python !pip install tensorflow python import tensorflow as tf python x = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32) y = tf.constant([2, 4, 6, 8, 10], dtype=tf.float32) w = tf.Variable(0.0) b = tf.Variable(0.0) y_pred = w * x + b loss = tf.reduce_mean(tf.square(y_pred - y)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) train_op = optimizer.minimize(loss) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(100): sess.run(train_op) final_w, final_b, final_loss = sess.run([w, b, loss]) print("Final weights:", final_w) print("Final bias:", final_b) print("Final loss:", final_loss) python import tensorflow as tf x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) hidden_layer = tf.layers.dense(x, 256, activation=tf.nn.relu) output_layer = tf.layers.dense(hidden_layer, 10) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=output_layer)) optimizer = tf.train.AdamOptimizer(0.001).minimize(loss) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) correct_prediction = tf.equal(tf.argmax(output_layer, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) test_accuracy = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Test Accuracy:", test_accuracy)