如何使用经过训练的 Tensorflow 模型预测值

Question

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)

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})

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！

Answer 1

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：

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。

考虑以下示例：

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)

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())

Answer 2

 output = sess.run(nn_output, feed_dict={ x: batch_x, keep_prob: 0.8 })

其中nn_output是名称是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

因此在您的代码中：

out_layer = tf.layers.dense(inputs=layer_1, units=6)

应该是：

out_layer = tf.layers.dense(inputs=layer_1, units=6, name='nn_output')

要恢复：

with tf.Session() as sess:    
saver = tf.train.import_meta_graph('my_test_model')
saver.restore(sess,tf.train.latest_checkpoint('./'))

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。

Answer 3

你可以知道使用tf.saved_model.builder.SavedModelBuilder函数。

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()

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)
`

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)`

Answer 4

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。

请注意

1. 您的网络构建/定义代码必须在Saver()实例创建之前执行。 否则你会得到错误： ValueError: No variables to save 。 在下面的代码中， LeNet(x)方法为输入占位符x构造网络。

2. 你不应该初始化会话变量。 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))