推薦系統與深度學習-學習筆記二

僅供學習
書本的第三章，講了TensorFlow平臺。

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

data_dir = './p38/'


def main():
    mnist = input_data.read_data_sets(data_dir, one_hot=True)

    x = tf.placeholder(tf.float32, [None, 784])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    y = tf.matmul(x, W) + b

    y_ = tf.placeholder(tf.float32, [None, 10])

    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

    sess = tf.InteractiveSession()
    tf.global_variables_initializer().run()

    for _ in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

    correct_prediction = tf.equal(tf.arg_max(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

main()

輸出結果

0.9198

3.4 其他深度學習平臺

• Keras 工作在TensorFlow和Theano之上
• Caffe 計算機視覺
• Theano 數值計算，不支持GPU
• MXNet
• Torch lua
• PyTorch 支持GPU
• DL4J
• Cognitive Toolkit 微軟
• Lasagne 工作在Theano之上
• DSSTNE 推薦系統，Amazon

ONNX，導入導出以上工具和引擎