def inference_tscnn_1layer(images, keep_prob, feat=[2]): _print_tensor_size(images, 'inference_tscnn') assert isinstance(keep_prob, object) conv_tensor = rsvp_quick_inference.inference_global_ts_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kwidth=1) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_tscnn_1layer(images, keep_prob, feat=[2]): _print_tensor_size(images, 'inference_tscnn') assert isinstance(keep_prob, object) conv_tensor = rsvp_quick_inference.inference_global_ts_filter( images, 'conv0', out_feat=feat[0]) logits = rsvp_quick_inference.inference_fully_connected_1layer( conv_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_tscnn(images, keep_prob, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_tscnn') assert isinstance(keep_prob, object) # global temporal local temporal conv_tensor = rsvp_quick_inference.inference_global_ts_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kwidth=1) for l in range(1, layer): # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_filter\ (pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l]) # the pooling should have the width padding to 1 because we only consider channel correlation pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kwidth=1) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_tscnn(images, keep_prob, layer=2, feat=[2, 4]): _print_tensor_size(images, 'inference_tscnn') assert isinstance(keep_prob, object) # global temporal local temporal conv_tensor = rsvp_quick_inference.inference_global_ts_filter(images, 'conv0', out_feat=feat[0]) pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kwidth=1) for l in range(1, layer): # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_filter\ (pool_tensor, 'conv'+str(l), in_feat=feat[l-1], out_feat=feat[l]) # the pooling should have the width padding to 1 because we only consider channel correlation pool_tensor = rsvp_quick_inference.inference_pooling_n_filter(conv_tensor, kwidth=1) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits