如何將基於Feed的TensorFlow基本代碼轉換爲使用「數據集」？

Question

我understand that there are advantages（特別是當我擴大我建立的模型範圍和他們的工作數據集的大小）到使用TensorFlow的新Dataset作爲我的數據饋送管道的習慣用法。但是，我無法將現有的基於feed_dict的代碼映射到此新模型。

我面臨的一個問題是，我無法理清批處理和時代如何交互，或者如何與我經常做的日誌記錄和驗證交互。

例如，下圖中的內容如何使用Dataset？如果需要的話

# Load and process data into tensors of dimension (N, C_i) for input and (N, C_o) for output 
# where N is the number of examples and C_ is the number of chanels, and the values are activations 
train_x, train_y, valid_x, valid_y = load_data(file, [segments], ...) 
train_size = len(train_x) 

train_stats_feed = {input_activation: train_x, correct_output: train_y, is_train: False} 
valid_stats_feed = {input_activation: valid_x, correct_output: valid_y, is_train: False} 

with tf.Session(config=tf.ConfigProto(...)) as sess: 
    sess.run(tf.initialize_all_variables()) 

    # Some analysis; not always done but the code needs to support it 
    train_writer.add_summary(sess.run(merged, feed_dict=train_stats_feed), 0) 
    test_writer.add_summary(sess.run(merged, feed_dict=valid_stats_feed), 0) 

    test_writer.add_summary(sess.run(gs_summary), 0) 

    print(log_fmt.format(0, float(sess.run(accuracy, feed_dict=valid_stats_feed)), 
         float(sess.run(loss, feed_dict=valid_stats_feed)))) 

    for ep in range(epochs): 
     # Slice the training data into random batches 
     batch_indices = np.array_split(np.random.permutation(train_size), int(train_size/mb_size)) 

     for mini_batch_indices in batch_indices: 
      sess.run(train_step, feed_dict={input_activation: train_x[mini_batch_indices], 
              correct_output: train_y[mini_batch_indices], is_train: True}) 

      gs = int(sess.run(global_step)) 
      if gs % log_steps == 0: 
       test_writer.add_summary(sess.run(merged, feed_dict=valid_stats_feed), gs) 
       train_writer.add_summary(sess.run(merged, feed_dict=train_stats_feed), gs) 

       acc = float(sess.run(accuracy, feed_dict=valid_stats_feed)) 
       sess.run(validation_accuracy.assign(acc)) 

       print(log_fmt.format(gs, acc, float(sess.run(loss, feed_dict=valid_stats_feed)))) 

     print(ep_fmt.format(ep + 2)) 
     test_writer.add_summary(sess.run(gs_summary), ep + 1) 

---

一些針對上述不太明顯的定義，：

# Preliminaries 

# Some basic preliminaries, the details of which are not important to the question 
# Mostly pretty standard; obvious things omitted from MWE for brevity 
global_step = tf.Variable(0, trainable=False, name='global_step') 
validation_accuracy = tf.Variable(0.0, trainable=False, name='validation_accuracy', dtype=tf.float32) 

is_train = tf.placeholder(tf.bool, [], name='is_train') 
input_activation = tf.placeholder(tf.float32, shape=[None, in_nodes], name='inputs') 
correct_output = tf.placeholder(tf.float32, shape=[None, out_nodes], name='correct_outputs') 

network_output = tf.identity(out_activations) 
correct_predictions = correct_fn(correct_output, network_output) 
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32)) 
error = cost_fn(correct_output, network_output) 
loss = error + FLAGS.regularization_weight * sum(tf.nn.l2_loss(w) for w in layer_weights) 

train_step = tf.train.MomentumOptimizer(learning_rate, momentum=momentum).minimize(loss, global_step=global_step) 

# Logging 
train_writer = tf.summary.FileWriter(trainlogfile, tf.get_default_graph()) 
test_writer = tf.summary.FileWriter(testlogfile, tf.get_default_graph()) 
gs_summary = tf.summary.scalar('global_step_at_epoch', global_step) 
merged = tf.summary.merge_all() 

Answer 1

我這裏還有幾行的上手培訓。同樣的邏輯適用於驗證

# Define placeholder for inputs data and labels 
inputs_placeholder = tf.placeholder(train_x.dtype, train_x.shape) 
labels_placeholder = tf.placeholder(train_y.dtype, train_y.shape) 
# Define a Dataset object using the above placeholders 
dataset = tf.contrib.data.Dataset.from_tensor_slices((inputs_placeholder,  labels_placeholder)) 
# Define batch_size 
batch_size = 128 
dataset = dataset.batch(batch_size) 
# Define iterator 
iterator = dataset.make_initializable_iterator() 
# Get one batch 
next_example, next_label = iterator.get_next() 
# calculate loss from the model fucntion you are using 
loss = some_model(next_example, next_label) 
# Set number of Epochs here 
num_epochs = 100 
for _ in range(num_epochs): 
    sess.run(iterator.initializer, feed_dict={inputs_placeholder: train_x, labels_placeholder: train_y})) 
    while True: 
     try: 
      _loss = sess.run(loss) 
     except tf.errors.OutOfRangeError: 
      break