テンソルフローエスティメーターテンプレートベースのモデルの保存と復元

Question

テンソルフローのgithubからニューラルネットワークの簡単な例を取り上げ、2つの部分に分割しようとしました。最初の部分はトレーニング+テストで、2番目の部分は復元が必要なテスト部分を分離しています。リストアは機能しているように見えますが、予測機能は見つかりません。

from __future__ import print_function 

from tensorflow.python.saved_model import builder as saved_model_builder 

# Import MNIST data 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) 

import tensorflow as tf 
import matplotlib 
import matplotlib.pyplot as plt 
import numpy as np 
import shutil 

matplotlib.use('TkAgg') 

# Parameters 
learning_rate = 0.1 
num_steps = 1000 
batch_size = 128 
display_step = 100 

# Network Parameters 
n_hidden_1 = 256 # 1st layer number of neurons 
n_hidden_2 = 256 # 2nd layer number of neurons 
num_input = 784 # MNIST data input (img shape: 28*28) 
num_classes = 10 # MNIST total classes (0-9 digits) 

#init = tf.initialize_all_variables() 

sess = tf.Session() 

# Define the input function for training 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': mnist.train.images}, y=mnist.train.labels, 
    batch_size=batch_size, num_epochs=None, shuffle=True) 

# Define the neural network 
def neural_net(x_dict): 
    # TF Estimator input is a dict, in case of multiple inputs 
    x = x_dict['images'] 
    # Hidden fully connected layer with 256 neurons 
    layer_1 = tf.layers.dense(x, n_hidden_1, name="layer_1") 
    # Hidden fully connected layer with 256 neurons 
    layer_2 = tf.layers.dense(layer_1, n_hidden_2, name="layer_2") 
    # Output fully connected layer with a neuron for each class 
    out_layer = tf.layers.dense(layer_2, num_classes, name="out_layer") 
    return out_layer 

# Define the model function (following TF Estimator Template) 
def model_fn(features, labels, mode): 
    # Build the neural network 
    logits = neural_net(features) 

    # Predictions 
    pred_classes = tf.argmax(logits, axis=1) 
    pred_probas = tf.nn.softmax(logits) 

    # If prediction mode, early return 
    if mode == tf.estimator.ModeKeys.PREDICT: 
     return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) 

    # Define loss and optimizer 
    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
     logits=logits, labels=tf.cast(labels, dtype=tf.int32))) 
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) 
    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) 

    # Evaluate the accuracy of the model 
    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) 

    # TF Estimators requires to return a EstimatorSpec, that specify 
    # the different ops for training, evaluating, ... 
    estim_specs = tf.estimator.EstimatorSpec(
     mode=mode, 
     predictions=pred_classes, 
     loss=loss_op, 
     train_op=train_op, 
     eval_metric_ops={'accuracy': acc_op}) 

    return estim_specs 

# Build the Estimator 
model = tf.estimator.Estimator(model_fn) 

# Train the Model 
model.train(input_fn, steps=num_steps) 

# Evaluate the Model 
# Define the input function for evaluating 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': mnist.test.images}, y=mnist.test.labels, 
    batch_size=batch_size, shuffle=False) 
# Use the Estimator 'evaluate' method 
model.evaluate(input_fn) 

#model.export_savedmodel(".", input_fn) 

init = tf.global_variables_initializer() 
sess.run(init) 

tf.add_to_collection("nn_model", model) 

# Add ops to save and restore all the variables. 
#saver = tf.train.Saver() 

#save_path = saver.save(sess, "model/model.ckpt") 

try: 
    shutil.rmtree("model") 
except: 
    pass 

builder = saved_model_builder.SavedModelBuilder("model") 
builder.add_meta_graph_and_variables(sess, ["nn"]) 
builder.save() 

print("Model saved in file") 

# Predict single images 
n_images = 4 
# Get images from test set 
test_images = mnist.test.images[:n_images] 
# Prepare the input data 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': test_images}, shuffle=False) 
# Use the model to predict the images class 
preds = list(model.predict(input_fn)) 

# Display 
for i in range(n_images): 
    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') 
    plt.show() 
    print("Model prediction:", preds[i]) 

上記のプログラムが正常に動作します：

は、ここで最初の部分です。モデルを保存しますが、正しく作成されているとは限りません。

警告：tensorflow：nn_modelをシリアル化するときにエラーが発生しました。 タイプがサポートされていないか、アイテムのタイプがCollectionDefのフィールドタイプと一致しません。 「見積もり」オブジェクトには、属性「名前」ここ

を持っていない予測（）ラインで復元して適用しようとすると失敗し、「適用」プログラムです：

import tensorflow as tf 

# Import MNIST data 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) 

sess=tf.Session() 
#First let's load meta graph and restore weights 
#saver = tf.train.import_meta_graph('model/model.ckpt.meta') 
#saver.restore(sess,tf.train.latest_checkpoint('nn_model')) 
tf.saved_model.loader.load(sess, ["nn"], "model") 

model = tf.get_collection('nn_model') 

# Predict single images 
n_images = 4 
# Get images from test set 
test_images = mnist.test.images[:n_images] 
# Prepare the input data 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': test_images}, shuffle=False) 
# Use the model to predict the images class 
preds = list(model.predict(input_fn)) 

# Display 
for i in range(n_images): 
    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') 
    plt.show() 
    print("Model prediction:", preds[i]) 

は、それが与える誤差はあるが：

トレースバック（最新の呼び出しの最後）： ファイル "applynn.py"、ライン35は、 predsに=リスト（model.predict（input_fn）） はAttributeError： 'モジュール' オブジェクトを予測」は属性を持っていません'

ここには何が欠けていますか？

Answer 1

この問題は修正されました。ここで私はこれを解決するために何をしなければならなかったのですか？

最初の部分は次のとおりです。

from __future__ import print_function 

from tensorflow.python.saved_model import builder as saved_model_builder 

# Import MNIST data 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) 

import tensorflow as tf 
import matplotlib 
import matplotlib.pyplot as plt 
import numpy as np 
import shutil 

matplotlib.use('TkAgg') 

# Parameters 
learning_rate = 0.1 
num_steps = 1000 
batch_size = 128 
display_step = 100 

# Network Parameters 
n_hidden_1 = 256 # 1st layer number of neurons 
n_hidden_2 = 256 # 2nd layer number of neurons 
num_input = 784 # MNIST data input (img shape: 28*28) 
num_classes = 10 # MNIST total classes (0-9 digits) 

#init = tf.initialize_all_variables() 

sess = tf.Session() 

# Define the input function for training 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': mnist.train.images}, y=mnist.train.labels, 
    batch_size=batch_size, num_epochs=None, shuffle=True) 

# Define the neural network 
def neural_net(x_dict): 
    # TF Estimator input is a dict, in case of multiple inputs 
    x = x_dict['images'] 
    # Hidden fully connected layer with 256 neurons 
    layer_1 = tf.layers.dense(x, n_hidden_1, name="layer_1") 
    # Hidden fully connected layer with 256 neurons 
    layer_2 = tf.layers.dense(layer_1, n_hidden_2, name="layer_2") 
    # Output fully connected layer with a neuron for each class 
    out_layer = tf.layers.dense(layer_2, num_classes, name="out_layer") 
    return out_layer 

# Define the model function (following TF Estimator Template) 
def model_fn(features, labels, mode): 
    # Build the neural network 
    logits = neural_net(features) 

    # Predictions 
    pred_classes = tf.argmax(logits, axis=1) 
    pred_probas = tf.nn.softmax(logits) 

    # If prediction mode, early return 
    if mode == tf.estimator.ModeKeys.PREDICT: 
     return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) 

    # Define loss and optimizer 
    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
     logits=logits, labels=tf.cast(labels, dtype=tf.int32))) 
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) 
    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) 

