cross_entropy = tf.reduce_mean( -tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) W_grad = tf.gradients(cross_entropy, [W])[0] train_step = tf.assign(W, W - 0.5 * W_grad) sess = tf.Session() sess.run(tf.global_variables_initializer()) # get the mnist dataset (use tensorflow here) from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # train for _ in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # eval correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) ans = sess.run(accuracy, feed_dict={ x: mnist.test.images, y_: mnist.test.labels }) print("Accuracy: %.3f" % ans) assert ans >= 0.87
# Training cycle for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples / batch_size) # Loop over all batches for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={ x: batch_x, y: batch_y }) #print _ # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", \ "{:.9f}".format(avg_cost)) print("Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) ans = accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) print("Accuracy: %.3f" % ans) assert ans >= 0.80