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加载张量流模型以测试一些新数据时,我遇到了各种麻烦。当我训练的模型,我用这个:如何加载经过训练的张量流模型
save_model_file = 'my_saved_model'
saver = tf.train.Saver()
save_path = saver.save(sess, save_model_file)
这似乎导致下列文件被创建:
my_saved_model.meta
checkpoint
my_saved_model.index
my_saved_model.data-00000-of-00001
我不知道这些文件的我应该要注意。
现在模型已经过训练,我似乎无法加载它或在不抛出异常的情况下使用它。下面是我在做什么:
def neural_net_data_input(data_shape):
theshape=(None,)+tuple(data_shape)
return tf.placeholder(tf.float32,shape=theshape,name='x')
def neural_net_label_input(n_out):
return tf.placeholder(tf.float32,shape=(None,n_out),name='one_hot_labels')
def neural_net_keep_prob_input():
return tf.placeholder(tf.float32,name='keep_prob')
def do_generate_network(x):
#
# here is where i generate the network layer by layer.
# this code works fine so i am not showing it here
#
pass
#
# Now I want to restore the model
#
tf.reset_default_graph()
input_data_shape=(32,32,1)
final_num_outputs=43
graph1 = tf.Graph()
with graph1.as_default():
x = neural_net_data_input(input_data_shape)
one_hot_labels = neural_net_label_input(final_num_outputs)
keep_prob=neural_net_keep_prob_input()
logits = do_generate_network(x)
# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')
#
# accuracy: we use this for validation testing
#
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
################################
# Evaluate
################################
new_data=myutils.load_pickle_file(SOME_DATA_FILE_NAME)
new_features=new_data['features']
new_one_hot_labels=new_data['labels']
print('Evaluating on new data...')
with tf.Session(graph=graph1) as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
saver.restore(sess,save_model_file)
new_acc = sess.run(accuracy, feed_dict={x: new_features, one_hot_labels: new_one_hot_labels, keep_prob: 1.})
print('Testing Accuracy For New Images: {}'.format(new_acc))
但是当我这样做,我得到这个:
TypeError: Cannot interpret feed_dict key as Tensor: The name 'save/Const:0' refers to a Tensor which does not exist. The operation, 'save/Const', does not exist in the graph.
所以,我尝试移动会议在我的图是这样的:
################################
# Evaluate
################################
print('Evaluating on web data...')
with tf.Session() as sess:
x = neural_net_data_input(input_data_shape)
one_hot_labels = neural_net_label_input(final_num_outputs)
keep_prob=neural_net_keep_prob_input()
logits = do_generate_network(x)
# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')
#
# accuracy: we use this for validation testing
#
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
sess.run(tf.global_variables_initializer())
my_save_dir="/home/carnd/CarND-Traffic-Sign-Classifier-Project"
load_model_meta_file=os.path.join(my_save_dir,"my_saved_model.meta")
load_model_path=os.path.join(my_save_dir,"my_saved_model")
new_saver = tf.train.import_meta_graph(load_model_meta_file)
new_saver.restore(sess, load_model_path)
web_acc = sess.run(accuracy, feed_dict={x: web_features, one_hot_labels: web_one_hot_labels, keep_prob: 1.})
print('Testing Accuracy For Web Images: {}'.format(web_acc))
现在它运行时不会抛出错误,但它打印的准确性结果是0.02!我的训练数据非常相似,我的准确率达到了95%。所以看来我以某种方式错误地加载我的模型。
我在做什么错?
我正在使用tensorflow 1.2 – Marc