def SampleText(model_file, op_file, base_output_dir, data_proto, gpu_mem,
main_mem):
datasets = ['train']
layernames = [
'RNA1seq_input_layer', 'RNA2seq_input_layer', 'RNA3seq_input_layer'
]
layernames_to_unclamp = [
'RNA1seq_input_layer', 'RNA2seq_input_layer', 'RNA3seq_input_layer',
'joint_hidden1'
]
method = 'gibbs' # 'gibbs'
steps = 1000
inference.DoInference(model_file,
op_file,
base_output_dir,
layernames,
layernames_to_unclamp,
memory='10G',
method=method,
steps=steps,
datasets=datasets,
gpu_mem=gpu_mem,
main_mem=main_mem,
data_proto=data_proto)
def SampleText(model_file, op_file, base_output_dir, data_proto, gpu_mem, main_mem):
datasets = ['test']
layernames = ['joint_hidden', 'text_layer']
layernames_to_unclamp = ['text_layer']
method = 'mf' # 'gibbs'
steps = 10
inference.DoInference(model_file, op_file, base_output_dir, layernames,
layernames_to_unclamp, memory='1G', method=method,
steps=steps, datasets=datasets, gpu_mem=gpu_mem,
main_mem=main_mem, data_proto=data_proto)
def SampleImage(model_file, op_file, base_output_dir, data_proto, gpu_mem, main_mem):
# datasets = ['train']
# datasets = ['test']
datasets = ['validation']
layernames = ['joint_hidden', 'image_hidden2']
layernames_to_unclamp = ['image_hidden2']
method = 'mf' # 'gibbs'
steps = 10
inference.DoInference(model_file, op_file, base_output_dir, layernames,
layernames_to_unclamp, memory='1G', method=method,
steps=steps, datasets=datasets, gpu_mem=gpu_mem,
main_mem=main_mem, data_proto=data_proto)
