class Xceptionmodel(CNNmodel):
def __init__(self, data_directory, batch_size=16):
self.cnn = CNNmodel(data_directory)
self.height, self.width, self.depth = (48, 48, 1)
self.batch_size = batch_size
self.xception = Xception(include_top=True,
weights=None,
input_shape=(self.height, self.width,
self.depth),
classes=4)
def summary(self):
print(self.xception.summary())
def train(self,
optimizer='adam',
loss='categorical_crossentropy',
epoch=20):
self.xception.compile(loss="categorical_crossentropy",
optimizer=optimizer,
metrics=["accuracy"])
print("[INFO] training network...")
es = EarlyStopping(monitor='val_acc',
mode='max',
verbose=1,
patience=4)
self.history = self.xception.fit(self.cnn.train_data,
epochs=epoch,
validation_data=self.cnn.val_data,
verbose=1,
callbacks=[es])
print("[INFO] Saving model...")
pickle.dump(self.xception, open('xception.pkl', 'wb'))
print("[INFO] Done...")
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)
import datetime
import numpy as np
num_samples = 1000
height = 224
width = 224
num_classes = 1000
start = datetime.datetime.now()
with tf.device('/cpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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')
end = datetime.datetime.now()
time_delta = end - start
print('GPU 처리시간:', time_delta)
start = datetime.datetime.now()
with tf.device('/cpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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)
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'xception_wood_model.hdf5'
weight_name = 'xception_wood_weight.hdf5'
model_path = os.path.join(save_dir, model_name)
weight_path = os.path.join(save_dir, weight_name)
x_train, y_train, x_test, y_test = image_crop.read_data()
x_train_resized = image_crop.resize_imgs(x_train)
y = to_categorical(y_train, num_classes=34)
model = Xception(include_top=True, weights=None, classes=34)
checkpoint = ModelCheckpoint(filepath=os.path.join(
save_dir, 'weight.{epoch:02d}-{loss:.2f}.hdf5'),
verbose=1)
opt = RMSprop(lr=2e-5)
model.compile(optimizer=opt,
loss=losses.categorical_crossentropy,
metrics=[metrics.categorical_accuracy])
model.fit(x_train_resized,
y,
epochs=10,
batch_size=36,
callbacks=[checkpoint])
model.save(model_path)
model.save_weights(weight_path)
