def inference_global_t_cnn_1layer(images, keep_prob, feat=[4]): _print_tensor_size(images, 'inference_global_t_cnn') assert isinstance(keep_prob, object) # global t # here use the channel wise filter which go across channels conv_tensor = rsvp_quick_inference.inference_channel_wise_filter(images, 'conv1', 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_global_t_cnn_1layer(images, keep_prob, feat=[4]): _print_tensor_size(images, 'inference_global_t_cnn') assert isinstance(keep_prob, object) # global t # here use the channel wise filter which go across channels conv_tensor = rsvp_quick_inference.inference_channel_wise_filter( images, 'conv1', out_feat=feat[0]) logits = rsvp_quick_inference.inference_fully_connected_1layer( conv_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_global_t_cnn(images, keep_prob, layer=1, feat=[4]): _print_tensor_size(images, 'inference_global_t_cnn') assert isinstance(keep_prob, object) # global t # here use the channel wise filter which go across channels conv_tensor = rsvp_quick_inference.inference_channel_wise_filter(images, 'conv1', out_feat=feat[0]) # the pooling should have the width padding to 1 because no width anymore 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