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Tensorflow 逻辑回归处理mnist数据集
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('./data/mnist_data/', one_hot=True)
train_img = mnist.train.images
train_label = mnist.train.labels
test_img = mnist.test.images
test_label = mnist.test.labelsx = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))actv = tf.nn.softmax(tf.matmul(x, W) + b)cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1))learning_rate = 0.01
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))accr = tf.reduce_mean(tf.cast(pred, tf.float32))init = tf.global_variables_initializer()
training_epochs = 100
batch_size = 100
display_step = 5
sess = tf.Session()
sess.run(init)
for epoch in range(training_epochs):
avg_cost = 0.
num_batch = int(mnist.train.num_examples / batch_size)
for i in range(num_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
feeds_train = {x: batch_xs, y: batch_ys}
sess.run(optm, feed_dict=feeds_train)
avg_cost += sess.run(cost, feed_dict=feeds_train) / num_batchif epoch % display_step == 0:
feeds_test = {x: mnist.test.images, y: mnist.test.labels}
train_acc = sess.run(accr, feed_dict=feeds_train)
test_acc = sess.run(accr, feed_dict=feeds_test)
print("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f" % (epoch, training_epochs, avg_cost, train_acc, test_acc))
print("DONE")
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