epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH,
verbose=1,
callbacks=[checkpoint, lr_scheduler, csv_logger])
###################################### end version basic and balance ###################################################
####################################### run on test set ################################################################
## only run for testing by adding parameter 'test' when running script
elif modus == 'test':
y_test_matrix = to_categorical(y_test, len(labeltonumber))
if worker == 'single':
print(model.metrics_names)
#model.load_weights(save_modeldirectory + '/Xception_genus_pad_version1.1/Xception.109.0.964.hdf5')
model.load_weights(save_modeldirectory + '/{}'.format(weightfile))
accuracy = model.evaluate(x=X_test, y=y_test_matrix)
## get predicted labels for test set
y_prob = model.predict(X_test)
y_pred = y_prob.argmax(axis=-1)
elif worker == 'parallel':
print(parallel_model.metrics_names)
parallel_model.load_weights(save_modeldirectory +
'/{}'.format(weightfile))
accuracy = parallel_model.evaluate(x=X_test, y=y_test_matrix)
## get predicted labels for test set
y_prob = parallel_model.predict(X_test)
y_pred = y_prob.argmax(axis=-1)
print('loss: {}, accuracy: {}'.format(accuracy[0], accuracy[1]))
## get precision, recall, f1-score and support for each class predicted on test set
classreport = classification_report(y_test, y_pred, output_dict=True)
from keras.utils import to_categorical
from keras import losses,metrics
from keras.callbacks import ModelCheckpoint
import Dataset_mobile
import os
if __name__ == '__main__':
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'xceptionMobile_wood_model.h5'
model_path = os.path.join(save_dir, model_name)
x_train, y_train, x_test, y_test, dictionary = Dataset_mobile.read_data()
x_train_resized = Dataset_mobile.resize_imgs(x_train)
x_test_resized = Dataset_mobile.resize_imgs(x_test)
y_train = to_categorical(y_train, num_classes=2)
y_test = to_categorical(y_test, num_classes=2)
model = Xception(include_top=True, weights=None, classes=2)
opt = Adam(lr=5e-6)
model.compile(optimizer=opt, loss=losses.categorical_crossentropy, metrics=[metrics.categorical_accuracy])
model.fit(x_train_resized,y_train,epochs=20,batch_size=6)
model.save(model_path)
score1 = model.evaluate(x_train_resized, y_train, batch_size=6)
score2 = model.evaluate(x_test_resized, y_test, batch_size=6)
print(score1)
print(score2)
