TensorFlow 中简单前馈 NN 的 GPU 训练的有效示例实现？也许与 tf.data？

Question

我刚开始使用 GPU 版本的 TensorFlow，希望它能加快我的前馈神经网络的训练速度。我可以在我的 GPU (GTX1080ti) 上进行训练，但不幸的是，它并不比我目前实现的方式在我的 CPU (i7-8700K) 上进行相同的训练快得多。在训练期间，GPU 似乎几乎没有被使用，这让我怀疑我的实现中的瓶颈是如何使用 feed_dict 将数据从主机复制到设备。

我听说 TensorFlow 有一个叫做“tf.data”管道的东西，它应该可以更轻松、更快地将数据馈送到 GPU 等。但是我找不到任何简单的例子来说明这个概念实施到多层感知器训练中，作为 feed_dict 的替代品。

有没有人知道这样的例子并可以指出它？最好尽可能简单，因为我通常是 TensorFlow 新手。还是在我当前的实现中我应该改变一些其他的东西来提高它的效率？我在这里粘贴我的代码：

import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
tf.reset_default_graph()   
import time

# Function for iris dataset.
def get_iris_data():
    iris   = datasets.load_iris()
    data   = iris["data"]
    target = iris["target"]

    # Convert to one-hot vectors
    num_labels = len(np.unique(target))
    all_Y = np.eye(num_labels)[target]
    return train_test_split(data, all_Y, test_size=0.33, random_state=89)
# Function which initializes tensorflow weights & biases for feed-forward NN.
def InitWeights(LayerSizes):
    with tf.device('/gpu:0'):
        # Make tf placeholders for network inputs and outputs.
        X = tf.placeholder( shape = (None,LayerSizes[0]),
                            dtype = tf.float32,
                            name ='InputData')
        y = tf.placeholder( shape = (None,LayerSizes[-1]),
                            dtype = tf.float32,
                            name ='OutputData')
        # Initialize weights and biases.
        W = {}; b = {};
        for ii in range(len(LayerSizes)-1):
            layername = f'layer%s' % ii
            with tf.variable_scope(layername):
                ny = LayerSizes[ii]
                nx = LayerSizes[ii+1]
                # Weights (initialized with xavier initializatiion).
                W['Weights_'+layername] = tf.get_variable(
                                    name = 'Weights_'+layername,
                                    shape = (ny, nx),
                                    initializer = tf.contrib.layers.xavier_initializer(),
                                    dtype = tf.float32
                                    )
                # Bias (initialized with xavier initializatiion).
                b['Bias_'+layername] = tf.get_variable(
                                    name = 'Bias_'+layername,
                                    shape = (nx),
                                    initializer = tf.contrib.layers.xavier_initializer(),
                                    dtype = tf.float32
                                    )
    return W, b, X, y
# Function for forward propagation of NN.
def FeedForward(X, W, b):    
    with tf.device('/gpu:0'):
        # Initialize 'a' of first layer to the placeholder of the network input.
        a = X
        # Loop all layers of the network.
        for ii in range(len(W)):
            # Use name of each layer as index.
            layername = f'layer%s' % ii
            ## Weighted sum: z = input*W + b
            z = tf.add(tf.matmul(a, W['Weights_'+layername], name = 'WeightedSum_z_'+layername), b['Bias_'+layername])
            ## Passed through actication fcn: a = h(z)
            if ii == len(W)-1:
                a = z
            else:
                a = tf.nn.relu(z, name = 'activation_a_'+layername)
    return a

if __name__ == "__main__":
    # Import data
    train_X, test_X, train_y, test_y = get_iris_data()
    # Define network size [ninputs-by-256-by-outputs]
    LayerSizes = [4, 256, 3]
    # Initialize weights and biases.
    W, b, X, y  = InitWeights(LayerSizes)

    # Define loss function to optimize.
    yhat = FeedForward(X, W, b)
    loss = tf.reduce_sum(tf.square(y - yhat),reduction_indices=[0])

    # Define optimizer to use when minimizing loss function.
    all_variables = tf.trainable_variables()
    optimizer     = tf.train.GradientDescentOptimizer(learning_rate = 0.0001)
    train_op      = optimizer.minimize(loss, var_list = all_variables)

    # Start tf session and initialize variables.
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())

    # Train 10000 minibatches and time how long it takes.   
    t0 = time.time()
    for i in range(10000):
        ObservationsToUse = np.random.choice(len(train_X), 32)
        X_minibatch = train_X[ObservationsToUse,:]
        y_minibatch = train_y[ObservationsToUse,:]
        sess.run(train_op, feed_dict={X : X_minibatch, y : y_minibatch})
    t1 = time.time()

    print('Training took %0.2f seconds' %(t1-t0)) 
    sess.close()

Answer 1

速度可能很低，因为：

• 您正在创建占位符。使用 numpy，我们将数据插入到 占位符，从而将它们转换为图的张量。

通过使用 tf.data.Dataset，您可以创建一个直接管道，使数据直接流入图表，无需占位符。它们速度快、可扩展，并且有许多功能可供使用。

    with np.load("/var/data/training_data.npy") as data:
  features = data["features"]
  labels = data["labels"]
    # Assume that each row of `features` corresponds to the same row as `labels`.
    assert features.shape[0] == labels.shape[0]
    dataset = tf.data.Dataset.from_tensor_slices((features, labels))

一些有用的功能：

dataset = dataset.shuffle(buffer_size=10000)
dataset = dataset.batch(32) # Creating batches
dataset = dataset.repeat(num_epochs) # repeat the dataset 'N' times
iterator = dataset.make_one_shot_iterator() # Create a iterator to retrieve batches of data

X, Y = iterator.get_next()

这里，32 是批量大小。 在你的情况下，

dataset = tf.data.Dataset.from_tensor_slices((data, targets))

因此，不需要占位符。直接运行，

session.run( train_op ) # no feed_dict!!