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私はTensorFlowでSegNetの実装を進めています。空中映像を「ビルディング」と「ビルドしない」の2つのクラスに分けるために使用しています。私は82％の精度で正確なネットワークを提供しています。複数の畳み込みレイヤを追加することで、エンコーダ/デコーダモデルの精度が低下する
しかし、元のSegNetのように複数の畳み込みレイヤーを追加してネットワークを拡張したいと考えましたが、動作させることができません。
これは私が働く小さなモデルに実装する方法である：
def inference_basic(images, phase_train, batch_size, keep_prob):
conv1 = conv_layer_with_bn(norm1, [7, 7, images.get_shape().as_list()[3], 64], phase_train, name="conv1")
pool1, pool1_indices = tf.nn.max_pool_with_argmax(conv1, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool1')
conv2 = conv_layer_with_bn(pool1, [7, 7, 64, 64], phase_train, name="conv2")
pool2, pool2_indices = tf.nn.max_pool_with_argmax(conv2, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool2')
conv3 = conv_layer_with_bn(pool2, [7, 7, 64, 64], phase_train, name="conv3")
pool3, pool3_indices = tf.nn.max_pool_with_argmax(conv3, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool3')
conv4 = conv_layer_with_bn(pool3, [7, 7, 64, 64], phase_train, name="conv4")
pool4, pool4_indices = tf.nn.max_pool_with_argmax(conv4, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool4')
""" End of encoder """
""" start decoder """
upsample4 = deconv_layer(pool4, [2, 2, 64, 64], [batch_size, FLAGS.image_h//8, FLAGS.image_w//8, 64], 2, "up4")
conv_decode4 = conv_layer_with_bn(upsample4, [7, 7, 64, 64], phase_train, False, name="conv_decode4")
upsample3= deconv_layer(conv_decode4, [2, 2, 64, 64], [batch_size, FLAGS.image_h//4, FLAGS.image_w//4, 64], 2, "up3")
conv_decode3 = conv_layer_with_bn(upsample3, [7, 7, 64, 64], phase_train, False, name="conv_decode3")
upsample2= deconv_layer(conv_decode3, [2, 2, 64, 64], [batch_size, FLAGS.image_h//2, FLAGS.image_w//2, 64], 2, "up2")
conv_decode2 = conv_layer_with_bn(upsample2, [7, 7, 64, 64], phase_train, False, name="conv_decode2")
upsample1= deconv_layer(conv_decode2, [2, 2, 64, 64], [batch_size, FLAGS.image_h, FLAGS.image_w, 64], 2, "up1")
conv_decode1 = conv_layer_with_bn(upsample1, [7, 7, 64, 64], phase_train, False, name="conv_decode1")
""" end of decoder """
""" Start Classify """
with tf.variable_scope('conv_classifier') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[1, 1, 64, FLAGS.num_class],
initializer=msra_initializer(1, 64),
wd=0.0005)
conv = tf.nn.conv2d(conv_decode1, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [FLAGS.num_class], tf.constant_initializer(0.0))
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
return conv_classifier
を、これは本当に悪い結果を取得する拡張モデル、次のとおりです。
def inference(images, phase_train, batch_size):
conv1_1 = conv_layer_with_bn(images, [7, 7, images.get_shape().as_list()[3], 64], phase_train, name="conv1_1")
conv1_2 = conv_layer_with_bn(conv1_1, [7, 7, 64, 64], phase_train, name="conv1_2")
pool1, pool1_indices = tf.nn.max_pool_with_argmax(conv1_2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME', name='pool1')
conv2_1 = conv_layer_with_bn(pool1, [7, 7, 64, 64], phase_train, name="conv2_1")
conv2_2 = conv_layer_with_bn(conv2_1, [7, 7, 64, 64], phase_train, name="conv2_2")
pool2, pool2_indices = tf.nn.max_pool_with_argmax(conv2_2, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool2')
conv3_1 = conv_layer_with_bn(pool2, [7, 7, 64, 64], phase_train, name="conv3_1")
conv3_2 = conv_layer_with_bn(conv3_1, [7, 7, 64, 64], phase_train, name="conv3_2")
conv3_3 = conv_layer_with_bn(conv3_2, [7, 7, 64, 64], phase_train, name="conv3_3")
pool3, pool3_indices = tf.nn.max_pool_with_argmax(conv3_3, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool3')
conv4_1 = conv_layer_with_bn(pool3, [7, 7, 64, 64], phase_train, name="conv4_1")
conv4_2 = conv_layer_with_bn(conv4_1, [7, 7, 64, 64], phase_train, name="conv4_2")
conv4_3 = conv_layer_with_bn(conv4_2, [7, 7, 64, 64], phase_train, name="conv4_3")
pool4, pool4_indices = tf.nn.max_pool_with_argmax(conv4_3, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool4')
conv5_1 = conv_layer_with_bn(pool4, [7, 7, 64, 64], phase_train, name="conv5_1")
