Question

我正在使用Tensorflow构建卷积神经网络。

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial,name = 'weights')

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial, name = 'biases')

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

with tf.Graph().as_default():
    with tf.name_scope('convolution1'):
        W_conv1 = weight_variable([5, 5, 1, 32])
        b_conv1 = bias_variable([32])

    x = tf.placeholder(tf.float32, shape=[None, 96*96])
    y_ = tf.placeholder(tf.float32, shape=[None, 30])
    x_image = tf.reshape(x, [-1,96,96,1])

    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    with tf.name_scope('convolution2'):
        W_conv2 = weight_variable([5, 5, 32, 64])
        b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    with tf.name_scope('connected'):
        W_fc1 = weight_variable([24 * 24 * 64, 1024])
        b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 24*24*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    with tf.name_scope('output'):
        W_fc2 = weight_variable([1024, 30])
        b_fc2 = bias_variable([30])

在此之后我做了一些计算和训练并保存所有变量。

现在我在另一个程序中重新创建相同的图形结构

PROGRAM 2代码段

tf.reset_default_graph()

x = tf.placeholder(tf.float32, shape=[None, 96*96])
x_image = tf.reshape(x, [-1,96,96,1])
y_ = tf.placeholder(tf.float32, shape=[None, 30])

with tf.name_scope('convolution1'):
    W_conv1 = tf.Variable(-1.0, validate_shape = False, name = 'weights')
    b_conv1 = tf.Variable(-1.0, validate_shape = False, name = 'biases')

with tf.name_scope('convolution2'):
    W_conv2 = tf.Variable(-1.0, validate_shape = False, name = 'weights')
    b_conv2 = tf.Variable(-1.0, validate_shape = False, name = 'biases')

with tf.name_scope('connected'):
    W_fc1 = tf.Variable(-1.0, validate_shape = False, name = 'weights')
    b_fc1 = tf.Variable(-1.0, validate_shape = False, name = 'biases')

with tf.name_scope('output'):
    W_fc2 = tf.Variable(-1.0, validate_shape = False, name = 'weights')
    b_fc2 = tf.Variable(-1.0, validate_shape = False, name = 'biases')

session = tf.Session()
saver = tf.train.Saver()
saver.restore(session, 'my-model-2000')
vars_list = tf.get_collection(tf.GraphKeys.VARIABLES)

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
h_pool2_flat = tf.reshape(h_pool2, [-1, 24*24*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
y_convtry = tf.matmul(h_fc1, W_fc2) + b_fc2
y_conv_alternate = 95.99*tf.ones_like(y_convtry)
y_conv = tf.select(tf.greater(y_convtry, y_conv_alternate), y_conv_alternate, y_convtry)

cost = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(tf.select(tf.is_nan(y_), y_conv, y_) - y_conv), reduction_indices=[1])))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cost,var_list = vars_list)

问题是vars_list中的变量，当我试图获得它们的形状时，它们仍然没有显示，但是跑步：

vars_list[i].eval(session = session)

正在给出正确答案，以便恢复正常。

我的问题是为什么isn＆＃t; tars_list [i] .get_shape（）给出了错误的答案tf.shape（vars_list [i]）似乎无法正常工作。

这是一个问题，因为当我使用

时

tf.AdamOptimizer.minimize(cost) //This internally call var.get_shape() and throws error

Answer 1

当您在创建tf.Variable时设置validate_shape=False时，这告诉TensorFlow该变量可以包含任何形状的数据，并允许您（例如）将任意形状的检查点数据恢复到变量中。但是，这为TensorFlow提供了关于变量形状的静态信息，例如AdamOptimizer.minimize()用于构建适当形状的累加器槽。

最好的解决方案是重复使用相同的代码来创建您在第一个程序中使用的变量，即

with tf.name_scope('convolution1'):
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])

......等等。这些变量的初始化函数永远不会运行，因此以这种方式编写它不需要额外的成本。

Tensorflow恢复命名变量

1 个答案: