python - Bagaimana untuk menggunakan TFRecord dalam aliran tensor?

Question

1. Bagaimana untuk menggantikan set data mnist dalam kod berikut dengan TFRecord

2. Andaikan set data TFRecord telah disediakan, train.tfrecords 和 test.tfrecords semuanya dalam direktori py semasa

3. Sudah ada kod bacaan TFRecord.

def read_and_decode(filename):
    filename_queue = tf.train.string_input_producer([filename])
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(serialized_example,
                                       features={
                                           'label': tf.FixedLenFeature([], tf.int64),
                                           'img_raw': tf.FixedLenFeature([], tf.string),
                                       })
    img = tf.decode_raw(features['img_raw'], tf.uint8)
    img = tf.reshape(img, [512, 288, 3])
    img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(features['label'], tf.int32)
    return img, label

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets("/tmp/tensorflow/mnist/input_data", one_hot=True)

# Parameters
learning_rate = 0.001
training_iters = 200000
batch_size = 64
display_step = 20

# Network Parameters
n_input = 784  # MNIST data input (img shape: 28*28)
n_classes = 10  # MNIST total classes (0-9 digits)
dropout = 0.75  # Dropout, probability to keep units

# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)  # dropout (keep probability)


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


# Create custom model
def conv2d(name, l_input, w, b):
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'), b), name=name)


def max_pool(name, l_input, k):
    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)


def norm(name, l_input, lsize=4):
    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)


def dnn(_x, _weights, _biases, _dropout):
    _x = tf.nn.dropout(_x, _dropout)
    d1 = tf.nn.relu(tf.nn.bias_add(tf.matmul(_x, _weights['wd1']), _biases['bd1']), name="d1")

    d2x = tf.nn.dropout(d1, _dropout)
    d2 = tf.nn.relu(tf.nn.bias_add(tf.matmul(d2x, _weights['wd2']), _biases['bd2']), name="d2")

    dout = tf.nn.dropout(d2, _dropout)
    out = tf.matmul(dout, _weights['out']) + _biases['out']
    return out


# Store layers weight & bias
weights = {
    'wd1': tf.Variable(tf.random_normal([784, 600], stddev=0.01)),
    'wd2': tf.Variable(tf.random_normal([600, 480], stddev=0.01)),
    'out': tf.Variable(tf.random_normal([480, 10]))
}

biases = {
    'bd1': tf.Variable(tf.random_normal([600])),
    'bd2': tf.Variable(tf.random_normal([480])),
    'out': tf.Variable(tf.random_normal([10]))
}

# Construct model
pred = dnn(x, weights, biases, keep_prob)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.global_variables_initializer()

#
tf.summary.scalar("loss", cost)
tf.summary.scalar("accuracy", accuracy)
# Merge all summaries to a single operator
merged_summary_op = tf.summary.merge_all()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    summary_writer = tf.summary.FileWriter('/tmp/logs/ex12_dnn', graph=sess.graph)
    step = 1
    # Keep training until reach max iterations
    while step * batch_size < training_iters:
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # Fit training using batch data
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
        if step % display_step == 0:
            # Calculate batch accuracy
            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            # Calculate batch loss
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))
            summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
            summary_writer.add_summary(summary_str, step)
        step += 1
    print("Optimization Finished!")
    # Calculate accuracy for 256 mnist test images
    print("Testing Accuracy:",
          sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))
    # 98%

Saya tidak tahu cara menggunakannya secara khusus, tetapi selepas menukarnya beberapa kali, saya masih mendapat ralat

Ralat adalah serupa

ValueError: Only call `softmax_cross_entropy_with_logits` with named arguments (labels=..., logits=..., ...)

Answer 1

Saya tidak tahu sama ada saya faham maksud anda Apa yang dibaca mnist = input_data.read_data_sets("/tmp/tensorflow/mnist/input_data", one_hot=True) ini ialah data mnist, kemudian gunakan kod bacaan TFRecord untuk membaca data TFRecord, dan ganti mnist dalam kod untuk melatih rangkaian di bawah. keluar, dan pastikan bahawa parameter operasi lilitan yang anda gunakan sepadan dengan data TFRecord.