conv5_2 = conv_layer_with_bn(conv5_1, [7, 7, 64, 64], phase_train, name="conv5_2")
conv5_3 = conv_layer_with_bn(conv5_2, [7, 7, 64, 64], phase_train, name="conv5_3")
pool5, pool5_indices = tf.nn.max_pool_with_argmax(conv5_3, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME', name='pool5')
""" End of encoder """
""" Start decoder """
upsample5 = deconv_layer(pool5, [2, 2, 64, 64], [batch_size, FLAGS.image_h//16, FLAGS.image_w//16, 64], 2, "up5")
conv_decode5_1 = conv_layer_with_bn(upsample5, [7, 7, 64, 64], phase_train, True, name="conv_decode5_1")
conv_decode5_2 = conv_layer_with_bn(conv_decode5_1, [7, 7, 64, 64], phase_train, True, name="conv_decode5_2")
conv_decode5_3 = conv_layer_with_bn(conv_decode5_2, [7, 7, 64, 64], phase_train, True, name="conv_decode5_3")
upsample4 = deconv_layer(conv_decode5_3, [2, 2, 64, 64], [batch_size, FLAGS.image_h//8, FLAGS.image_w//8, 64], 2, "up4")
conv_decode4_1 = conv_layer_with_bn(upsample4, [7, 7, 64, 64], phase_train, True, name="conv_decode4_1")
conv_decode4_2 = conv_layer_with_bn(conv_decode4_1, [7, 7, 64, 64], phase_train, True, name="conv_decode4_2")
conv_decode4_3 = conv_layer_with_bn(conv_decode4_2, [7, 7, 64, 64], phase_train, True, name="conv_decode4_3")
upsample3 = deconv_layer(conv_decode4_3, [2, 2, 64, 64], [batch_size, FLAGS.image_h//4, FLAGS.image_w//4, 64], 2, "up3")
conv_decode3_1 = conv_layer_with_bn(upsample3, [7, 7, 64, 64], phase_train, True, name="conv_decode3_1")
conv_decode3_2 = conv_layer_with_bn(conv_decode3_1, [7, 7, 64, 64], phase_train, True, name="conv_decode3_2")
conv_decode3_3 = conv_layer_with_bn(conv_decode3_2, [7, 7, 64, 64], phase_train, True, name="conv_decode3_3")
upsample2= deconv_layer(conv_decode3_3, [2, 2, 64, 64], [batch_size, FLAGS.image_h//2, FLAGS.image_w//2, 64], 2, "up2")
conv_decode2_1 = conv_layer_with_bn(upsample2, [7, 7, 64, 64], phase_train, True, name="conv_decode2_1")
conv_decode2_2 = conv_layer_with_bn(conv_decode2_1, [7, 7, 64, 64], phase_train, True, name="conv_decode2_2")
upsample1 = deconv_layer(conv_decode2_2, [2, 2, 64, 64], [batch_size, FLAGS.image_h, FLAGS.image_w, 64], 2, "up1")
conv_decode1_1 = conv_layer_with_bn(upsample1, [7, 7, 64, 64], phase_train, True, name="conv_decode1_1")
conv_decode1_2 = conv_layer_with_bn(conv_decode1_1, [7, 7, 64, 64], phase_train, True, name="conv_decode1_2")
""" End of decoder """
""" Start Classify """
# output predicted class number
with tf.variable_scope('conv_classifier') as scope: #all variables prefixed with "conv_classifier/"
kernel = _variable_with_weight_decay('weights',
shape=[1, 1, 64, FLAGS.num_class],
initializer=msra_initializer(1, 64),
wd=0.0005)
conv = tf.nn.conv2d(conv_decode1_2, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [FLAGS.num_class], tf.constant_initializer(0.0))
conv_classifier = tf.nn.bias_add(conv, biases, name=scope.name)
#logit = conv_classifier = prediction
return conv_classifier
畳み込み層：
def conv_layer_with_bn(inputT, shape, train_phase, activation=True, name=None):
in_channel = shape[2]
out_channel = shape[3]
k_size = shape[0]
with tf.variable_scope(name) as scope:
kernel = _variable_with_weight_decay('weights',
shape=shape,
initializer=msra_initializer(k_size, in_channel),
wd=None)
conv = tf.nn.conv2d(inputT, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [out_channel], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
if activation is True:
conv_out = tf.nn.relu(batch_norm_layer(bias, train_phase, scope.name))
else:
conv_out = batch_norm_layer(bias, train_phase, scope.name)
return conv_out
def batch_norm_layer(inputT, is_training, scope):
"""Used in conv_layer_with_bn()"""
return tf.cond(is_training,
lambda: tf.contrib.layers.batch_norm(inputT, is_training=True,
center=False, updates_collections=None, scope=scope+"_bn"),
lambda: tf.contrib.layers.batch_norm(inputT, is_training=False,
updates_collections=None, center=False, scope=scope+"_bn", reuse = True))
画像内のすべてのピクセルがthに分類されるため、拡張モデルは10％前後になりますe「ビルドしない」クラス。なぜこれが起こっているのか理解するのを助けることができますか？私はSegNetのcaffe implementationを見てきましたが、2つの実装の違いはわかりません。