def main():
# ====================================================
# Download and build a custom Xception
# ====================================================
# instantiate pre-trained Xception model
base_model = Xception(include_top=False,
weights='imagenet',
input_shape=(299, 299, 3))
# save base model
base_model_json = base_model.to_json()
name = "xcept_base_model"
with open(name + ".json", "w") as json_file:
json_file.write(base_model_json)
base_model.save_weights(name + "_weight.h5")
print("Saved base model to disk")
# create a custom top classifier
num_classes = 2
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=base_model.inputs, outputs=predictions)
# save custom model
model_json = model.to_json()
name = "xcept_custom_model"
with open(name + ".json", "w") as json_file:
json_file.write(model_json)
model.save_weights(name + "_weight.h5")
print("Saved custom model to disk")
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())
from keras.utils import plot_model
plot_model(model,
to_file='model.png',
show_shapes=True,
show_layer_names=True)
import subprocess
subprocess.call("python ./keras-js/encoder.py model.hdf5", shell=True)
exit()
