def inference_pooling_L2norm_choose_filter(images, kheight=2, kwidth=5):
# channel domain pooling mapper
split_dim = 1 # 1 represents split on spatial domain
input_image_list = split_eeg.split_eeg_signal_axes(images,
split_dim=split_dim)
input_image_length = len(input_image_list)
# the pooling mapper should choose half size of the image size
pool_s, _ = concat_eeg.pool_eeg_signal_channel(input_image_list, input_image_length/2, 1)
_print_tensor_size(pool_s)
input_shape = pool_s.get_shape()
range_even = tf.range(0, input_shape[0], 2)
range_odd = tf.range(1, input_shape[0], 2)
even_rows = tf.nn.embedding_lookup(images, range_even)
odd_rows = tf.nn.embedding_lookup(images, range_odd)
even_rows = tf.mul(pool_s,pool_s)
even_rows = tf.mul(3.0, pool_s)
even_rows = tf.nn.avg_pool(even_rows, ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1], padding='VALID')
pool_s = tf.sqrt(pool_s)
pool_s = tf.nn.max_pool(pool_s, ksize=[1, 2, 1, 1],
strides=[1, 2, 1, 1], padding='VALID')
_print_tensor_size(pool_s)
return pool_s
def inference_pooling_L2norm_filter(images, kwidth=5):
kheight = 2
# channel domain pooling mapper
split_dim = 1 # 1 represents split on spatial domain
input_image_list = split_eeg.split_eeg_signal_axes(images,
split_dim=split_dim)
input_image_length = len(input_image_list)
if input_image_length < 16:
images2 = tf.nn.dropout(images, 0.85)
return images2
# the pooling mapper should choose half size of the image size
pool_s, _ = concat_eeg.pool_eeg_signal_channel(input_image_list, input_image_length/2, 1)
_print_tensor_size(pool_s)
pool_s = tf.mul(pool_s,pool_s)
pool_s = tf.mul(float(kwidth), pool_s)
pool_s = tf.nn.avg_pool(pool_s, ksize=[1, 1, kwidth, 1],
strides=[1, 1, kwidth, 1], padding='VALID')
pool_s = tf.sqrt(pool_s)
pool_s = tf.nn.max_pool(pool_s, ksize=[1, 2, 1, 1],
strides=[1, 2, 1, 1], padding='VALID')
_print_tensor_size(pool_s)
pool_s2 = tf.nn.dropout(pool_s, 0.85)
return pool_s2
def inference_pooling_L2norm_choose_filter(images, kheight=2, kwidth=5):
# channel domain pooling mapper
split_dim = 1 # 1 represents split on spatial domain
input_image_list = split_eeg.split_eeg_signal_axes(images,
split_dim=split_dim)
input_image_length = len(input_image_list)
# the pooling mapper should choose half size of the image size
pool_s, _ = concat_eeg.pool_eeg_signal_channel(input_image_list, input_image_length/2, 1)
_print_tensor_size(pool_s)
input_shape = pool_s.get_shape()
range_even = tf.range(0, input_shape[0], 2)
range_odd = tf.range(1, input_shape[0], 2)
even_rows = tf.nn.embedding_lookup(images, range_even)
odd_rows = tf.nn.embedding_lookup(images, range_odd)
even_rows = tf.mul(pool_s,pool_s)
even_rows = tf.mul(3.0, pool_s)
even_rows = tf.nn.avg_pool(even_rows, ksize=[1, 1, 3, 1],
strides=[1, 1, 3, 1], padding='VALID')
pool_s = tf.sqrt(pool_s)
pool_s = tf.nn.max_pool(pool_s, ksize=[1, 2, 1, 1],
strides=[1, 2, 1, 1], padding='VALID')
_print_tensor_size(pool_s)
return pool_s
def inference_pooling_L2norm_filter(images, kwidth=5):
kheight = 2
# channel domain pooling mapper
split_dim = 1 # 1 represents split on spatial domain
input_image_list = split_eeg.split_eeg_signal_axes(images,
split_dim=split_dim)
input_image_length = len(input_image_list)
if input_image_length < 16:
images2 = tf.nn.dropout(images, 0.85)
return images2
# the pooling mapper should choose half size of the image size
pool_s, _ = concat_eeg.pool_eeg_signal_channel(input_image_list, input_image_length/2, 1)
_print_tensor_size(pool_s)
pool_s = tf.mul(pool_s,pool_s)
pool_s = tf.mul(float(kwidth), pool_s)
pool_s = tf.nn.avg_pool(pool_s, ksize=[1, 1, kwidth, 1],
strides=[1, 1, kwidth, 1], padding='VALID')
pool_s = tf.sqrt(pool_s)
pool_s = tf.nn.max_pool(pool_s, ksize=[1, 2, 1, 1],
strides=[1, 2, 1, 1], padding='VALID')
_print_tensor_size(pool_s)
pool_s2 = tf.nn.dropout(pool_s, 0.85)
return pool_s2
def inference_pooling_t_filter(images, pool_layer_scope, kheight=2, kwidth=2):
# channel domain pooling mapper
split_dim = 1 # 1 represents split on spatial domain
input_image_list = split_eeg.split_eeg_signal_axes(images,
split_dim=split_dim)
input_image_length = len(input_image_list)
# the pooling mapper should choose half size of the image size
pool_t, _ = concat_eeg.pool_eeg_signal_channel(input_image_list, input_image_length/2, 1, is_rep=True)
_print_tensor_size(pool_t)
# apply the normal max pooling methods with stride = 2
pool_t = inference_pooling_n_filter(pool_t, pool_layer_scope, kheight, kwidth)
return pool_t
def inference_pooling_t_filter(images, pool_layer_scope, kheight=2, kwidth=2):
# channel domain pooling mapper
split_dim = 1 # 1 represents split on spatial domain
input_image_list = split_eeg.split_eeg_signal_axes(images,
split_dim=split_dim)
input_image_length = len(input_image_list)
# the pooling mapper should choose half size of the image size
pool_t, _ = concat_eeg.pool_eeg_signal_channel(input_image_list, input_image_length/2, 1, is_rep=True)
_print_tensor_size(pool_t)
# apply the normal max pooling methods with stride = 2
pool_t = inference_pooling_n_filter(pool_t, pool_layer_scope, kheight, kwidth)
return pool_t