我正在按照教程进行操作,可以浏览代码,该代码可以训练神经网络并评估其准确性.
但我不知道如何在新的单个输入(字符串)上使用训练模型来预测其标签.
你能告诉我们如何做到这一点吗？
教程：
会话代码：
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(len(newsgroups_train.data)/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_x,batch_y = get_batch(newsgroups_train,i,batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
c,_ = sess.run([loss,optimizer],Feed_dict={input_tensor: batch_x,output_tensor:batch_y})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:",'%04d' % (epoch+1),"loss=",\
"{:.9f}".format(avg_cost))
print("Optimization Finished!")
# Test model
correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(output_tensor,1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
total_test_data = len(newsgroups_test.target)
batch_x_test,batch_y_test = get_batch(newsgroups_test,total_test_data)
print("Accuracy:",accuracy.eval({input_tensor: batch_x_test,output_tensor: batch_y_test}))
我有一些Python的经验,但基本上没有Tensorflow的经验.
解决方法
首先,我们需要将文本转换为数组：
def text_to_vector(text):
layer = np.zeros(total_words,dtype=float)
for word in text.split(' '):
layer[word2index[word.lower()]] += 1
return layer
# Convert text to vector so we can send it to our model
vector_txt = text_to_vector(text)
# Wrap vector like we do in get_batches()
input_array = np.array([vector_txt])
我们可以保存并加载模型以供重用.我们首先创建一个Saver对象然后保存会话(在训练模型之后)：
saver = tf.train.Saver() ... train the model ... save_path = saver.save(sess,"/tmp/model.ckpt")
在示例模型中,模型体系结构中的最后一个“步骤”(即在polym_perceptron方法中完成的最后一件事)是：
'out': tf.Variable(tf.random_normal([n_classes]))
因此,为了获得预测,我们得到该数组的最大值的索引(预测类)：
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess,"/tmp/model.ckpt")
print("Model restored.")
classification = sess.run(tf.argmax(prediction,Feed_dict={input_tensor: input_array})
print("Predicted category:",classification)
您可以在这里查看整个代码：https://github.com/dmesquita/understanding_tensorflow_nn