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