    # Evaluate the accuracy of the model 
    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) 

    # TF Estimators requires to return a EstimatorSpec, that specify 
    # the different ops for training, evaluating, ... 
    estim_specs = tf.estimator.EstimatorSpec(
     mode=mode, 
     predictions=pred_classes, 
     loss=loss_op, 
     train_op=train_op, 
     eval_metric_ops={'accuracy': acc_op}) 

    return estim_specs 

# Build the Estimator 
estimator = tf.estimator.Estimator(model_fn, model_dir='estimator') 

# Train the Model 
estimator.train(input_fn, steps=num_steps) 

# Evaluate the Model 
# Define the input function for evaluating 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': mnist.test.images}, y=mnist.test.labels, 
    batch_size=batch_size, shuffle=False) 
# Use the Estimator 'evaluate' method 
estimator.evaluate(input_fn) 

#model.export_savedmodel(".", input_fn) 

init = tf.global_variables_initializer() 
sess.run(init) 

tf.add_to_collection("nn_model", estimator) 

# Add ops to save and restore all the variables. 
#saver = tf.train.Saver() 

#save_path = saver.save(sess, "model/model.ckpt") 

try: 
    shutil.rmtree("model") 
except: 
    pass 

builder = saved_model_builder.SavedModelBuilder("model") 
builder.add_meta_graph_and_variables(sess, ["nn"]) 
builder.save() 

print("Model saved in file") 

# Predict single images 
n_images = 4 
# Get images from test set 
test_images = mnist.test.images[:n_images] 
# Prepare the input data 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': test_images}, shuffle=False) 
# Use the model to predict the images class 
preds = list(estimator.predict(input_fn)) 

# Display 
for i in range(n_images): 
    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') 
    plt.show() 
    print("Model prediction:", preds[i]) 

第二の部分は次のとおりです。それは本当に推定量であるので、私は、推定量などのモデル変数と呼ばれている

import tensorflow as tf 
import matplotlib.pyplot as plt 
import numpy as np 

# Import MNIST data 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) 

# Network Parameters 
n_hidden_1 = 256 # 1st layer number of neurons 
n_hidden_2 = 256 # 2nd layer number of neurons 
num_classes = 10 # MNIST total classes (0-9 digits) 

# Define the neural network 
def neural_net(x_dict): 
    # TF Estimator input is a dict, in case of multiple inputs 
    x = x_dict['images'] 
    # Hidden fully connected layer with 256 neurons 
    layer_1 = tf.layers.dense(x, n_hidden_1, name="layer_1") 
    # Hidden fully connected layer with 256 neurons 
    layer_2 = tf.layers.dense(layer_1, n_hidden_2, name="layer_2") 
    # Output fully connected layer with a neuron for each class 
    out_layer = tf.layers.dense(layer_2, num_classes, name="out_layer") 
    return out_layer 

# Define the model function (following TF Estimator Template) 
def model_fn(features, labels, mode): 
    # Build the neural network 
    logits = neural_net(features) 

    # Predictions 
    pred_classes = tf.argmax(logits, axis=1) 
    pred_probas = tf.nn.softmax(logits) 

    # If prediction mode, early return 
    if mode == tf.estimator.ModeKeys.PREDICT: 
     return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) 

    # Define loss and optimizer 
    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
     logits=logits, labels=tf.cast(labels, dtype=tf.int32))) 
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) 
    train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step()) 

    # Evaluate the accuracy of the model 
    acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) 

    # TF Estimators requires to return a EstimatorSpec, that specify 
    # the different ops for training, evaluating, ... 
    estim_specs = tf.estimator.EstimatorSpec(
     mode=mode, 
     predictions=pred_classes, 
     loss=loss_op, 
     train_op=train_op, 
     eval_metric_ops={'accuracy': acc_op}) 

    return estim_specs 


sess=tf.Session() 

estimator = tf.estimator.Estimator(model_fn, model_dir='estimator') 

# Predict single images 
n_images = 4 
# Get images from test set 
test_images = mnist.test.images[:n_images] 
# Prepare the input data 
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': test_images}, shuffle=False) 
# Use the model to predict the images class 
preds = list(estimator.predict(input_fn)) 

# Display 
for i in range(n_images): 
    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') 
    plt.show() 
    print("Model prediction:", preds[i]) 

注意。また、私はmodel_dirを渡しているので、他の変数とは別に見積もりをシリアル化します。また、2番目のpythonファイルが2つの関数とそれに依存する変数にアクセスできることを明示的に確認しなければなりませんでした。このコードでは、いくつか微妙な修正が加えられました。