手写汉字笔迹识别模型：
第一名用的是googleNet，准确率97.3%
def GoogleLeNetSlim(x, num_classes, keep_prob=0.5):
with tf.variable_scope(\'main\'):
t = slim.conv2d(x, 64, [3, 3], [1, 1], padding=\'SAME\', activation_fn=relu, normalizer_fn=slim.batch_norm, scope=\'conv1\')
t = slim.max_pool2d(t, [2, 2], [2, 2], padding=\'SAME\')
t = slim.conv2d(t, 96, [3, 3], [1, 1], padding=\'SAME\', activation_fn=relu, normalizer_fn=slim.batch_norm, scope=\'conv2\')
t = slim.conv2d(t, 192, [3, 3], [1, 1], padding=\'SAME\', activation_fn=relu, normalizer_fn=slim.batch_norm, scope=\'conv3\')
t = slim.max_pool2d(t, [2, 2], [2, 2], padding=\'SAME\')
with tf.variable_scope(\'block1\'):
t = block_slim(t, [64, 96, 128, 16, 32, 32], name=\'block1\') # [?, 16, 16, 256]
with tf.variable_scope(\'block2\'):
t = block_slim(t, [128, 128, 192, 32, 96, 64], name=\'block1\') # [?, 16, 16, 480]
t = tf.nn.max_pool(t, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\'SAME\')
with tf.variable_scope(\'block3\'):
t = block_slim(t, [192, 96, 208, 16, 48, 64], name=\'block1\')
t = block_slim(t, [160, 112, 224, 24, 64, 64], name=\'block2\')
t = block_slim(t, [128, 128, 256, 24, 64, 64], name=\'block3\')
t = block_slim(t, [112, 144, 288, 32, 64, 64], name=\'block4\')
t = block_slim(t, [256, 160, 320, 32, 128, 128], name=\'block5\') # [?, 8, 8, 832]
t = tf.nn.max_pool(t, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\'SAME\')
with tf.variable_scope(\'block4\'):
t = block_slim(t, [256, 160, 320, 32, 128, 128], name=\'block1\')
t = block_slim(t, [384, 192, 384, 48, 128, 128], name=\'block2\') # [?, 8, 8, 1024]
t = tf.nn.max_pool(t, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\'SAME\')
with tf.variable_scope(\'fc\'):
t = slim.flatten(t)
t = slim.fully_connected(slim.dropout(t, keep_prob), 1024, activation_fn=relu, normalizer_fn=slim.batch_norm, scope=\'fc1\')
t = slim.fully_connected(slim.dropout(t, keep_prob), num_classes, activation_fn=None, scope=\'logits\')
return t
TODO：实验下，https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py
还有使用inception v3的！！！
def build_graph_all(top_k,scope=None):
keep_prob = tf.placeholder(dtype=tf.float32, shape=[], name=\'keep_prob\')
images = tf.placeholder(dtype=tf.float32, shape=[None, image_size, image_size, 1], name=\'image_batch\')
labels = tf.placeholder(dtype=tf.int64, shape=[None], name=\'label_batch\')
with tf.variable_scope(scope,\'Incept_Net\',[images]):
with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],stride=1,padding=\'VALID\'):
net = slim.conv2d(images,32,[3,3],scope=\'conv2d_1a_3x3\')
print(\'tensor 1:\' + str(net.get_shape().as_list()))
net = slim.conv2d(net,32,[3,3],scope=\'conv2d_2a_3x3\')
print(\'tensor 2:\' + str(net.get_shape().as_list()))
net = slim.conv2d(net,64,[3,3],padding=\'SAME\',scope=\'conv2d_2b_3x3\')
print(\'tensor 3:\' + str(net.get_shape().as_list()))
net = slim.max_pool2d(net,[3,3],stride=2,scope=\'maxpool_3a_3x3\')
print(\'tensor 4:\' + str(net.get_shape().as_list()))
net = slim.conv2d(net,80,[1,1],scope=\'conv2d_3b_1x1\')
print(\'tensor 5:\' + str(net.get_shape().as_list()))
net = slim.conv2d(net,192,[3,3],scope=\'conv2d_4a_3x3\')
print(\'tensor 6:\' + str(net.get_shape().as_list()))
net = slim.max_pool2d(net,[3,3],stride=2,scope=\'maxpool_5a_3x3\')
print(\'tensor 7:\' + str(net.get_shape().as_list()))
with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],stride=1,padding=\'SAME\'):
with tf.variable_scope(\'mixed_5b\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,64,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,48,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,64,[5,5],scope=\'conv2d_0b_5x5\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,64,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,96,[3,3],scope=\'conv2d_0b_3x3\')
branch_2 = slim.conv2d(branch_2,96,[3,3],scope=\'conv2d_0c_3x3\')
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,32,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 8:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_5c\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,64,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,48,[1,1],scope=\'conv2d_0b_1x1\')
branch_1 = slim.conv2d(branch_1,64,[5,5],scope=\'conv2d_0c_5x5\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,64,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,96,[3,3],scope=\'conv2d_0b_3x3\')
branch_2 = slim.conv2d(branch_2,96,[3,3],scope=\'conv2d_0c_3x3\')
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,64,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 9:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_5d\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,64,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,48,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,64,[5,5],scope=\'conv2d_0b_5x5\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,64,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,96,[3,3],scope=\'conv2d_0b_3x3\')
branch_2 = slim.conv2d(branch_2,96,[3,3],scope=\'conv2d_0c_3x3\')
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,64,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 10:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_6a\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,384,[3,3],stride=2,padding=\'VALID\',scope=\'conv2d_1a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,64,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,96,[3,3],scope=\'conv2d_0b_3x3\')
branch_1 = slim.conv2d(branch_1,96,[3,3],stride=2,padding=\'VALID\',scope=\'conv2d_1a_1x1\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.max_pool2d(net,[3,3],stride=2,padding=\'VALID\',scope=\'maxpool_1a_3x3\')
net = tf.concat([branch_0,branch_1,branch_2],3)
print(\'tensor 11:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_6b\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,128,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,128,[1,7],scope=\'conv2d_0b_1x7\')
branch_1 = slim.conv2d(branch_1,192,[7,1],scope=\'conv2d_0c_7x1\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,128,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,128,[7,1],scope=\'conv2d_0b_7x1\')
branch_2 = slim.conv2d(branch_2,128,[1,7],scope=\'conv2d_0c_1x7\')
branch_2 = slim.conv2d(branch_2,128,[7,1],scope=\'conv2d_0d_7x1\')
branch_2 = slim.conv2d(branch_2,192,[1,7],scope=\'conv2d_0e_1x7\')
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,192,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 12:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_6c\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,160,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,160,[1,7],scope=\'conv2d_0b_1x7\')
branch_1 = slim.conv2d(branch_1,192,[7,1],scope=\'conv2d_0c_7x1\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,160,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,160,[7,1],scope=\'conv2d_0b_7x1\')
branch_2 = slim.conv2d(branch_2,160,[1,7],scope=\'conv2d_0c_1x7\')
branch_2 = slim.conv2d(branch_2,160,[7,1],scope=\'conv2d_0d_7x1\')
branch_2 = slim.conv2d(branch_2,192,[1,7],scope=\'conv2d_0e_1x7\')
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,192,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 13:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_6d\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,160,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,160,[1,7],scope=\'conv2d_0b_1x7\')
