height = 224
width = 224
num_classes = 1000
# Instantiate the base model (or "template" model).
# We recommend doing this with under a CPU device scope,
# so that the model's weights are hosted on CPU memory.
# Otherwise they may end up hosted on a GPU, which would
# complicate weight sharing.
with tf.device('/cpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
# Replicates the model on 8 GPUs.
# This assumes that your machine has 8 available GPUs.
parallel_model = multi_gpu_model(model, gpus=8)
parallel_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))
# This `fit` call will be distributed on 8 GPUs.
# Since the batch size is 256, each GPU will process 32 samples.
parallel_model.fit(x, y, epochs=20, batch_size=256)
# Save model via the template model (which shares the same weights):
model.save('my_model.h5')
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)
target_size=(img_height,
img_width),
batch_size=batch_size,
shuffle=True,
class_mode='categorical')
print(train_generator.class_indices)
# validation_generator = datagen.flow_from_directory(validation_data_dir,
# target_size=(img_width, img_height),
# batch_size=batch_size,
# shuffle=True, class_mode='categorical')
# train
for epoch in range(1, epochs + 1):
print('Epoch: ', epoch)
progbar = generic_utils.Progbar(nb_train_samples)
for i in range(1, steps + 1):
x_batch, y_batch = train_generator.next()
x_batch = np.array(
[cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in x_batch])
loss, train_acc = model.train_on_batch(x_batch, y_batch)
progbar.add(x_batch.shape[0],
values=[('train loss', loss), ('train acc', train_acc)])
# saving model
m_name = 'epoch{}-train_loss{:.3f}-train_acc{:.3f}.h5'.format(
epoch, loss, train_acc)
print('Saving ', m_name)
if not os.path.exists(saving_path):
os.makedirs(saving_path)
model.save(os.path.join(saving_path, m_name))
print("GPU 시간 측정 시작 !!!")
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('./model/time_check1.h5')
end1 = datetime.datetime.now()
time_delta1 = end1 - start1
print("GPU 시간 측정 끝 !!!")
print("CPU 시간 측정 시작 !!!")
start2 = 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')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
width = 71
num_classes = 100
start = datetime.datetime.now()
with tf.device('/gpu:3'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
# Generete 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')
end = datetime.datetime.now()
time_delta = end - start
print("GPU 처리시간 : ", time_delta)
import tensorflow as tf
from keras.applications import Xception
from keras.utils import multi_gpu_model
import numpy as np
import datetime
num_samples = 100
height = 71
width = 71
num_classes = 100
