Udacity深度学习-深度神经网络-assignment3

六层深度神经网络+SGD+L2正则项+dropout，TensorFlow实现

#NN with SGD, L2batch_size = 128layer_cnt = 6#层数graph = 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.  weights = []  biases = []  hidden_cur_cnt = 784  for i in range(layer_cnt - 2):     if hidden_cur_cnt > 2:         hidden_next_cnt = int(hidden_cur_cnt / 2)     else:         hidden_next_cnt = 2     hidden_stddev = np.sqrt(2.0 / hidden_cur_cnt)     weights.append(tf.Variable(tf.truncated_normal([hidden_cur_cnt, hidden_next_cnt], stddev=hidden_stddev)))     biases.append(tf.Variable(tf.zeros([hidden_next_cnt])))     hidden_cur_cnt = hidden_next_cnt  weights.append(tf.Variable(tf.truncated_normal([hidden_cur_cnt, num_labels], stddev=hidden_stddev)))  biases.append(tf.Variable(tf.zeros([num_labels])))    # Training computation.  hidden_drop = tf_train_dataset  keep_prob = 0.5  for i in range(layer_cnt - 2):     y1 = tf.matmul(hidden_drop, weights[i]) + biases[i]     hidden_drop = tf.nn.relu(y1)     keep_prob += 0.5 * i / (layer_cnt + 1)     hidden_drop = tf.nn.dropout(hidden_drop, keep_prob)    z3 = tf.matmul(hidden_drop, weights[-1]) + biases[-1]  l2_loss = tf.Variable(0.0)  for wi in weights:        l2_loss += tf.nn.l2_loss(wi)  loss = tf.reduce_mean(    tf.add(        tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=z3),0.001 *  l2_loss) )    # Optimizer.  global_step = tf.Variable(0)  # count the number of steps taken.  learning_rate = tf.train.exponential_decay(0.5, global_step, 1000, 0.9)  optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)    # Predictions for the training, validation, and test data.  train_prediction = tf.nn.softmax(z3)  def predict(dataset):      hidden_drop = dataset      for i in range(layer_cnt - 2):         y1 = tf.matmul(hidden_drop, weights[i]) + biases[i]         hidden_drop = tf.nn.relu(y1)        result = tf.matmul(hidden_drop, weights[-1]) + biases[-1]            return result    valid_prediction = tf.nn.softmax(predict(tf_valid_dataset))    test_prediction = tf.nn.softmax(predict(tf_test_dataset))

运行代码：

num_steps = 20001with 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))