TensorFlow FixedLengthRecordReader Fehler beim Platzhaltertyp

Question

Ich versuche ein CNN mit TensorFlow mit dem FixedLengthRecordReader zu erstellen.

import tensorflow as tf 
import numpy as np 

BATCH_SIZE = 100 
IMAGE_SIZE = 30 
IMAGE_DEPTH = 3 
image_data_len = IMAGE_SIZE * IMAGE_SIZE * 3 

class Record(object): 
    pass 

def init_weights(shape): 
    return tf.Variable(tf.random_normal(shape, stddev=0.01)) 

def generate_batch(image, label, min_queue_examples, batch_size): 
    num_preprocess_threads = 16 
    images, label_batch = tf.train.shuffle_batch(
     [image, label], 
     batch_size=batch_size, 
     num_threads=num_preprocess_threads, 
     capacity=min_queue_examples + 3 * batch_size, 
     min_after_dequeue=min_queue_examples) 
    return images, tf.reshape(label_batch, [batch_size]) 

def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden): 
    l1a = tf.nn.relu(tf.nn.conv2d(X, w, strides=[1, 1, 1, 1], padding='SAME')) 
    l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 
    l1 = tf.nn.dropout(l1, p_keep_conv) 

    l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, strides=[1, 1, 1, 1], padding='SAME')) 
    l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 
    l2 = tf.nn.dropout(l2, p_keep_conv) 

    l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, strides=[1, 1, 1, 1], padding='SAME')) 
    l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 
    l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) 
    l3 = tf.nn.dropout(l3, p_keep_conv) 

    l4 = tf.nn.relu(tf.matmul(l3, w4)) 
    l4 = tf.nn.dropout(l4, p_keep_hidden) 

    pyx = tf.matmul(l4, w_o) 
    return pyx 

reader = tf.FixedLengthRecordReader(record_bytes=(1 + image_data_len)) 
filenames = ['train.bin'] 
filename_queue = tf.train.string_input_producer(filenames) 
result = Record() 
result.key, value = reader.read(filename_queue) 
record_bytes = tf.decode_raw(value, tf.uint8) 
result.label = tf.cast(tf.slice(record_bytes, [0], [1]), tf.int32) 
depth_major = tf.reshape(tf.slice(record_bytes, [1], [image_data_len]), [IMAGE_DEPTH, IMAGE_SIZE, IMAGE_SIZE]) 
result.uint8image = tf.transpose(depth_major, [1, 2, 0]) 
float_image = tf.image.per_image_standardization(tf.cast(result.uint8image, tf.float32)) 

min_fraction_of_examples_in_queue = 0.4 
min_queue_examples = int(10000 * min_fraction_of_examples_in_queue) 
batch_images, batch_labels = generate_batch(float_image, result.label, min_queue_examples, BATCH_SIZE) 

X = tf.placeholder("float", [None, IMAGE_SIZE, IMAGE_SIZE, IMAGE_DEPTH]) 
Y = tf.placeholder("float", [None, 1]) 

w = init_weights([3, 3, IMAGE_DEPTH, 32]) 
w2 = init_weights([3, 3, 32, 64])  
w3 = init_weights([3, 3, 64, 128]) 
w4 = init_weights([128 * 4 * 4, 625]) 
w_o = init_weights([625, 1])   

p_keep_conv = tf.placeholder("float") 
p_keep_hidden = tf.placeholder("float") 
py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden) 

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) 
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost) 
predict_op = tf.argmax(py_x, 1) 

init = tf.global_variables_initializer() 

sess = tf.Session() 
sess.run(init) 
tf.train.start_queue_runners(sess=sess) 
for step in range(1000): 
    _, loss_value = sess.run([train_op, cost]) 

In der letzten Zeile bekomme ich folgendes:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float 
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

Irgendwelche Ideen?

Answer 1

Sie benötigen [?, IMAGE_SIZE, IMAGE_SIZE, IMAGE_DEPTH] ein Bild (Stapel von Bildern) von Dimensionen passieren und ein Etikett (Charge von Etikett) der Dimension [?, 1] zu Ihrem sess.run() Ruf als feed_dict.

Da Sie einen Stapel von Bildern mit dem Namen batch_images und eine Charge von Etikett batch_labels Ihre letzten Zeilen sollten genannt sein:

images, labels = sess.run([batch_images, batch_labels]) 
_, loss_value = sess.run([train_op, cost], feed_dict={X: images, Y: labels}) 

Answer 2

Sie definiert hat tf.placeholder()X und Y aber Sie keinen Wert auf diese Platzhalter Bereitstellung bei Verwendung von sess.run(). Gehen Sie wie folgt:

for step in range(1000): 
    img, lbl = sess.run([batch_images, batch_labels]) 
    _, loss_value = sess.run([train_op, cost],feed_dict={X: batch_images,\ 
          Y: batch_labels})