python import tensorflow as tf x = tf.placeholder(tf.float32, shape=[None, 784]) W_hidden = tf.Variable(tf.random_normal([784, 256])) b_hidden = tf.Variable(tf.zeros([256])) hidden_layer = tf.nn.relu(tf.matmul(x, W_hidden) + b_hidden) W_output = tf.Variable(tf.random_normal([256, 10])) b_output = tf.Variable(tf.zeros([10])) output_layer = tf.matmul(hidden_layer, W_output) + b_output y_true = tf.placeholder(tf.float32, shape=[None, 10]) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output_layer, labels=y_true)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for epoch in range(1000): sess.run(optimizer, feed_dict={x: batch_x, y_true: batch_y}) accuracy = sess.run(accuracy_metrice, feed_dict={x: test_x, y_true: test_y})