MethodError: keine Methode passende reduce_sum (:: TensorFlow.Tensor {Float32}; reduction_indices = [2])

Question

ich bei TensorFlow aus dem Beispiel lerne: MNIST Tutorial - Julia.jl

im Gegensatz zu ihren Tutorial, ich meine eigenen benutzerdefinierten Bilder mit (32 X 32 (RGB)) Format asimular zu cifar-10. Jede Zeile in der CSV ist 3073 Spalten, die letzte ist die Bezeichnung. Und ich habe 300 Zeilen, von denen ich Array Slicing verwendet habe, um die ersten 240 Zeilen für das Training und die restlichen für Tests auszuwählen. also in einer nut shell Ich habe 4 Arrays: images_train = 240 X 3072 labels_train = 240 X 1 images_test = 60 X 3072 labels_test = 60 X 1 Unten ist der Fehler: THE ERROR

der Code:

using TensorFlow 
using Distributions 
include("loader.jl") 
session = Session(Graph()) 
function weight_variable(shape) 
    initial = map(Float32, rand(Normal(0, .001), shape...)) 
    return Variable(initial) 
end 

function bias_variable(shape) 
    initial = fill(Float32(.1), shape...) 
    return Variable(initial) 
end 

function conv2d(x, W) 
    nn.conv2d(x, W, [1, 1, 1, 1], "SAME") 
end 

function max_pool_2x2(x) 
    nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], "SAME") 
end 

x = placeholder(Float32) 
y_ = placeholder(Float32) 

W_conv1 = weight_variable([5, 5, 3, 32]) 
b_conv1 = bias_variable([32]) 

x_image = reshape(x, [-1, 32, 32, 3]) 

h_conv1 = nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 

W_conv2 = weight_variable([5, 5, 32, 64]) 
b_conv2 = bias_variable([64]) 

h_conv2 = nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 
h_pool2 = max_pool_2x2(h_conv2) 

W_fc1 = weight_variable([7*7*64, 1024]) 
b_fc1 = bias_variable([1024]) 

h_pool2_flat = reshape(h_pool2, [-1, 7*7*64]) 
h_fc1 = nn.relu(h_pool2_flat * W_fc1 + b_fc1) 

keep_prob = placeholder(Float32) 
h_fc1_drop = nn.dropout(h_fc1, keep_prob) 

W_fc2 = weight_variable([1024, 9]) 
b_fc2 = bias_variable([9]) 

y_conv = nn.softmax(h_fc1_drop * W_fc2 + b_fc2) 


cross_entropy = reduce_mean(-reduce_sum((y_ .* log(y_conv)), reduction_indices=[2])) 

train_step = train.minimize(train.AdamOptimizer(1e-4), cross_entropy) 

correct_prediction = indmax(y_conv, 2) .== indmax(y_, 2) 

accuracy = reduce_mean(cast(correct_prediction, Float32)) 

run(session, initialize_all_variables()) 

for i in 1:40 
    images_train,labels_train = next_batch(16) # randomly generate batches from training dataset 
    if i%4 == 1 
     train_accuracy = run(session, accuracy, Dict(x=>images_train, y_=>labels_train, keep_prob=>1.0)) 
     info("step $i, training accuracy $train_accuracy") 
    end 
    run(session, train_step, Dict(x=>images_train, y_=>labels_train, keep_prob=>.5)) 
end 

images_test, labels_test = load_test_set() # 60 X 3072, 60 X 1 Arrays 

test_accuracy = run(session, accuracy, Dict(x=>images_test, y_=>labels_test, keep_prob=>1.0)) 
info("test accuracy $test_accuracy") 

Danke

Answer 1

es gibt kein Argument ist reduction_indices in reduce_sum Sie es axis ändern müssen, wie:

cross_entropy = reduce_mean(-reduce_sum((y_ .* log(y_conv)), axis=[2]))