def inference_local_s_cnn(images, keep_prob, layer=1, feat=[2]): _print_tensor_size(images, 'inference_local_s_cnn') assert isinstance(keep_prob, object) if not layer == len(feat): print('Make sure you have defined the feature map size for each layer.') return # local t # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_filter(images, 'conv0', out_feat=feat[0]) # 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, kheight=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, kheight=1) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_local_s_cnn(images, keep_prob, layer=1, feat=[2]): _print_tensor_size(images, 'inference_local_s_cnn') assert isinstance(keep_prob, object) if not layer == len(feat): print( 'Make sure you have defined the feature map size for each layer.') return # local t # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_filter( images, 'conv0', out_feat=feat[0]) # 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, kheight=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, kheight=1) logits = rsvp_quick_inference.inference_fully_connected_1layer( pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_local_s_cnn_1layer(images, keep_prob, layer=1, feat=[2]): _print_tensor_size(images, 'inference_local_s_cnn') assert isinstance(keep_prob, object) # local t # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_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_local_s_cnn_1layer(images, keep_prob, layer=1, feat=[2]): _print_tensor_size(images, 'inference_local_s_cnn') assert isinstance(keep_prob, object) # local s # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_filter(images, 'conv0', out_feat=feat[0]) # 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, kheight=1) logits = rsvp_quick_inference.inference_fully_connected_1layer(pool_tensor, keep_prob) assert isinstance(logits, object) return logits
def inference_local_s_cnn_1layer(images, keep_prob, layer=1, feat=[2]): _print_tensor_size(images, 'inference_local_s_cnn') assert isinstance(keep_prob, object) # local t # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_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_local_s_cnn_1layer(images, keep_prob, layer=1, feat=[2]): _print_tensor_size(images, 'inference_local_s_cnn') assert isinstance(keep_prob, object) # local s # here use the 1*5 filter which go across channels conv_tensor = rsvp_quick_inference.inference_spatial_filter( images, 'conv0', out_feat=feat[0]) # 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, kheight=1) logits = rsvp_quick_inference.inference_fully_connected_1layer( pool_tensor, keep_prob) assert isinstance(logits, object) return logits