:
|
|
train_step(images, labels)
|
|
message='Entrainement {:04d}: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}'
|
|
print(message.format(entrainement+1,
|
|
train_loss.result(),
|
|
train_accuracy.result()*100,
|
|
time.time()-start))
|
|
train_loss.reset_states()
|
|
train_accuracy.reset_states()
|
|
test(test_ds)
|
|
|
|
def test(test_ds):
|
|
start=time.time()
|
|
for test_images, test_labels in test_ds:
|
|
predictions=model_panneau(test_images)
|
|
t_loss=loss_object(test_labels, predictions)
|
|
test_loss(t_loss)
|
|
test_accuracy(test_labels, predictions)
|
|
message=' >>> Test: loss: {:6.4f}, accuracy: {:7.4f}%, temps: {:7.4f}'
|
|
print(message.format(test_loss.result(),
|
|
test_accuracy.result()*100,
|
|
time.time()-start))
|
|
test_loss.reset_states()
|
|
test_accuracy.reset_states()
|
|
|
|
optimizer=tf.keras.optimizers.Adam()
|
|
loss_object=tf.keras.losses.SparseCategoricalCrossentropy()
|
|
train_loss=tf.keras.metrics.Mean()
|
|
train_accuracy=tf.keras.metrics.SparseCategoricalAccuracy()
|
|
test_loss=tf.keras.metrics.Mean()
|
|
test_accuracy=tf.keras.metrics.SparseCategoricalAccuracy()
|
|
model_panneau=common.panneau_model(len(tab_panneau))
|
|
checkpoint=tf.train.Checkpoint(model_panneau=model_panneau)
|
|
|
|
print("Entrainement")
|
|
train(train_ds, nbr_entrainement)
|
|
checkpoint.save(file_prefix="./training_panneau/panneau")
|
|
|
|
for i in range(len(test_images)):
|
|
prediction=model_panneau(np.array([test_images[i]]))
|
|
print("prediction", prediction, tab_panneau[np.argmax(prediction[0])])
|
|
cv2.imshow("image", test_images[i])
|
|
if cv2.waitKey()&0xFF==ord('q'):
|
|
break
|