Task2 SGD

这次的作业是把logistics regression改成一个简单的神经网络
激活函数是一个ReLU函数，实现非线性化，之后输出的值传递给普通的logistics regression的输入即可

batch_size = 128graph = tf.Graph()with graph.as_default():  # Input data. For the training data, we use a placeholder that will be fed  # at run time with a training minibatch.  tf_train_dataset = tf.placeholder(tf.float32,                                    shape=(batch_size, image_size * image_size))  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))  tf_valid_dataset = tf.constant(valid_dataset)  tf_test_dataset = tf.constant(test_dataset)  # Variables.  weights1 = tf.Variable(    tf.truncated_normal([image_size * image_size, 1024]))  biases1 = tf.Variable(tf.zeros([1024]))  weights2 = tf.Variable(    tf.truncated_normal([1024, num_labels]))  biases2 = tf.Variable(tf.zeros([num_labels]))     # Training computation.  #input to hidden layer  z1 = tf.matmul(tf_train_dataset, weights1) + biases1  h1 = tf.nn.relu(z1)  #hidden layer to output  logits = tf.matmul(h1, weights2) + biases2  loss = tf.reduce_mean(    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))  # Optimizer.  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)  # Predictions for the training, validation, and test data.  train_prediction = tf.nn.softmax(logits)  valid_z1 = tf.matmul(tf_valid_dataset, weights1) + biases1  valid_h1 = tf.nn.relu(valid_z1)  #hidden layer to output  valid_logits = tf.matmul(valid_h1, weights2) + biases2  valid_prediction = tf.nn.softmax(valid_logits)  test_z1 = tf.matmul(tf_test_dataset, weights1) + biases1  test_h1 = tf.nn.relu(test_z1)  #hidden layer to output  test_logits = tf.matmul(test_h1, weights2) + biases2  test_prediction = tf.nn.softmax(test_logits)

训练

num_steps = 3001with tf.Session(graph=graph) as session:  tf.global_variables_initializer().run()  print("Initialized")  for step in range(num_steps):    # Pick an offset within the training data, which has been randomized.    # Note: we could use better randomization across epochs.    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)    # Generate a minibatch.    batch_data = train_dataset[offset:(offset + batch_size), :]    batch_labels = train_labels[offset:(offset + batch_size), :]    # Prepare a dictionary telling the session where to feed the minibatch.    # The key of the dictionary is the placeholder node of the graph to be fed,    # and the value is the numpy array to feed to it.    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}    _, l, predictions = session.run(      [optimizer, loss, train_prediction], feed_dict=feed_dict)    if (step % 500 == 0):      print("Minibatch loss at step %d: %f" % (step, l))      print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))      print("Validation accuracy: %.1f%%" % accuracy(        valid_prediction.eval(), valid_labels))  print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

结果