Question

我有以下代码，并在尝试保存模型时出错。我可能做错了什么，我该如何解决这个问题？

import tensorflow as tf

data, labels = cifar_tools.read_data('C:\\Users\\abc\\Desktop\\Testing')

x = tf.placeholder(tf.float32, [None, 150 * 150])
y = tf.placeholder(tf.float32, [None, 2])

w1 = tf.Variable(tf.random_normal([5, 5, 1, 64]))
b1 = tf.Variable(tf.random_normal([64]))

w2 = tf.Variable(tf.random_normal([5, 5, 64, 64]))
b2 = tf.Variable(tf.random_normal([64]))

w3 = tf.Variable(tf.random_normal([38*38*64, 1024]))
b3 = tf.Variable(tf.random_normal([1024]))

w_out = tf.Variable(tf.random_normal([1024, 2]))
b_out = tf.Variable(tf.random_normal([2]))

def conv_layer(x,w,b):
    conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
    conv_with_b = tf.nn.bias_add(conv,b)
    conv_out = tf.nn.relu(conv_with_b)
    return conv_out

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

def model():
    x_reshaped = tf.reshape(x, shape=[-1, 150, 150, 1])

    conv_out1 = conv_layer(x_reshaped, w1, b1)
    maxpool_out1 = maxpool_layer(conv_out1)
    norm1 = tf.nn.lrn(maxpool_out1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
    conv_out2 = conv_layer(norm1, w2, b2)
    norm2 = tf.nn.lrn(conv_out2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
    maxpool_out2 = maxpool_layer(norm2)

    maxpool_reshaped = tf.reshape(maxpool_out2, [-1, w3.get_shape().as_list()[0]])
    local = tf.add(tf.matmul(maxpool_reshaped, w3), b3)
    local_out = tf.nn.relu(local)

    out = tf.add(tf.matmul(local_out, w_out), b_out)
    return out

model_op = model()

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(model_op, y))
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

correct_pred = tf.equal(tf.argmax(model_op, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    onehot_labels = tf.one_hot(labels, 2, on_value=1.,off_value=0.,axis=-1)
    onehot_vals = sess.run(onehot_labels)
    batch_size = 1
    saver = tf.train.Saver()
    saved_path = saver.save(sess, 'mymodel')
    print("The model is in this file: ", saved_path)


for j in range(0, 5):
    print('EPOCH', j)
    for i in range(0, len(data), batch_size):
        batch_data = data[i:i+batch_size, :]
        batch_onehot_vals = onehot_vals[i:i+batch_size, :]
        _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: batch_data, y: batch_onehot_vals})
        print(i, accuracy_val)

    print('DONE WITH EPOCH')

修改-1

忘记说出我遇到的错误： - ）

Traceback (most recent call last):
  File "cnn.py", line 67, in <module>
    save_path = saver.save(sess, 'mymodel')
  File "C:\Python35\lib\site-packages\tensorflow\python\training\saver.py", line 1314, in save
    "Parent directory of {} doesn't exist, can't save.".format(save_path))
ValueError: Parent directory of mymodel doesn't exist, can't save.

感谢。

Answer 1

似乎您要存储模型的文件夹不存在（可能会检查您当前的工作目录是什么）。为了避免这些问题，我会使用绝对路径，在保存之前执行以下操作：

save_path = ...
if not os.path.exists(save_path):
    os.makedirs(save_path)
...
saver = tf.train.Saver()
with tf.Session() as sess:
    ...
    saved_path = saver.save(sess, os.path.join(save_path, 'my_model')
    print("The model is in this file: ", saved_path)

Answer 2

您也可以使用saved_path = saver.save(sess, './mymodel')

Tensorflow - 保存模型

2 个答案: