batch_size = 4
es = EarlyStopping(monitor='val_loss',
patience=20,
restore_best_weights=True,
verbose=1)
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/nmb/nmb_data/h5/5s/Xception/xception_sgd_1.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_1.h5')
# model.load_weights('C:/nmb/nmb_data/h5/5s/Xception/xception_sgd_1.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
import tensorflow as tf
from tensorflow.keras.applications import Xception
from tensorflow.keras.utils import multi_gpu_model
import numpy as np
import datetime
num_samples = 100
height = 71
width = 71
num_classes = 100
start1 = datetime.datetime.now()
with tf.device('/gpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))
model.fit(x, y, epochs=3, batch_size=16)
model.save('my_model_h5')
end1 = datetime.datetime.now()
