def buildTunerModel(self, img_size):
"""
This function builds a base Xception model for keras tuner
Parameters:
img_size (int): the size of input images (img_size, img_size).
Returns:
model (class): the Xception model used for keras tuner.
"""
base_model = Xception(weights='imagenet',
include_top=False,
input_shape=(img_size, img_size, 3))
if not self.weights == 'imagenet':
base_model.load_weights(self.weights, by_name=True)
return base_model
def train_and_test(model_name, compression_type, training_size, test_size):
# train model
if compression_type == "basic" and test_size == 224:
start = time.process_time()
print("training")
model = train(model_name, train_flow, valid_flow)
end = time.process_time()
else:
start = time.process_time()
model = Xception(
weights=None,
include_top=False,
#input_shape=(224,224,3)
)
model = build_model(model)
print("loading weight")
model.load_weights("results/Xception.ckpt")
end = time.process_time()
training_time = end - start
FLOW_MAP = {
"nearest": test_flow_nearest,
"box": test_flow_box,
"lanczos": test_flow_lanczos,
"hamming": test_flow_hamming,
}
test_flow = FLOW_MAP[compression_type]
# save model
# np.savez("model_%s.npz" % model_name, **model)
#Evaluate Model
# get compression specific test flow
# test_flow =
start = time.process_time()
train_pred = model.predict(valid_flow)
train_test = valid_flow.classes
train_accuracy = metrics.accuracy_score(train_test, np.round(train_pred))
# train_accuracy = history.history["accuracy"]
test_pred = model.predict(test_flow)
test_test = test_flow.classes
test_accuracy = metrics.accuracy_score(test_test, np.round(test_pred))
print("Sample predictions: ", np.round(test_pred))
print("Sample actual labels: ", test_test)
test_matrix = metrics.confusion_matrix(test_test,
np.round(test_pred)).ravel()
end = time.process_time()
test_time = end - start
print("Model: %s\n" % model_name)
print("Image Compression: %s\n" % compression)
print("Train Image Size: %d\n" % training_size)
print("Test Image Size: %d\n" % test_size)
print("Train accuracy: %f\n" % train_accuracy)
print("Test accuracy: %f\n" % test_accuracy)
print("True Negatives: %f\n" % test_matrix[0])
print("False Positives: %f\n" % test_matrix[1])
print("False Negatives: %f\n" % test_matrix[2])
print("True Positives: %f\n" % test_matrix[3])
print("Training Time: %s sec\n" % training_time)
print("Testing Time: %s sec\n" % test_time)
# save results
try:
with open("results/%s_results.txt" % model_name, "a") as f:
f.write("Model: %s\n" % model_name)
f.write("Image Compression: %s\n" % compression)
f.write("Train Image Size: %d\n" % training_size)
f.write("Test Image Size: %d\n" % test_size)
f.write("Train accuracy: %f\n" % train_accuracy)
f.write("Test accuracy: %f\n" % test_accuracy)
f.write("True Negatives: %f\n" % test_matrix[0])
f.write("False Positives: %f\n" % test_matrix[1])
f.write("False Negatives: %f\n" % test_matrix[2])
f.write("True Positives: %f\n" % test_matrix[3])
f.write("Training Time: %s sec\n" % training_time)
f.write("Testing Time: %s sec\n" % test_time)
f.write("\n\n")
f.close()
except Exception as e:
print(e)
return {
"Model": model_name,
"Image Compression": compression,
"Train Image Size": training_size,
"Test Image Size": test_size,
"Training Accuracy": train_accuracy,
"Test Accuracy": test_accuracy,
"True Negative": test_matrix[0],
"False Positive": test_matrix[1],
"False Negative": test_matrix[2],
"True Positive": test_matrix[3],
"Train Time": training_time,
"Test Time": test_time
}
path = 'C:/nmb/nmb_data/h5/5s/Xception/xception_sgd_2.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
metrics=['acc'])
history = model.fit(x_train,
y_train,
epochs=1000,
batch_size=batch_size,
validation_split=0.2,
callbacks=[es, lr, mc])
# 평가, 예측
# model = load_model('C:/nmb/nmb_data/h5/5s/Xception/xception_sgd_2.h5')
model.load_weights('C:/nmb/nmb_data/h5/5s/Xception/xception_sgd_2.h5')
result = model.evaluate(x_test, y_test, batch_size=8)
print("loss : {:.5f}".format(result[0]))
print("acc : {:.5f}".format(result[1]))
############################################ PREDICT ####################################
pred = ['C:/nmb/nmb_data/predict_04_26/F', 'C:/nmb/nmb_data/predict_04_26/M']
count_f = 0
count_m = 0
for pred_pathAudio in pred:
files = librosa.util.find_files(pred_pathAudio, ext=['wav'])
files = np.asarray(files)
for file in files:
