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