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這裏是我的Tensorflow MNIST例子的修改版本:在訓練模型Tensorflow MNIST分類

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 
from tensorflow.contrib.learn.python.learn.datasets.mnist import DataSet 

import tensorflow as tf 
import numpy as np 

FLAGS = None 


def deepnn(x, numclasses): 
    """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, numclasses]) 
    b_fc2 = bias_variable([numclasses]) 

    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) 


def main(_): 
    # Import data 
    images = np.load("../rwclassi/db/images.npy") 
    labels = np.load("../rwclassi/db/labels.npy") 

    train = DataSet(images, labels, reshape=True) 
    numpixels = images.shape[1] * images.shape[2] * images.shape[3] 
    numclasses = labels.shape[1] 
    #test = train 
    #mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) 
    # Create the model 
    x = tf.placeholder(tf.float32, [None, numpixels]) 

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

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

    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()) 
    saver = tf.train.Saver() 
    resume = True 
    with tf.Session() as sess: 
    if resume: 
     saver.restore(sess, "model.ckpt") 
     print("Model restored.") 
    else: 
     sess.run(tf.global_variables_initializer()) 
    for i in range(20000): 
     batch = train.next_batch(100) 
     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)) 
     if i % 1000 == 0: 
      saver.save(sess,"model.ckpt") 
      print ("Model saved") 
     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})) 

if __name__ == '__main__': 
    parser = argparse.ArgumentParser() 
    parser.add_argument('--modelfile', type=str, 
         default='model.ckpt', 
         help='Model file') 
    FLAGS, unparsed = parser.parse_known_args() 
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) 

如何預測/使用該訓練模型進行分類? sess.run(???)? argmax?

回答

0

自己想出來。

answer = sess.run(y_conv, feed_dict={x: [train.images[5230]], keep_prob: 1.0}) 
print (answer) 

y_conv, keep_prob = deepnn(x, numclasses) 

得到的網狀結構,其中y_conv是輸出和keep_prob是差的概率的標量佔位符。