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jigsaw_train.py
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jigsaw_train.py
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import os
import numpy as np
import tensorflow as tf
from cv2 import imread
from sklearn.model_selection import train_test_split
def toInt(s):
return int(s)
def readDir(path="./imgs", info=True):
features, labels = [], []
files = os.listdir(path)
if info:
print(f"Sao {len(files)} arquivos a serem lidos!")
i = 0
for img_name in files:
features.append(imread(os.path.join(path, img_name)))
img_name = os.path.splitext(img_name)[0]
labels.append(list(map(toInt, img_name.split("-"))))
if info:
if (i%100 == 0):
print(f"Estamos no arquivo numero {i}!")
i+=1
return np.array(features), np.array(labels)
if __name__ == "__main__":
# Carregando o dataset
features, labels = readDir()
x_train, x_test, y_train, y_test = train_test_split(features, labels, test_size=0.33, random_state=23, shuffle=True)
# Neural Net em si
# Modelo 1 --> Soprado da documentação do keras
inputs = tf.keras.layers.Input(shape=(300,300,3))
pre_treinada_saida = inputs
neuralNet = tf.keras.applications.EfficientNetB0(weights="imagenet", input_tensor=pre_treinada_saida, include_top=False)
neuralNet.trainable = True # O modelo base nao treina
pre_treinada_saida = tf.keras.layers.GlobalAveragePooling2D()(neuralNet.output)
pre_treinada_saida = tf.keras.layers.BatchNormalization()(pre_treinada_saida)
pre_treinada_saida = tf.keras.layers.Dropout(0.2)(pre_treinada_saida)
out = tf.keras.layers.Dense(9, activation="softmax")(pre_treinada_saida) # Output
# Construção do modelo
neuralNet = tf.keras.Model(inputs, out)
#neuralNet.summary() # Resumo da rede
neuralNet.compile(loss="categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
neuralNet.fit(x_train, y_train, epochs=40, validation_data=(x_test, y_test), verbose=1)
neuralNet.save("./contact-lens-model-effnetb0")