Question

我正在使用python 2.7并尝试更好地了解张量流。

我正在使用以下代码尝试在mnist数据上训练一个自动编码器，当我使用sigmoid激活时，它gerelizes ok（90％），但是当我尝试relu它只是随机的。

最接近我found，但我找不到解决方案。

我做错了什么？我应该加辍学吗？也许成本函数或优化器对relu不好？

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




# Parameters
learning_rate = 0.01
training_epochs = 60
batch_size = 256
display_step = 1
examples_to_show = 10

# Network Parameters
n_hidden_1 = 256 # 1st layer num features
#n_hidden_1 = 400
n_hidden_2 = 128 # 2nd layer num features
#n_hidden_2 = 250
n_hidden_3 = 60

#n_hidden_2 = 30
n_input = 784 # MNIST data input (img shape: 28*28)

# tf Graph input (only pictures)
X = tf.placeholder("float", [None, n_input])

keep_prob = tf.placeholder("float", None)
#keep_prob = tf.placeholder(tf.float32)

weights = {
    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),
    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_2])),
    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
    'decoder_h3': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
}
biases = {
    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),
    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_2])),
    'decoder_b2': tf.Variable(tf.random_normal([n_hidden_1])),
    'decoder_b3': tf.Variable(tf.random_normal([n_input])),
}




# Building the encoder
def encoder(x):
    # Encoder Hidden layer with sigmoid activation #1

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),
                                   biases['encoder_b1']))
    # Decoder Hidden layer with sigmoid activation #2
    dropout1 = tf.nn.dropout(layer_1, keep_prob)

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),
                                   biases['encoder_b2']))

    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']),
                                   biases['encoder_b3']))

    return layer_3


# Building the decoder
def decoder(x):
    # Encoder Hidden layer with sigmoid activation #1
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),
                                   biases['decoder_b1']))
    # Decoder Hidden layer with sigmoid activation #2
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),
                                   biases['decoder_b2']))

    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']),
                                   biases['decoder_b3']))

    return layer_3

# Construct model
encoder_op = encoder(X)
decoder_op = decoder(encoder_op)

# Prediction
y_pred = decoder_op
x_encode = encoder_op 

# Targets (Labels) are the input data.
y_true = X

# Define loss and optimizer, minimize the squared error
cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))

optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)
#optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()



# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    print("num examples are ", mnist.train.num_examples, mnist.validation.num_examples, mnist.test.num_examples)
    total_batch = int(mnist.train.num_examples/batch_size)
    # Training cycle
    for epoch in range(training_epochs):
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})
        # Display logs per epoch step
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1),
                  "cost=", "{:.9f}".format(c))

    print("Optimization Finished!")




    # Applying encode and decode over test set
    encode_decode = sess.run(
        y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})

    encoded_data = sess.run(x_encode, feed_dict={X: mnist.test.images})


distance_matrix = scipy.spatial.distance.squareform(pdist(encoded_data))

d_m_2 = distance_matrix[:,:]
np.fill_diagonal(d_m_2,np.inf)

labels = np.argmax(mnist.test.labels,1) #these are the labels!
predicate = labels[np.argmin(d_m_2,1)] #get the indecies of the closest data sample
print ("this is the ammount of coorect clasificcations in the test set", np.sum(labels==predicate)) #count how many similar values are there!

谢谢！

Answer 1

也许ReLU正在努力应对负输入值，因为它被定义为R（x）：= max（0，x）。因此，如果输入为负，则R（x）= 0，并且梯度也将为零。因此，您的优化器不知道如何更新参数。你可以尝试使用像tf.random_normal(shape=..., mean=0.5, stddev=0.2)那样积极的东西初始化你的权重。也许这会减少这个问题。

python tensor flow - 无法在autoencoder任务中学习

1 个答案: