问题遇到的现象和发生背景
想复现书中的Google Net
问题相关代码，请勿粘贴截图
import tensorflow as tf
slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
def inception_v3_arg_scope(weight_decay=0.00004,
stddev=0.1,
batch_norm_var_collection='moving_vars '):
batch_norm_params = {
'decay': 0.9997,
'epsilon': 0.001,
'updates_collections': tf.GraphKeys.UPDATE_OPS,
'variables_collections': {
'beta': None,
'gamma': None,
'moving_mean': [batch_norm_var_collection],
'moving_variance': [batch_norm_var_collection],
}
}
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope(
[slim.conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as sc:
return sc
def inception_v3_base(inputs, scope=None):
end_points = {}
with tf.variable_scope(scope, 'InceptionV3', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='VALID '):
net = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a_3x3')
net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3 ')
net = slim.conv2d(net, 64, [3, 3], padding='SAME',
scope='Conv2d_2b_3x3')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')
net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')
net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')
net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')
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)
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='Conv_1_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)
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)
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)
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_oc_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)
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)
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)
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, 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)
end_points['Mixed_6e'] = net
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_ec_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)
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)
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_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)
return net, end_points
def inception_v3(inputs,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
prediction_fn=slim.softmax,
spatial_squeeze=True,
reuse=None,
scope='Inceptionv3'):
with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes],
reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points = inception_v3_base(inputs, scope=scope)
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
aux_logits = end_points['Mixed_6e']
with tf.variable_scope('AuXLogits'):
aux_logits = slim.avg_pool2d(
aux_logits, [5, 5], stride=3, padding='VALID',
scope='AvgPool_1a_5x5')
aux_1ogits = slim.conv2d(aux_logits, 128, [1, 1],
scope='Conv2d_1b_1x1')
aux_logits = slim.conv2d(
aux_logits, 768, [5, 5],
weights_initializer=trunc_normal(0.01),
padding='VALID', scope='Conv2d_2a_5x5')
aux_logits = slim.conv2d(
aux_logits, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, weights_initializer=trunc_normal(0.001),
scope='Conv2d_2b_1x1')
if spatial_squeeze:
aux_logits = tf.squeeze(aux_logits, [1, 2],
name='SpatialSqueeze')
end_points['AuxLogits '] = aux_logits
with tf.variable_scope('Logits'):
net = slim.avg_pool2d(net, [8, 8], padding='VALID',
scope='AvgPool_1a_8x8')
net = slim.dropout(net, keep_prob=dropout_keep_prob,
scope='Dropout_1b')
end_points['PreLogits'] = net
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='Spatialsqueeze')
end_points['Logits'] = logits
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points
运行结果及报错内容
Traceback (most recent call last):
File "C:/Users/Admin/PycharmProjects/pythonProject/Google Net.py", line 3, in
slim = tf.contrib.slim
AttributeError: module 'tensorflow' has no attribute 'contrib'
在 'init.py | init.py' 中找不到引用 'contrib'
在 'init.py | init.py' 中找不到引用 'truncated_normal_initializer'
在 'init.py | init.py' 中找不到引用 'GraphKeys'
在 'init.py | init.py' 中找不到引用 'variable_scope'
类 'float' 未定义 'getitem'，所以不能对其实例使用 '[]' 运算符
