Udacity Deep Learning课程作业（二）

第二次作业比较简单，实现一个隐含层1024个节点的三层神经网络，选用ReLU作为激活函数，实现作业一中的字母图片分类。实现代码如下，主要是实现computation(dataset, weights, biases)函数，返回logits取代原来的线性回归模型。

hidden_nodes = 1024batch_size = 128def computation(dataset, weights, biases):    weight_sum = tf.add(tf.matmul(dataset, weights[0]), biases[0])    hidden_layer = tf.nn.relu(weight_sum)    logits = tf.add(tf.matmul(hidden_layer, weights[1]), biases[1])    return logitsgraph = 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 = [tf.Variable(tf.truncated_normal([image_size * image_size, hidden_nodes])),              tf.Variable(tf.truncated_normal([hidden_nodes, num_labels]))            ]    biases = [tf.Variable(tf.zeros([hidden_nodes])),            tf.Variable(tf.zeros([num_labels]))]    # Training computation.    logits = computation(tf_train_dataset, weights, biases)    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_prediction = tf.nn.softmax(computation(tf_valid_dataset, weights, biases))    test_prediction = tf.nn.softmax(computation(tf_test_dataset, weights, biases))

def accuracy(predictions, labels):  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))          / predictions.shape[0])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))

结果如下：

InitializedMinibatch loss at step 0: 333.015137Minibatch accuracy: 11.7%Validation accuracy: 42.1%Minibatch loss at step 500: 17.960817Minibatch accuracy: 85.2%Validation accuracy: 77.0%Minibatch loss at step 1000: 8.218993Minibatch accuracy: 82.0%Validation accuracy: 81.0%Minibatch loss at step 1500: 18.049088Minibatch accuracy: 71.9%Validation accuracy: 79.2%Minibatch loss at step 2000: 3.682401Minibatch accuracy: 84.4%Validation accuracy: 81.5%Minibatch loss at step 2500: 6.891292Minibatch accuracy: 82.8%Validation accuracy: 81.2%Minibatch loss at step 3000: 4.673051Minibatch accuracy: 84.4%Validation accuracy: 81.8%Test accuracy: 89.7%

此外，官方代码教程ipynb里有些代码值得学习一下（基于numpy和TensorFlow的操作）：

• 对numpy的label变量进行OneHot编码

# num_labels是label数目，labels是label具体列表labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32) # one-hot encoding

• 计算准确率

def accuracy(predictions, labels):  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))          / predictions.shape[0])

• TensorFlow使用placeholder保存训练数据，每次epoch中加载更新；用constant保存验证数据；用随机分布初始化权值

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 = tf.Variable(    tf.truncated_normal([image_size * image_size, num_labels]))biases = tf.Variable(tf.zeros([num_labels]))

• TensorFlow定义计算图，并调用session.run()执行计算图

graph = tf.Graph()with graph.as_default():    ...with tf.Session(graph=graph) as session:    tf.global_variables_initializer().run()    ...

• TensorFlow实现在每次epoch中基于offset实现minibatch训练

# for each epoch     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), :]    # 用一个字典记录batch_data，key值是graph的placeholder节点（即训练数据），value是label数组    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}    _, l, predictions = session.run(      [optimizer, loss, train_prediction], feed_dict=feed_dict)