Tensorflow-保存模型 [英] Tensorflow - Saving a model
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问题描述
我有以下代码,并且在尝试保存模型时出现错误.我可能在做错什么,如何解决此问题?
I have the following code, and getting an error when trying to save the model. What could I be doing wrong, and how can I solve this issue?
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')
EDIT-1
忘记声明我遇到的错误:-)
Forgot to state the error I'm having :-)
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.
谢谢.
推荐答案
似乎您要存储模型的文件夹不存在(可以检查您当前的工作目录是什么).为了避免这些问题,我将使用绝对路径,并在保存之前执行以下操作:
It seems like the folder in which you want to store the model does not exist (may be check what your current working directory is). To avoid those issues, I would use absolute paths and before saving do something like this:
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)
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