如何根據訓練過的Tensorflow模型進行預測？

Question

我檢查了與我的問題有關的Stackoverflow的很多問題，但我仍然有問題。

我從Deep MNIST for Experts以下使用教程並使用mnist_deep.py的代碼，並且我使用tf.saved_model.builder.SavedModelBuilder()將模型保存到磁盤。

而在我的predict.py中，我使用tf.saved_model.loader.load()加載模型，在加載模型後，基於我在Google上搜索的很多搜索結果，我知道我必須運行sess.run(y_, feed_dict={x: test_data})來做出預測，而我對於變量y也知道，它應該是最後一層，對於feed_dict中的'x'，它應該是訓練中輸入的佔位符。

我的問題是，我不知道哪個代碼屬於mnist_deep.py的最後一層。

我mnist_deep.py代碼如下：

from __future__ import absolute_import 
from __future__ import division 
from __future__ import print_function 

import argparse 
import sys 
import tempfile 

from tensorflow.examples.tutorials.mnist import input_data 

import tensorflow as tf 

FLAGS = None 


def deepnn(x): 
    """deepnn builds the graph for a deep net for classifying digits. 
    Args: 
    x: an input tensor with the dimensions (N_examples, 784), where 784 is the 
    number of pixels in a standard MNIST image. 
    Returns: 
    A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values 
    equal to the logits of classifying the digit into one of 10 classes (the 
    digits 0-9). keep_prob is a scalar placeholder for the probability of 
    dropout. 
    """ 
    # Reshape to use within a convolutional neural net. 
    # Last dimension is for "features" - there is only one here, since images are 
    # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. 
    with tf.name_scope('reshape'): 
    x_image = tf.reshape(x, [-1, 28, 28, 1]) 

    # First convolutional layer - maps one grayscale image to 32 feature maps. 
    with tf.name_scope('conv1'): 
    W_conv1 = weight_variable([5, 5, 1, 32]) 
    b_conv1 = bias_variable([32]) 
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 

    # Pooling layer - downsamples by 2X. 
    with tf.name_scope('pool1'): 
    h_pool1 = max_pool_2x2(h_conv1) 

    # Second convolutional layer -- maps 32 feature maps to 64. 
    with tf.name_scope('conv2'): 
    W_conv2 = weight_variable([5, 5, 32, 64]) 
    b_conv2 = bias_variable([64]) 
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 

    # Second pooling layer. 
    with tf.name_scope('pool2'): 
    h_pool2 = max_pool_2x2(h_conv2) 

    # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image 
    # is down to 7x7x64 feature maps -- maps this to 1024 features. 
    with tf.name_scope('fc1'): 
    W_fc1 = weight_variable([7 * 7 * 64, 1024]) 
    b_fc1 = bias_variable([1024]) 

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) 
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 

    # Dropout - controls the complexity of the model, prevents co-adaptation of 
    # features. 
    with tf.name_scope('dropout'): 
    keep_prob = tf.placeholder(tf.float32) 
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

    # Map the 1024 features to 10 classes, one for each digit 
    with tf.name_scope('fc2'): 
    W_fc2 = weight_variable([1024, 10]) 
    b_fc2 = bias_variable([10]) 

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 
    return y_conv, keep_prob 


def conv2d(x, W): 
    """conv2d returns a 2d convolution layer with full stride.""" 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 


def max_pool_2x2(x): 
    """max_pool_2x2 downsamples a feature map by 2X.""" 
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], 
         strides=[1, 2, 2, 1], padding='SAME') 


def weight_variable(shape): 
    """weight_variable generates a weight variable of a given shape.""" 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 


def bias_variable(shape): 
    """bias_variable generates a bias variable of a given shape.""" 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial) 



# Import data 
mnist = input_data.read_data_sets("./MNIST_data", one_hot=True) 

# Create the model 
x = tf.placeholder(tf.float32, [None, 784]) 

# Define loss and optimizer 
y_ = tf.placeholder(tf.float32, [None, 10]) 

# Build the graph for the deep net 
y_conv, keep_prob = deepnn(x) 

with tf.name_scope('loss'): 
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(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), tf.argmax(y_, 1)) 
    correct_prediction = tf.cast(correct_prediction, tf.float32) 
accuracy = tf.reduce_mean(correct_prediction) 

graph_location = tempfile.mkdtemp() 
print('Saving graph to: %s' % graph_location) 
train_writer = tf.summary.FileWriter(graph_location) 
train_writer.add_graph(tf.get_default_graph()) 

builder = tf.saved_model.builder.SavedModelBuilder("./model") 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    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}) 
    builder.add_meta_graph_and_variables(sess,"CNN4mnist") 
    print('test accuracy %g' % accuracy.eval(feed_dict={ 
     x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) 

builder.save() 

這是我的predict.py：

import tensorflow as tf 
import pandas as pd 
import numpy as np 

PATH_TEST = "../data/test.csv" 

# load test data 
print('>>>loading test data...') 
test_data=pd.read_csv(PATH_TEST) 
test_data /= 255 
mean = np.mean(test_data) 
test_data -= mean 
test_data = np.asarray([ x.reshape(28,28,1) for x in test_data.as_matrix() ]) 
print(len(test_data)) 

results = None 
with tf.Session() as sess: 
    tf.saved_model.loader.load(sess,"CNN4mnist", "./model") 
    W = tf.Variable(tf.zeros([784,10])) 
    b = tf.Variable(tf.zeros([10])) 
    x = tf.placeholder(tf.float32, [None, 28,28,1]) 
    y = tf.nn.softmax(tf.matmul(x,W) + b) 
    results = sess.run(y_, feed_dict={x: test_data}) 

print(results) 
print(">>>saving results...") 
df = pd.DataFrame({'Label':results}) 
df.index += 1 
df.index.name='ImageId' 
df.to_csv('results.csv') 

Answer 1

你不跑y_，它的佔位符。

您也不會在加載模型後重新定義變量，而是使用保存的變量。

因此，加載後，只需運行sess.run(y_conv, feed_dict={x: test_data})

y_conv是模型中的最後一層預測的輸出。

要訪問y_conv，加載模型後，通過獲得它：
y_conv = sess.graph.get_tensor_by_name("it's name goes here")
你需要在保存前命名y_conv。
tf.add_to_collection('vars', y_conv)
y_conv = tf.get_collection('vars')[0]