Question

我还看到了其他有关深度学习模型可重复性的问题。特别是在张量流中，主要方法是设置tf.random.set_random_seed()

在以下示例代码中，在tensorflow中定义了一个LSTM，然后在MNIST数据集上对其进行了训练。我试图设置随机种子，以便网络的初始化是相同的（相同的权重和偏差），并尝试在经过相同数量的训练迭代后获得相同的损失和准确性结果。

尽管我为权重和偏差的初始化设置了种子，然后在会话循环中通过张量流设置了随机种子，但是在网络的不同初始化的相同迭代中，我仍然会得到不同的损失和准确性。

此代码中的随机性来源是什么，可以阻止每次随机事件播种的尝试？

from __future__ import print_function

import tensorflow as tf
from tensorflow.contrib import rnn

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# Training Parameters
learning_rate = 0.001
training_steps = 100
batch_size = 128
display_step = 200

# Network Parameters
num_input = 28 # MNIST data input (img shape: 28*28)
timesteps = 28 # timesteps
num_hidden = 128 # hidden layer num of features
num_classes = 10 # MNIST total classes (0-9 digits)

# tf Graph input
X = tf.placeholder("float", [None, timesteps, num_input])
Y = tf.placeholder("float", [None, num_classes])

# Define weights
weights = {
    'out': tf.Variable(tf.random_normal([num_hidden, num_classes], seed=1))
}
biases = {
    'out': tf.Variable(tf.random_normal([num_classes], seed=1))
}


def RNN(x, weights, biases):
    x = tf.unstack(x, timesteps, 1)
    lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

logits = RNN(X, weights, biases)
prediction = tf.nn.softmax(logits)

# Define loss and optimizer
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)

# Evaluate model (with test logits, for dropout to be disabled)
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()


# Start training
with tf.Session() as sess:
    tf.set_random_seed(1)
    # Run the initializer
    sess.run(init)

    for step in range(1, training_steps+1):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_x = batch_x.reshape((batch_size, timesteps, num_input))
        # Run optimization op (backprop)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accuracy
            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
                                                                 Y: batch_y})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))

    print("Optimization Finished!")

    # Calculate accuracy for 128 mnist test images
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:", \
    sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))

试图在tensorflow上设置种子以使可复制在LSTM上运行？

0 个答案: