Tensorflowオートコードコードの明確化とカスタムテストデータ

Question

Tensorflow入力キューに関して完全に理解できないことについて質問したいと思います。私はTensorflowモジュールを作成しました。このモジュールは、以下のようにデータバッチを作成します。

このコード：

# various initialization variables 
BATCH_SIZE = 128 
N_FEATURES = 9 

def batch_generator(filenames, record_bytes): 
    """ filenames is the list of files you want to read from. 
    In this case, it contains only heart.csv 
    """ 

    record_bytes = 29**2 # 29x29 images per record 
    filename_queue = tf.train.string_input_producer(filenames) 
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) # skip the first line in the file 
    _, value = reader.read(filename_queue) 
    print(value) 



    # read in the 10 columns of data 
    content = tf.decode_raw(value, out_type=tf.uint8) 

    # The bytes read represent the image, which we reshape 
    # from [depth * height * width] to [depth, height, width]. 
    depth_major = tf.reshape(
    tf.strided_slice(content, [0], 
        [record_bytes]), 
    [1, 29, 29]) 

    # Convert from [depth, height, width] to [height, width, depth]. 
    uint8image = tf.transpose(depth_major, [1, 2, 0]) 
    uint8image = tf.reshape(uint8image, [29**2]) # reshape it a single- dimensional vector 
    uint8image = tf.cast(uint8image, tf.float32) 
    uint8image = tf.nn.l2_normalize(uint8image,dim=0) # normalize along vertical dimension 

    # minimum number elements in the queue after a dequeue, used to ensure 
    # that the samples are sufficiently mixed 
    # I think 10 times the BATCH_SIZE is sufficient 
    min_after_dequeue = 10 * BATCH_SIZE 

    # the maximum number of elements in the queue 
    capacity = 20 * BATCH_SIZE 

    # shuffle the data to generate BATCH_SIZE sample pairs 
    data_batch = tf.train.shuffle_batch([uint8image], batch_size=BATCH_SIZE, 
            capacity=capacity, min_after_dequeue=min_after_dequeue) 

    return data_batch 

私の質問は、私は正確に128私はこの関数を呼び出すたびに記録し得るのですか？など

batch_xs = sess.run(data_batch) 

1）ここでは、batch_xsの値はどのような値になりますか？

2）私が使用した例では、トレーニングの効率を評価するために、次のコードを使用：

encode_decode = sess.run(
    y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) 

どのように私は別のバイナリに保存されてきた私自​​身のテストデータを供給しに行きますかファイル？この質問は私の前の投稿に関連しています。Tensorflow Autoencoder with custom training examples from binary fileです。

Answer 1

上記課題を解決するために、私は以下に示すIが作成data_readerモジュール使用：次のように私は、新しいdata_batch_evalを作成

import tensorflow as tf 

# various initialization variables 
BATCH_SIZE = 128 
N_FEATURES = 9 

def batch_generator(filenames, record_bytes): 
    """ filenames is the list of files you want to read from. 
    In this case, it contains only heart.csv 
    """ 

    record_bytes = 29**2 # 29x29 images per record 
    filename_queue = tf.train.string_input_producer(filenames) 
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) # skip the first line in the file 
    _, value = reader.read(filename_queue) 
    print(value) 

    # record_defaults are the default values in case some of our columns are empty 
    # This is also to tell tensorflow the format of our data (the type of the decode result) 
    # for this dataset, out of 9 feature columns, 
    # 8 of them are floats (some are integers, but to make our features homogenous, 
    # we consider them floats), and 1 is string (at position 5) 
    # the last column corresponds to the lable is an integer 

    #record_defaults = [[1.0] for _ in range(N_FEATURES)] 
    #record_defaults[4] = [''] 
    #record_defaults.append([1]) 

    # read in the 10 columns of data 
    content = tf.decode_raw(value, out_type=tf.uint8) 
    #print(content) 

    # convert the 5th column (present/absent) to the binary value 0 and 1 
    #condition = tf.equal(content[4], tf.constant('Present')) 
    #content[4] = tf.where(condition, tf.constant(1.0), tf.constant(0.0)) 

    # pack all UINT8 values into a tensor 
    features = tf.stack(content) 
    #print(features) 

    # assign the last column to label 
    #label = content[-1] 

    # The bytes read represent the image, which we reshape 
    # from [depth * height * width] to [depth, height, width]. 
    depth_major = tf.reshape(
    tf.strided_slice(content, [0], 
        [record_bytes]), 
    [1, 29, 29]) 

    # Convert from [depth, height, width] to [height, width, depth]. 
    uint8image = tf.transpose(depth_major, [1, 2, 0]) 
    uint8image = tf.reshape(uint8image, [29**2]) # reshape it a single-dimensional vector 
    uint8image = tf.cast(uint8image, tf.float32) 
    uint8image = tf.nn.l2_normalize(uint8image,dim=0) # normalize along vertical dimension 

    # minimum number elements in the queue after a dequeue, used to ensure 
    # that the samples are sufficiently mixed 
    # I think 10 times the BATCH_SIZE is sufficient 
    min_after_dequeue = 10 * BATCH_SIZE 

    # the maximum number of elements in the queue 
    capacity = 20 * BATCH_SIZE 

    # shuffle the data to generate BATCH_SIZE sample pairs 
    data_batch = tf.train.shuffle_batch([uint8image], batch_size=BATCH_SIZE, 
            capacity=capacity, min_after_dequeue=min_after_dequeue) 

    return data_batch 

は：

data_batch_eval = data_reader.batch_generator([DATA_PATH_EVAL],29**2) # 

評価は

セット

これはテストコードです：

encode_decode = sess.run(
    y_pred, feed_dict={X: batch_ys[:examples_to_show]}) 
# Compare original images with their reconstructions 
f, a = plt.subplots(2, 10, figsize=(10, 2)) 
for i in range(examples_to_show): 
    #a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28))) 
    a[0][i].imshow(np.reshape(batch_ys[i], (29, 29)), cmap='gray') 
    a[1][i].imshow(np.reshape(encode_decode[i], (29, 29)), cmap='gray') 
f.show() 
plt.draw() 
plt.waitforbuttonpress() 

私の問題は、encode_decodeイメージがすべて同じイメージを指していると思うようになったことです。上記のように、これはオートエンコーダのトレーニングコードで何か問題が起こっている可能性がありますか？