branch_1 = slim.conv2d(branch_1,192,[7,1],scope=\'conv2d_0c_7x1\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,160,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,160,[7,1],scope=\'conv2d_0b_7x1\')
branch_2 = slim.conv2d(branch_2,160,[1,7],scope=\'conv2d_0c_1x7\')
branch_2 = slim.conv2d(branch_2,160,[7,1],scope=\'conv2d_0d_7x1\')
branch_2 = slim.conv2d(branch_2,192,[1,7],scope=\'conv2d_0e_1x7\')
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,192,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 14:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_6e\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,192,[1,7],scope=\'conv2d_0b_1x7\')
branch_1 = slim.conv2d(branch_1,192,[7,1],scope=\'conv2d_0c_7x1\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,192,[7,1],scope=\'conv2d_0b_7x1\')
branch_2 = slim.conv2d(branch_2,192,[1,7],scope=\'conv2d_0c_1x7\')
branch_2 = slim.conv2d(branch_2,192,[7,1],scope=\'conv2d_0d_7x1\')
branch_2 = slim.conv2d(branch_2,192,[1,7],scope=\'conv2d_0e_1x7\')
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,192,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 15:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_7a\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
branch_0 = slim.conv2d(branch_0,320,[3,3],stride=2,padding=\'VALID\',scope=\'conv2d_1a_3x3\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,192,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = slim.conv2d(branch_1,192,[1,7],scope=\'conv2d_0b_1x7\')
branch_1 = slim.conv2d(branch_1,192,[7,1],scope=\'conv2d_0c_7x1\')
branch_1 = slim.conv2d(branch_1,192,[3,3],stride=2,padding=\'VALID\',scope=\'conv2d_1a_3x3\')
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.max_pool2d(net,[3,3],stride=2,padding=\'VALID\',scope=\'maxpool_1a_3x3\')
net = tf.concat([branch_0,branch_1,branch_2],3)
print(\'tensor 16:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_7b\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,320,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,384,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = tf.concat([
slim.conv2d(branch_1,384,[1,3],scope=\'conv2d_0b_1x3\'),
slim.conv2d(branch_1,384,[3,1],scope=\'conv2d_0b_3x1\')
],3)
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,448,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,384,[3,3],scope=\'conv2d_0b_3x3\')
branch_2 = tf.concat([
slim.conv2d(branch_2,384,[1,3],scope=\'conv2d_0c_1x3\'),
slim.conv2d(branch_2,384,[3,1],scope=\'conv2d_0d_3x1\')
],3)
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,192,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 17:\' + str(net.get_shape().as_list()))
with tf.variable_scope(\'mixed_7c\'):
with tf.variable_scope(\'branch_0\'):
branch_0 = slim.conv2d(net,320,[1,1],scope=\'conv2d_0a_1x1\')
with tf.variable_scope(\'branch_1\'):
branch_1 = slim.conv2d(net,384,[1,1],scope=\'conv2d_0a_1x1\')
branch_1 = tf.concat([
slim.conv2d(branch_1,384,[1,3],scope=\'conv2d_0b_1x3\'),
slim.conv2d(branch_1,384,[3,1],scope=\'conv2d_0c_3x1\')],3)
with tf.variable_scope(\'branch_2\'):
branch_2 = slim.conv2d(net,448,[1,1],scope=\'conv2d_0a_1x1\')
branch_2 = slim.conv2d(branch_2,384,[3,3],scope=\'conv2d_0b_3x3\')
branch_2 = tf.concat([
slim.conv2d(branch_2,384,[1,3],scope=\'conv2d_0c_1x3\'),
slim.conv2d(branch_2,384,[3,1],scope=\'conv2d_0d_3x1\')],3)
with tf.variable_scope(\'branch_3\'):
branch_3 = slim.avg_pool2d(net,[3,3],scope=\'avgpool_0a_3x3\')
branch_3 = slim.conv2d(branch_3,192,[1,1],scope=\'conv2d_0b_1x1\')
net = tf.concat([branch_0,branch_1,branch_2,branch_3],3)
print(\'tensor 18:\' + str(net.get_shape().as_list()))
with slim.arg_scope([slim.conv2d,slim.max_pool2d,slim.avg_pool2d],stride=1,padding=\'SAME\'):
with tf.variable_scope(\'logits\'):
net = slim.avg_pool2d(net,[3,3],padding=\'VALID\',scope=\'avgpool_1a_3x3\')
