Python 如何使用经过训练的 Tensorflow 模型预测值
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How to predict values with a trained Tensorflow model
提问by T.Poe
I've trained my NN in Tensorflow and saved the model like this:
我已经在 Tensorflow 中训练了我的神经网络，并像这样保存了模型：
def neural_net(x):
layer_1 = tf.layers.dense(inputs=x, units=195, activation=tf.nn.sigmoid)
out_layer = tf.layers.dense(inputs=layer_1, units=6)
return out_layer
train_x = pd.read_csv("data_x.csv", sep=" ")
train_y = pd.read_csv("data_y.csv", sep=" ")
train_x = train_x / 6 - 0.5
train_size = 0.9
train_cnt = int(floor(train_x.shape[0] * train_size))
x_train = train_x.iloc[0:train_cnt].values
y_train = train_y.iloc[0:train_cnt].values
x_test = train_x.iloc[train_cnt:].values
y_test = train_y.iloc[train_cnt:].values
x = tf.placeholder("float", [None, 386])
y = tf.placeholder("float", [None, 6])
nn_output = neural_net(x)
cost = tf.reduce_mean(tf.losses.mean_squared_error(labels=y, predictions=nn_output))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
training_epochs = 5000
display_step = 1000
batch_size = 30
keep_prob = tf.placeholder("float")
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(training_epochs):
total_batch = int(len(x_train) / batch_size)
x_batches = np.array_split(x_train, total_batch)
y_batches = np.array_split(y_train, total_batch)
for i in range(total_batch):
batch_x, batch_y = x_batches[i], y_batches[i]
_, c = sess.run([optimizer, cost],
feed_dict={
x: batch_x,
y: batch_y,
keep_prob: 0.8
})
saver.save(sess, 'trained_model', global_step=1000)
Now I want to use the trained model in a different file. Of course there are many many examples of restoring and saving the model, I went through lots of them. Still I couldn't make any of them work, there is always some kind of error. So this is my restore file, could you please help me to make it restore the saved model?
现在我想在不同的文件中使用经过训练的模型。当然，恢复和保存模型的例子有很多，我经历了很多。我仍然无法使它们中的任何一个工作，总是存在某种错误。所以这是我的恢复文件，你能帮我恢复保存的模型吗？
saver = tf.train.import_meta_graph('trained_model-1000.meta')
y_pred = []
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('./'))
sess.run([y_pred], feed_dict={x: input_values})
E.g. this attempt gave me the error "The session graph is empty. Add operations to the graph before calling run()." So what operation should I add to the graph and how? I don't know what that operation should be in my model... I don't understand this whole concept of saving/restoring in Tensorflow. Or should I do the restoring completely differently? Thanks in advance!
例如，此尝试给了我错误“会话图为空。在调用 run() 之前向图中添加操作”。那么我应该向图中添加什么操作以及如何添加？我不知道我的模型中的操作应该是什么......我不理解在 Tensorflow 中保存/恢复的整个概念。或者我应该完全不同地进行恢复？提前致谢！
回答by Lasse Jacobs
Forgive me if I am wrong but tf.train.Saver()only saves the variable values not the graph itself. This means that if you want to load the model in a different file you need to rebuild the graph or somehow load the graph as well. Tensorflow documentation states:
如果我错了，请原谅我，但tf.train.Saver()只保存变量值而不是图形本身。这意味着如果您想在不同的文件中加载模型，您需要重建图形或以某种方式加载图形。Tensorflow 文档指出：
The tf.train.Saver object not only saves variables to checkpoint files, it also restores variables. Note that when you restore variables from a file you do not have to initialize them beforehand.
tf.train.Saver 对象不仅将变量保存到检查点文件，还可以恢复变量。请注意，当您从文件中恢复变量时，您不必事先初始化它们。
Consider the following example:
考虑以下示例：
One file that saves the model:
保存模型的一个文件：
# Create some variables.
v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)
inc_v1 = v1.assign(v1+1)
dec_v2 = v2.assign(v2-1)
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, initialize the variables, do some work, and save the
# variables to disk.
with tf.Session() as sess:
sess.run(init_op)
# Do some work with the model.
inc_v1.op.run()
dec_v2.op.run()
# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")
print("Model saved in file: %s" % save_path)
The other file that loads the previously saved model:
加载之前保存的模型的另一个文件：
tf.reset_default_graph()
# Create some variables.
v1 = tf.get_variable("v1", shape=[3])
v2 = tf.get_variable("v2", shape=[5])
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print("Model restored.")
