1. How to replace the mnist data set in the following code with TFRecord

2. Assume that the TFRecord data set has been prepared, train.tfrecords and test.tfrecords are both in the current py directory

3. There is already a TFRecord reading code.

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%

I don’t know how to use it, but I got an error after changing it several times.

Error is similar

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