def DeepLocModel(input_images, is_training):
conv1 = nn_layers.conv_layer(input_images, 3, 3, 2, 64, 1, 'conv_1',is_training=is_training)
conv2 = nn_layers.conv_layer(conv1, 3, 3, 64, 64, 1, 'conv_2', is_training=is_training)
pool1 = nn_layers.pool2_layer(conv2, 'pool1')
conv3 = nn_layers.conv_layer(pool1, 3, 3, 64, 128, 1, 'conv_3', is_training=is_training)
conv4 = nn_layers.conv_layer(conv3, 3, 3, 128, 128, 1, 'conv_4', is_training=is_training)
pool2 = nn_layers.pool2_layer(conv4, 'pool2')
conv5 = nn_layers.conv_layer(pool2, 3, 3, 128, 256, 1, 'conv_5', is_training=is_training)
conv6 = nn_layers.conv_layer(conv5, 3, 3, 256, 256, 1, 'conv_6', is_training=is_training)
conv7 = nn_layers.conv_layer(conv6, 3, 3, 256, 256, 1, 'conv_7', is_training=is_training)
conv8 = nn_layers.conv_layer(conv7, 3, 3, 256, 256, 1, 'conv_8', is_training=is_training)
pool3 = nn_layers.pool2_layer(conv8, 'pool3')
pool3_flat = tf.reshape(pool3, [-1, 8 * 8 * 256])
fc_1 = nn_layers.nn_layer(pool3_flat, 8 * 8 * 256, 512, 'fc_1', act=tf.nn.relu, is_training=is_training)
fc_2 = nn_layers.nn_layer(fc_1, 512, 512, 'fc_2', act=tf.nn.relu,is_training=is_training)
logit = nn_layers.nn_layer(fc_2, 512, 19, 'final_layer', act=None, is_training=is_training)
return logit
1,
'conv_7',
is_training=is_training)
conv8 = nn_layers.conv_layer(conv7,
3,
3,
256,
256,
1,
'conv_8',
is_training=is_training)
pool3 = nn_layers.pool2_layer(conv8, 'pool3')
pool3_flat = tf.reshape(pool3, [-1, 8 * 8 * 256])
fc_1 = nn_layers.nn_layer(pool3_flat,
8 * 8 * 256,
512,
'fc_1',
act=tf.nn.relu,
is_training=is_training)
fc_2 = nn_layers.nn_layer(fc_1,
512,
512,
'fc_2',
act=tf.nn.relu,
is_training=is_training)
lastAct = nn_layers.nn_layer(fc_2,
512,
19,
'final_layer',
act=None,
is_training=is_training)
1,
'conv_7',
is_training=is_training)
conv8 = nn_layers.conv_layer(conv7,
3,
3,
256,
256,
1,
'conv_8',
is_training=is_training)
pool3 = nn_layers.pool2_layer(conv8, 'pool3')
pool3_flat = tf.reshape(pool3, [-1, 8 * 8 * 256])
fc_1 = nn_layers.nn_layer(pool3_flat,
8 * 8 * 256,
512,
'fc_1',
act=tf.nn.relu,
is_training=is_training)
fc_2 = nn_layers.nn_layer(fc_1,
512,
512,
'fc_2',
act=tf.nn.relu,
is_training=is_training)
fc2_drop = tf.nn.dropout(fc_2, keep_prob)
logits = nn_layers.nn_layer(fc2_drop,
512,
numClasses,
'final_layer',
act=None,
is_training=is_training)