def train():
# setting up, options contains all our params
options = Options(
library=0, # use keras
configs=2, # use resnet50 model
transform=1) # use transform for resnet50
# initialize model
model = options.FlowerClassificationModel(options)
# fit model
model.fit()
def train():
# setting up, options contains all our params
options = Options(
library=0, # use keras
configs=2, # use resnet50 model
transform=1) # use transform for resnet50
# set options to your specific experiment
options.experiment = "fine_tuned_oxford102_model_dropout"
options.dropout = 0.1
options.number = options.dropout
# settings
options.gpu = 2
options.save_test_result = True
# early stopping
options.early_stopping = True
options.patience = 20
# general hyperparameters
options.lr = 1E-2
options.batch_size = 128
# reduce lr on plateau
options.reduce_lr = 0.5
for i in range(0, 9):
# initialize model
model = options.FlowerClassificationModel(options)
# fit model
model.fit()
# evaluate model
model.evaluate()
# reset model for next parameter
model.reset()
# change dropout from 0.1 to 0.9
options.dropout += 0.1
# change the log number saved to checkpoints
options.number = options.dropout
def eval():
# setting up, options contains all our params
options = Options(
library=0, # use keras
configs=2, # use resnet50 model
transform=1) # use transform for resnet50
# set options to the specific experiment you are testing on
# options.gpu = 1
options.test_batch_size = 84 # since there are 336 test examples
# load the weight file
options.load = True
options.load_file = './checkpoints/2017-10-02_experiment_0/model_best_weights.h5'
# initialize model
model = options.FlowerClassificationModel(options)
# fit model
model.evaluate()
image = tf.cast(image, dtype=tf.float32)
# setting up, options contains all our params
options = Options(
library=0, # use keras
configs=2, # use resnet50 model
transform=1) # use transform for resnet50
# load the weight file
options.load = True
options.load_file = osp.join(args.model_dir, args.trained_model)
input_tensor = preprocess_input(image)
# initialize model
model = options.FlowerClassificationModel(options,
input_tensor=input_tensor)
my_model = model.model
keys_placeholder = tf.placeholder(tf.string, shape=[None])
inputs = {'key': keys_placeholder, 'image_bytes': image_str_tensor}
# To extract the id, we need to add the identity function.
keys = tf.identity(keys_placeholder)
outputs = {
'key': keys,
'prediction': my_model.output,
}
signature_def = build_signature(inputs=inputs, outputs=outputs)
signature_def_map = {