# Check the values of the variables
print("v1 : %s" % v1.eval())
print("v2 : %s" % v2.eval())
回答by CAta.RAy
output = sess.run(nn_output, feed_dict={ x: batch_x, keep_prob: 0.8 })
Where nn_outputis the name is the output variable of the last layer of you network. You can save you variable using:
哪里nn_output的名字是你网络最后一层的输出变量。您可以使用以下方法保存变量：
saver = tf.train.Saver([nn_output])
saver.save(sess, 'my_test_model',global_step=1000) # save every 1000 steps
and therefore in your code:
因此在您的代码中：
out_layer = tf.layers.dense(inputs=layer_1, units=6)
should be :
应该 ：
out_layer = tf.layers.dense(inputs=layer_1, units=6, name='nn_output')
To restore:
恢复：
with tf.Session() as sess:
saver = tf.train.import_meta_graph('my_test_model')
saver.restore(sess,tf.train.latest_checkpoint('./'))
Now you should have access to that node of the graph. If the name is not specified, it is difficult to recover that particular layer.
现在您应该可以访问图形的该节点。如果未指定名称，则很难恢复该特定层。
回答by Isma?la
You can know use tf.saved_model.builder.SavedModelBuilderfunction.
你可以知道使用tf.saved_model.builder.SavedModelBuilder函数。
The main lines for the saving:
保存的主要线路：
builder = tf.saved_model.builder.SavedModelBuilder(graph_location)
builder.add_meta_graph_and_variables(sess, ["cnn_mnist"])
builder.save()
A code to save the model :
保存模型的代码：
...
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.int64, [None])
# Build the graph for the deep net
y_conv, keep_prob = deepnn(x) # an unknow model model
with tf.name_scope('loss'):
cross_entropy = tf.losses.sparse_softmax_cross_entropy(
labels=y_, logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location ="tmp/"
print('Saving graph to: %s' % graph_location)
**builder = tf.saved_model.builder.SavedModelBuilder(graph_location)**
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
saver = tf.train.Saver(max_to_keep=1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
**builder.add_meta_graph_and_variables(sess, ["cnn_mnist"])**
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0})
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
**builder.save()**
saver.save(sess, "tmp/my_checkpoint.ckpt", global_step =0)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
`
A code to restore the model :
恢复模型的代码：
import tensorflow as tf
# récupération des poids
export_dir = "tmp"
sess = tf.Session()
tf.saved_model.loader.load(sess,["cnn_mnist"], export_dir)
#trainable_var = tf.trainable_variables()
trainable_var = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
for var in trainable_var:
print(var.name)`
回答by zardosht
This question is old. But if someone else is struggling with doing predictions with a trained model (with TF 1.x) this code might help.
这个问题很老了。但是，如果其他人正在努力使用经过训练的模型（使用 TF 1.x）进行预测，此代码可能会有所帮助。
Pay attention that
请注意
Your network construction/defining code must be executed before the
Saver()instance is created. Otherwise you get the error:ValueError: No variables to save. In the code below theLeNet(x)method constructs the network for input placeholderx.You should notinitialize the variables in the session.Because obviously you are loading them from the saved model.
您的网络构建/定义代码必须在
Saver()创建实例之前执行。否则你会得到错误：ValueError: No variables to save. 在下面的代码中，该LeNet(x)方法为输入占位符构建网络x。你应该不初始化会话变量。因为显然您是从保存的模型中加载它们。
# all the network construction code
# (e.g. defining the variables and layers)
# must be exectured before the creation of
# the Saver() object. Otherwise you get the
# error: ValueError: No variables to save.
logits = LeNet(x)
saver = tf.train.Saver()
index = random.randint(0, len(X_train))
image = X_train[index].squeeze()
label = y_train[index]
print("Label: ", label)
plt.figure(figsize=(1,1))
plt.imshow(image, cmap="gray")
plt.show()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('./checkpoints/'))
logits_output = sess.run(logits, feed_dict={x: image.reshape((1, 32, 32, 1))})
logits_output = logits_output.squeeze()
pred_output = np.exp(logits_output)/sum(np.exp(logits_output)) #softmax
print("Logits: ", logits_output)
print("Prediction output:", pred_output)
print("Predicted Label: ", np.argmax(pred_output))
