def shallow2(inpt, inpt_size, is_training):
'''
18-ticnn
'''
end_points = {}
keep_prob = tf.cond(is_training, lambda: tf.constant(0.5),
lambda: tf.constant(1.0))
with tf.name_scope('reshape'):
x_image = tf.reshape(inpt, [-1, inpt_size, 1, 1])
## first conv-----------------------------
num_feature = 16
out = tu.add_conv1d_layer(x_image,
num_feature,
9,
2,
is_training=is_training,
layer_name='conv1')
end_points['conv1'] = out
out = tu.batch_normalization(out, is_training=is_training)
out = tu.max_pool(out, ksize=4, stride=2, layer_name='pool1')
end_points['pool1'] = out
tu.print_activations(out)
out = tu.add_conv1d_layer(out,
num_feature,
9,
2,
is_training=is_training,
layer_name='conv2')
end_points['conv2'] = out
out = tu.max_pool(out, ksize=4, stride=2, layer_name='pool2')
end_points['pool2'] = out
tu.print_activations(out)
## fully connected layers-----------------------------
with tf.name_scope('fc1'):
out = tu.add_fc_layer(out,
100,
relu=True,
BN=True,
is_training=is_training) # previously 256 nodes
out = tf.nn.dropout(out, keep_prob)
with tf.name_scope('fc2-1'):
out1 = tu.add_fc_layer(out, 3)
with tf.name_scope('fc2-2'):
print(end_points.keys())
out2 = tu.add_fc_layer(out, 1)
end_points['class_end'] = out1
end_points['speed_end'] = out2
return end_points
def cvgg19_with_pindex(inpt, inpt_size, is_training):
end_points = {}
keep_prob = tf.cond(is_training, lambda: tf.constant(0.5),
lambda: tf.constant(1.0))
with tf.name_scope('reshape'):
x_image = tf.reshape(inpt, [-1, inpt_size, 1, 1])
## first conv-----------------------------
with tf.name_scope('block1'):
num_feature = 32
out = tu.add_conv1d_layer(x_image,
num_feature,
9,
2,
BN=False,
layer_name='conv1')
end_points['conv1'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
2,
BN=False,
layer_name='conv2')
end_points['conv2'] = out
out = tu.batch_normalization(out, is_training=is_training)
out, pool_args = tu.max_pool_with_argmax(out, 4, layer_name='pool1')
end_points['pool1'] = out
end_points['pool1_arg'] = pool_args
tu.print_activations(out)
## second conv-----------------------------
with tf.name_scope('block2'):
num_feature = num_feature * 2
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv3')
end_points['conv3'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv4',
print_activation=True)
end_points['conv4'] = out
out, pool_args = tu.max_pool_with_argmax(out, 4, layer_name='pool2')
end_points['pool2_arg'] = pool_args
end_points['pool2'] = out
tu.print_activations(out)
## third conv-----------------------------
with tf.name_scope('block3'):
num_feature = num_feature * 2
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv6')
end_points['conv6'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv7')
end_points['conv7'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv8',
print_activation=True)
end_points['conv8'] = out
out, pool_args = tu.max_pool_with_argmax(out, 4, layer_name='pool3')
end_points['pool3_arg'] = pool_args
end_points['pool3'] = out
tu.print_activations(out)
## forth conv-----------------------------
with tf.name_scope('block4'):
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv10')
end_points['conv10'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv11')
end_points['conv11'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv12',
print_activation=True)
end_points['conv12'] = out
out, pool_args = tu.max_pool_with_argmax(out, 4, layer_name='pool4')
end_points['pool4_arg'] = pool_args
# out = tu.global_average_pool(out)
end_points['pool4'] = out
tu.print_activations(out)
## fully connected layers-----------------------------
with tf.name_scope('fc1'):
out = tu.add_fc_layer(out,
256,
relu=True,
BN=True,
is_training=is_training)
out = tf.nn.dropout(out, keep_prob)
with tf.name_scope('fc4'):
out = tu.add_fc_layer(out, 3)
with tf.name_scope('block8'):
print(end_points.keys())
out2 = tu.add_fc_layer(end_points['pool4'],
256,
relu=True,
BN=True,
is_training=is_training)
out2 = tf.nn.dropout(out2, keep_prob)
with tf.name_scope('block9'):
out2 = tu.add_fc_layer(out2, 1)
end_points['class_end'] = out
end_points['speed_end'] = out2
return end_points
def cvgg19_3(inpt, inpt_size, is_training, num_class=3):
end_points = {}
keep_prob = tf.cond(is_training, lambda: tf.constant(0.5),
lambda: tf.constant(1.0))
with tf.name_scope('reshape'):
x_image = tf.reshape(inpt, [-1, inpt_size, 1, 1])
## first conv-----------------------------
num_feature = 16
