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私は専門家のためのテンソルフローのチュートリアルに従っています。チュートリアルのコピーである以下のようなコードを記述します。しかし、私は私のコードを実行すると、精度は92％、86％です...それは私のMacで非常に高速1または2分だけ実行されます。そして、ステップが増加して、精度TensorFlow専門家向けMNIST低精度
step 0, training accuracy 0.08
step 100, training accuracy 0.1
step 200, training accuracy 0.16
step 300, training accuracy 0.22
step 400, training accuracy 0.1
step 500, training accuracy 0.18
step 600, training accuracy 0.26
step 700, training accuracy 0.16
step 800, training accuracy 0.24
...
step 19600, training accuracy 0.9
step 19700, training accuracy 0.82
step 19800, training accuracy 0.98
step 19900, training accuracy 0.86
test accuracy 0.9065
しかし、私は公式のコードmnist_deep.pyを実行したとき。非常にゆっくりと出力されます。
step 0, training accuracy 0.1
step 100, training accuracy 0.84
step 200, training accuracy 0.84
step 300, training accuracy 0.9
step 400, training accuracy 0.88
step 500, training accuracy 0.92
step 600, training accuracy 0.98
step 700, training accuracy 0.96
step 800, training accuracy 0.96
step 900, training accuracy 0.96
step 1000, training accuracy 0.96
step 1100, training accuracy 0.94
step 1200, training accuracy 0.96
これはうまくいきます。私は自分のコードとmnist_deep.pyを比較します。違いは彼らと一緒に使うことだけです。なぜ私のコードはとても悪いですか？なぜ彼らは一緒に使うべきですか？以下は私のコードです。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def main(_):
mnist = input_data.read_data_sets("/MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.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 = tf.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 = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
if __name__ == '__main__':
tf.app.run(main=main)