Tensorflow细节-P89-collection的使用

知识总结

（1）再次注意summary的使用

（2）x = rdm.rand(dataset_size, 2) y_ = [[x1**2 + x2**2] for (x1, x2) in x]这里的问题要注意

（3）注意batch时，全部先按照一套W进行前向传播，这时候在进行正则化时，加的是同一套W，然后反向传播改变W值，进行下一轮前向传播

代码如下

import tensorflow as tf

import numpy as np

from numpy.random import RandomState

rdm = RandomState(1)

dataset_size = 128

x = rdm.rand(dataset_size, 2)

y_ = [[x1**2 + x2**2] for (x1, x2) in x]

def get_weight(shape, alpha, name):

    with tf.variable_scope("get_variable" + name):

        var = tf.get_variable(name, shape, tf.float32, initializer=tf.truncated_normal_initializer(0.01))

        tf.add_to_collection("losses", tf.contrib.layers.l2_regularizer(alpha)(var))

        return var

with tf.name_scope("generate_value"):

    xs = tf.placeholder(tf.float32, [None, 2], name="x_input")

    ys = tf.placeholder(tf.float32, [None, 1], name="y_output")

batch_size = 8

layers_dimension = [2 ,10, 10, 10 ,1]

n_layers = len(layers_dimension)

in_dimension = layers_dimension[0]

cur_layer = xs

for i in range(1, n_layers):

    out_dimension = layers_dimension[i]

    with tf.variable_scope("layer%d" % i):

        weights = get_weight([in_dimension, out_dimension], 0.001, "layers")

        biases = tf.get_variable("biases", [out_dimension], tf.float32, tf.constant_initializer(0.0))

        cur_layer = tf.matmul(cur_layer, weights) + biases

        cur_layer = tf.nn.relu(cur_layer)

    in_dimension = layers_dimension[i]

with tf.name_scope("loss_op"):

    mse_loss = tf.reduce_mean(tf.square(ys - cur_layer))

    tf.add_to_collection("losses", mse_loss)

    loss = tf.add_n(tf.get_collection("losses"))

    tf.summary.scalar("loss", loss)

train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

merged = tf.summary.merge_all()

init = tf.global_variables_initializer()

with tf.Session() as sess:

    writer = tf.summary.FileWriter("path/", tf.get_default_graph())

    sess.run(init)

    for i in range(5000):

        start = i*batch_size % dataset_size

        end = min(start+batch_size, dataset_size)

        if i % 50 == 0:

            result = sess.run(merged, feed_dict={xs: x, ys: y_})

            writer.add_summary(result, global_step=i)

        if i % 500 ==0:

            loss_op = sess.run(loss, feed_dict={xs: x, ys: y_})

            print("After %d training, loss is %g" % (i, loss_op))

        _ = sess.run(train_op, feed_dict={xs: x[start:end], ys: y_[start:end]})

writer.close()

巴特西

Tensorflow细节-P89-collection的使用

知识总结

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