我见过几个posts恢复时TF
模型和Google
文档页面位于导出图表但我想我错过了一些东西。
我使用这个中的代码Gist将模型与此 utils 文件一起保存到其中defines该模型
现在我想恢复它并在以前未见过的测试数据中运行,如下所示:
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
total_loss = 0
sess = tf.get_default_session()
acc_steps = len(X_data) // BATCH_SIZE
for i in range(acc_steps):
batch_x, batch_y = next_batch(X_val, Y_val, BATCH_SIZE)
loss, accuracy = sess.run([loss_value, acc], feed_dict={
images_placeholder: batch_x,
labels_placeholder: batch_y,
keep_prob: 0.5
})
total_accuracy += (accuracy * len(batch_x))
total_loss += (loss * len(batch_x))
return (total_accuracy / num_examples, total_loss / num_examples)
## re-execute the code that defines the model
# Image Tensor
images_placeholder = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name='x')
gray = tf.image.rgb_to_grayscale(images_placeholder, name='gray')
gray /= 255.
# Label Tensor
labels_placeholder = tf.placeholder(tf.float32, shape=(None, 43), name='y')
# dropout Tensor
keep_prob = tf.placeholder(tf.float32, name='drop')
# construct model
logits = inference(gray, keep_prob)
# calculate loss
loss_value = loss(logits, labels_placeholder)
# training
train_op = training(loss_value, 0.001)
# accuracy
acc = accuracy(logits, labels_placeholder)
with tf.Session() as sess:
loader = tf.train.import_meta_graph('gtsd.meta')
loader.restore(sess, tf.train.latest_checkpoint('./'))
sess.run(tf.initialize_all_variables())
test_accuracy = evaluate(X_test, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy[0]))
我得到的测试准确度仅为3%。但是,如果我在训练模型后不关闭笔记本并立即运行测试代码,我会得到一个95%准确性。
这让我相信我没有正确加载模型?