num_epoch=num_epoch,
initializer=mx.init.Xavier(),
force_init=False,
optimizer='adam',
optimizer_params={"learning_rate": learning_rate})
return mod
def _input_fn(csv_filepath, config, batch_size):
# data_filepath = os.path.join(training_dir, data_file)
with open(csv_filepath, 'rb') as f:
reader = csv.reader(f)
rows = [x for x in reader]
frame_files = [x[0] for x in rows]
labels = [np.array(x[1:], dtype=np.float32) for x in rows]
return zip(labels, frame_files)
if __name__ == "__main__":
from config import ConfigManager
config_manager = ConfigManager('inception_pool_config_gpu_sage.json',
folder='configs/')
config = config_manager.get_json()
mx.viz.plot_network(get_graph('reg_out'))
m = train(config['training'], {"root": "csvs/"},
1,
0,
path_root="/Users/campbellweaver/Documents/VisionData/")
predictions = Dense(nb_outputs,
activation='linear',
use_bias=False,
kernel_initializer='zero',
name='m_matrix_regr_out')(x)
model = Model(inputs=base_model.input, outputs=predictions)
return model
def get_model(self):
# print self.model.summary()
return self.model
if __name__ == '__main__':
from config import ConfigManager
cm = ConfigManager('inception_pool_config_sage.json')
cfg = cm.get_json()
rm = ResearchModels(cfg['training'], load_imagenet=False)
model = rm.get_model()
print model.summary()
# # Then remove the top so we get features not predictions.
# # From: https://github.com/fchollet/keras/issues/2371
# self.model.layers.pop()
# self.model.layers.pop() # two pops to get to pool layer
# self.model.outputs = [self.model.layers[-1].output]
# self.model.output_layers = [self.model.layers[-1]]
# self.model.layers[-1].outbound_nodes = []
