def make_model(X,
class_names,
nb_layers=4,
try_checkpoint=True,
weights_file='weights.hdf5',
quiet=False,
missing_weights_fatal=False):
''' In the following, the reason we hang on to & return serial_model,
is because Keras can't save parallel models, but according to fchollet
the serial & parallel versions will always share the same weights
(Strange but true!)
'''
gpu_count = get_available_gpus()
if gpu_count <= 1:
serial_model = MyCNN_Keras2(X,
nb_classes=len(class_names),
nb_layers=nb_layers)
else:
with tf.device("/cpu:0"):
serial_model = MyCNN_Keras2(X,
nb_classes=len(class_names),
nb_layers=nb_layers)
# Initialize weights using checkpoint if it exists.
if (try_checkpoint):
print("Looking for previous weights...")
if (isfile(weights_file)):
print('Weights file detected. Loading from ', weights_file)
loaded_model = load_model(
weights_file
) # strip any previous parallel part, to be added back in later
serial_model.set_weights(loaded_model.get_weights()
) # assign weights based on checkpoint
else:
if (missing_weights_fatal):
print("Need weights file to continue. Aborting")
assert (not missing_weights_fatal)
else:
print('No weights file detected, so starting from scratch.')
if (gpu_count >= 2):
print(" Parallel run on", gpu_count, "GPUs")
model = make_parallel(serial_model, gpu_count=gpu_count)
else:
model = serial_model
opt = Adam(lr=0.00001) #
loss = 'categorical_crossentropy'
metrics = ['accuracy']
model.compile(loss=loss, optimizer=opt, metrics=metrics)
serial_model.compile(loss=loss, optimizer=opt, metrics=metrics)
if (not quiet):
print("Summary of serial model (duplicated across", gpu_count,
"GPUs):")
serial_model.summary() # print out the model layers
return model, serial_model
def setup_model(X, class_names, nb_layers=4, try_checkpoint=True,
weights_file='weights.hdf5', quiet=False, missing_weights_fatal=False, multi_tag=False):
''' In the following, the reason we hang on to & return serial_model,
is because Keras can't save parallel models, but according to fchollet
the serial & parallel versions will always share the same weights
(Strange but true!)
'''
# Here's where one might 'swap out' different neural network 'model' choices
serial_model = MyCNN_Keras2(X.shape, nb_classes=len(class_names), nb_layers=nb_layers)
#serial_model = old_model(X.shape, nb_classes=len(class_names), nb_layers=nb_layers)
#serial_model = imageModels(X, nb_classes=len(class_names))
# don't bother with freezing layers, at least with the hope of trianing on a laptop. doesn't speed up by more than a factor of 2.
# serial_model = freeze_layers(serial_model, train_last = 3)
# Initialize weights using checkpoint if it exists.
if (try_checkpoint):
print("Looking for previous weights...")
if ( isfile(weights_file) ):
print ('Weights file detected. Loading from ',weights_file)
loaded_model = load_model(weights_file) # strip any previous parallel part, to be added back in later
serial_model.set_weights( loaded_model.get_weights() ) # assign weights based on checkpoint
else:
if (missing_weights_fatal):
print("Need weights file to continue. Aborting")
assert(not missing_weights_fatal)
else:
print('No weights file detected, so starting from scratch.')
opt = 'adadelta' # Adam(lr = 0.00001) # So far, adadelta seems to work the best of things I've tried
#opt = 'adam'
metrics = ['accuracy']
if (multi_tag): # multi_tag means more than one class can be 'chosen' at a time; default is 'only one'
loss = 'binary_crossentropy'
else:
loss = 'categorical_crossentropy'
serial_model.compile(loss=loss, optimizer=opt, metrics=metrics)
# Multi-GPU "parallel" capability
gpu_count = get_available_gpus()
if (gpu_count >= 2):
print(" Parallel run on",gpu_count,"GPUs")
model = make_parallel(serial_model, gpu_count=gpu_count)
model.compile(loss=loss, optimizer=opt, metrics=metrics)
else:
model = serial_model
if (not quiet):
print("Summary of serial model (duplicated across",gpu_count,"GPUs):")
serial_model.summary() # print out the model layers
return model, serial_model # fchollet says to hang on to the serial model for checkpointing