C2:Run With TensorFlow

GavinMoreYoung

于 2019-02-04 15:50:48 发布

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分类专栏： TensorFlow

本文链接：https://blog.youkuaiyun.com/u013452217/article/details/86763425

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本文通过使用TensorFlow框架实现了一个手写数字识别的深度学习模型。从数据准备到模型训练，详细介绍了如何利用softmax函数进行多分类任务，并评估了模型在MNIST数据集上的准确率。

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HelloWorld

h = tf.constant("Hello")
w = tf.constant(" World!")
hw = h + w
with tf.Session() as sess:
    ans = sess.run(hw)
print(ans)

softmax

DATA_DIR = '/tmp/data'
NUM_STEPS = 1000
MINIBATCH_SIZE = 100
data = input_data.read_data_sets(DATA_DIR, one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
y_true = tf.placeholder(tf.float32, [None, 10])
y_pred = tf.matmul(x, W)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=y_pred, labels=y_true))
gd_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_mask = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
accuracy = tf.reduce_mean(tf.cast(correct_mask, tf.float32))
with tf.Session() as sess:
    # Train
    sess.run(tf.global_variables_initializer())
    for _ in range(NUM_STEPS):
        batch_xs, batch_ys = data.train.next_batch(MINIBATCH_SIZE)
        sess.run(gd_step, feed_dict={x: batch_xs, y_true: batch_ys})
    # Test
    ans = sess.run(accuracy, feed_dict={x: data.test.images, y_true: data.test.labels})
print("Accuracy: {:.4}%".format(ans*100))