weights='imagenet',
input_tensor=None,
input_shape=None)
elif args.model == "squeezenet":
import sys
sys.path.append("./keras-squeezenet")
from keras_squeezenet import SqueezeNet
model = SqueezeNet(input_tensor=None,
input_shape=None,
weights='imagenet',
classes=1000)
else:
exit()
if args.compress == True:
model.save("model_original.hdf5")
import sys
sys.path.append("./keras_compressor/keras_compressor")
import subprocess
subprocess.call(
"python ./keras_compressor/bin/keras-compressor.py model_original.hdf5 model_compressed.hdf5 --log-level DEBUG",
shell=True)
from keras.models import load_model
model = load_model("model_compressed.hdf5")
model.summary()
model.save_weights("model.hdf5")
model.summary()
model.save_weights("model.hdf5")
with open('model.json', 'w') as f:
f.write(model.to_json())
# In[ ]:
network_history = model.fit(x_train,
y_train,
batch_size=32,
epochs=100,
verbose=1,
validation_data=(x_val, y_val))
# ### Save the Model Trained
# In[ ]:
#model.save('/content/drive/My Drive/ColabNotebooks/AllmodeloRMSpropXception.h5')
model.save('/content/drive/My Drive/ColabNotebooks/Xception/modelXception.h5')
# ### Load the Model Trained
# In[ ]:
from keras.models import load_model
#model = load_model('/content/drive/My Drive/ColabNotebooks/AllmodeloRMSpropXception.h5')
model = load_model(
'/content/drive/My Drive/ColabNotebooks/Xception/modelXception.h5')
# ## Predict the Model Trained
# **Show the three better images and the three wrong image of the test set**
def top_5(y_true, y_pred):
return top_k_categorical_accuracy(y_true, y_pred, k=5)
if __name__ == '__main__':
batch_size = 16
train_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
'data',
target_size=(224, 224),
batch_size=batch_size,
class_mode='categorical',
shuffle=True)
model = Xception(include_top=True, weights=None, classes=10)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy', top_5])
model.fit_generator(train_generator,
steps_per_epoch=125 // batch_size,
epochs=10)
model.save('xception10.h5')
num_classes = 100
start = 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')
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')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))
