placeholder and feed

gistfile1.txt

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

# some reshaping here...

X = tf.placeholder(tf.float32, [None, 784], name="X")
Y_truth = tf.placeholder(tf.float32, [None, 10], name="Y")

# define model, loss, and optimizer
optimizer = tf.train.GradientDescentOptimizer(0.003).minimize(cross_entropy)

with tf.Session() as sess:
    
    for i in range(25000):
        batch_X, batch_Y = data_helper.next_batch(100)

        train_data = {X: batch_X, Y_truth: batch_Y}
        sess.run(optimizer, feed_dict=train_data)