print(\'tensor 19:\' + str(net.get_shape().as_list()))
net = slim.dropout(net,keep_prob=keep_prob,scope=\'dropout_1b\')
logits = slim.conv2d(net, char_size,[2,2],padding=\'VALID\',activation_fn=None,normalizer_fn=None,
scope=\'conv2d_1c_2x2\')
print(\'logits 1:\' + str(logits.get_shape().as_list()))
logits = tf.squeeze(logits,[1,2],name=\'spatialsqueeze\')
print(\'logits 2:\' + str(logits.get_shape().as_list()))
regularization_loss = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
total_loss = loss + regularization_loss
print(\'get total_loss\')
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))
global_step = tf.get_variable("step", [], initializer=tf.constant_initializer(0.0), trainable=False)
rate = tf.train.exponential_decay(2e-3, global_step, decay_steps=2000, decay_rate=0.97, staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = tf.train.AdamOptimizer(learning_rate=rate).minimize(total_loss, global_step=global_step)
probabilities = tf.nn.softmax(logits)
tf.summary.scalar(\'loss\', loss)
tf.summary.scalar(\'accuracy\', accuracy)
merged_summary_op = tf.summary.merge_all()
predicted_val_top_k, predicted_index_top_k = tf.nn.top_k(probabilities, k=top_k)
accuracy_in_top_k = tf.reduce_mean(tf.cast(tf.nn.in_top_k(probabilities, labels, top_k), tf.float32))
return {\'images\': images,
\'labels\': labels,
\'keep_prob\': keep_prob,
\'top_k\': top_k,
\'global_step\': global_step,
\'train_op\': train_op,
\'loss\': total_loss,
\'accuracy\': accuracy,
\'accuracy_top_k\': accuracy_in_top_k,
\'merged_summary_op\': merged_summary_op,
\'predicted_distribution\': probabilities,
\'predicted_index_top_k\': predicted_index_top_k,
\'predicted_val_top_k\': predicted_val_top_k}
用resnet v2的:
resnet_v2.default_image_size = 128
def resnet_v2_50(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
spatial_squeeze=True,
reuse=None,
scope=\'resnet_v2_50\'):
"""ResNet-50 model of [1]. See resnet_v2() for arg and return description."""
blocks = [
resnet_v2_block(\'block1\', base_depth=64, num_units=3, stride=2),
resnet_v2_block(\'block2\', base_depth=128, num_units=4, stride=2),
resnet_v2_block(\'block3\', base_depth=256, num_units=6, stride=2),
resnet_v2_block(\'block4\', base_depth=512, num_units=3, stride=1),
]
return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
global_pool=global_pool, output_stride=output_stride,
include_root_block=True, spatial_squeeze=spatial_squeeze,
reuse=reuse, scope=scope)
resnet_v2_50.default_image_size = resnet_v2.default_image_size
def resnet_v2_101(inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
spatial_squeeze=True,
reuse=None,
scope=\'resnet_v2_101\'):
"""ResNet-101 model of [1]. See resnet_v2() for arg and return description."""
blocks = [
resnet_v2_block(\'block1\', base_depth=64, num_units=3, stride=2),
resnet_v2_block(\'block2\', base_depth=128, num_units=4, stride=2),
resnet_v2_block(\'block3\', base_depth=256, num_units=23, stride=2),
resnet_v2_block(\'block4\', base_depth=512, num_units=3, stride=1),
]
return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
global_pool=global_pool, output_stride=output_stride,
include_root_block=True, spatial_squeeze=spatial_squeeze,
reuse=reuse, scope=scope)
def build_graph(top_k, is_training):
# with tf.device(\'/cpu:0\'):
keep_prob = tf.placeholder(dtype=tf.float32, shape=[], name=\'keep_prob\')
images = tf.placeholder(dtype=tf.float32, shape=[None, 128, 128, 1], name=\'image_batch\')
labels = tf.placeholder(dtype=tf.int64, shape=[None], name=\'label_batch\')
logits, _ = resnet_v2_50(images, num_classes=3755, is_training=is_training, global_pool=True,
output_stride=None, spatial_squeeze=True, reuse=None)
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