out = tu.add_conv1d_layer(x_image,
num_feature,
9,
2,
BN=False,
layer_name='conv1')
end_points['conv1'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
2,
BN=False,
layer_name='conv2')
end_points['conv2'] = out
out = tu.batch_normalization(out, is_training=is_training)
out = tu.max_pool(out, ksize=4, layer_name='pool1')
end_points['pool1'] = out
tu.print_activations(out)
## second conv-----------------------------
num_feature = num_feature * 2
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv3')
end_points['conv3'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv4',
print_activation=True)
end_points['conv4'] = out
out = tu.max_pool(out, ksize=4, layer_name='pool2')
end_points['pool2'] = out
tu.print_activations(out)
## third conv-----------------------------
num_feature = num_feature * 2
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv6')
end_points['conv5'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv7')
end_points['conv6'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv8',
print_activation=True)
end_points['conv7'] = out
out = tu.max_pool(out, ksize=4, layer_name='pool3')
end_points['pool3'] = out
tu.print_activations(out)
## forth conv-----------------------------
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv10')
end_points['conv8'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv11')
end_points['conv9'] = out
out1 = tu.add_conv1d_layer(out,
num_class,
1,
is_training=is_training,
layer_name='conv10',
print_activation=True)
out2 = tu.add_conv1d_layer(out,
1,
1,
is_training=is_training,
layer_name='conv11',
print_activation=True)
end_points['conv10'] = out1
# out = tu.max_pool(out, ksize=4, layer_name='pool4')
out1 = tu.global_average_pool(out1)
out2 = tu.global_average_pool(out2)
end_points['GAP'] = out1
tu.print_activations(out1)
out1 = tf.reshape(out1, [-1, num_class])
out2 = tf.reshape(out2, [-1, 1])
print("out1" + str(out1.get_shape().as_list()))
print("out2" + str(out2.get_shape().as_list()))
end_points['class_end'] = out1
end_points['speed_end'] = out2
return end_points
def cvgg19_2(inpt, inpt_size, is_training):
end_points = {}
keep_prob = tf.cond(is_training, lambda: tf.constant(0.5),
lambda: tf.constant(1.0))
with tf.name_scope('reshape'):
x_image = tf.reshape(inpt, [-1, inpt_size, 1, 1])
## first conv-----------------------------
num_feature = 16
out = tu.add_conv1d_layer(x_image,
num_feature,
9,
2,
BN=False,
layer_name='conv1')
end_points['conv1'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
2,
BN=False,
layer_name='conv2')
end_points['conv2'] = out
out = tu.batch_normalization(out, is_training=is_training)
out = tu.max_pool(out, ksize=4, layer_name='pool1')
end_points['pool1'] = out
tu.print_activations(out)
## second conv-----------------------------
num_feature = num_feature * 2
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv3')
end_points['conv3'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv4',
print_activation=True)
end_points['conv4'] = out
out = tu.max_pool(out, ksize=4, layer_name='pool2')
end_points['pool2'] = out
tu.print_activations(out)
## third conv-----------------------------
num_feature = num_feature * 2
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv6')
end_points['conv5'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv7')
end_points['conv6'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv8',
print_activation=True)
end_points['conv7'] = out
out = tu.max_pool(out, ksize=4, layer_name='pool3')
end_points['pool3'] = out
tu.print_activations(out)
## forth conv-----------------------------
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv10')
end_points['conv8'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
BN=False,
layer_name='conv11')
end_points['conv9'] = out
out = tu.add_conv1d_layer(out,
num_feature,
9,
is_training=is_training,
layer_name='conv12',
print_activation=True)
end_points['conv10'] = out
# out = tu.max_pool(out, ksize=4, layer_name='pool4')
out = tu.global_average_pool(out)
end_points['GAP'] = out
tu.print_activations(out)
## fully connected layers-----------------------------
with tf.name_scope('fc1'):
out = tu.add_fc_layer(out,
256,
relu=True,
BN=True,
is_training=is_training)
out = tf.nn.dropout(out, keep_prob)
with tf.name_scope('fc2-1'):
out1 = tu.add_fc_layer(out, 3)
with tf.name_scope('fc2-2'):
out2 = tu.add_fc_layer(out, 1)
end_points['class_end'] = out1
end_points['speed_end'] = out2
return end_points