TensorFlow学习笔记----TensorBoard_2

，使用全连接识别MNIST，需要命名空间更多，程序更灵活，但基本的函数换是那些。

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
flags = tf.app.flagsFLAGS = flags.FLAGSflags.DEFINE_boolean('fake_data', False, 'If true, uses fake data ' 'for unit testing.')flags.DEFINE_integer('max_steps', 1000, 'Number of steps to run trainer.')flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')flags.DEFINE_float('dropout', 0.5, 'Keep probability for training dropout.')flags.DEFINE_string('data_dir', '/tmp/data', 'Directory for storing data')flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')
# Import datamnist = input_data.read_data_sets(FLAGS.data_dir,one_hot=True,fake_data=FLAGS.fake_data)sess = tf.InteractiveSession()
# We can't initialize these variables to 0 - the network will get stuck.def weight_variable(shape): """Create a weight variable with appropriate initialization.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial)
def bias_variable(shape): """Create a bias variable with appropriate initialization.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial)
def variable_summaries(var, name): """Attach a lot of summaries to a Tensor.""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.scalar_summary('mean/' + name, mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean))) tf.scalar_summary('sttdev/' + name, stddev) tf.scalar_summary('max/' + name, tf.reduce_max(var)) tf.scalar_summary('min/' + name, tf.reduce_min(var)) tf.histogram_summary(name, var)
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu): with tf.name_scope(layer_name): # This Variable will hold the state of the weights for the layer with tf.name_scope('weights'): weights = weight_variable([input_dim, output_dim]) variable_summaries(weights, layer_name + '/weights') with tf.name_scope('biases'): biases = bias_variable([output_dim]) variable_summaries(biases, layer_name + '/biases') with tf.name_scope('Wx_plus_b'): preactivate = tf.matmul(input_tensor, weights) + biases tf.histogram_summary(layer_name + '/pre_activations', preactivate) activations = act(preactivate, 'activation') tf.histogram_summary(layer_name + '/activations', activations) return activations
# Input placehoolderswith tf.name_scope('input'): x = tf.placeholder(tf.float32, [None, 784], name='x-input') y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
keep_prob = tf.placeholder(tf.float32)
def feed_dict(train): """Make a TensorFlow feed_dict: maps data onto Tensor placeholders.""" if train or FLAGS.fake_data: xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data) k = FLAGS.dropout else: xs, ys = mnist.test.images, mnist.test.labels k = 1.0 return {x: xs, y_: ys, keep_prob: k}
def train():
with tf.name_scope('input_reshape'): image_shaped_input = tf.reshape(x, [-1, 28, 28, 1]) tf.image_summary('input', image_shaped_input, 10)
hidden1 = nn_layer(x, 784, 500, 'layer1')

with tf.name_scope('dropout1'): tf.scalar_summary('dropout_keep_probability1', keep_prob) dropped1 = tf.nn.dropout(hidden1, keep_prob)
hidden2 = nn_layer(dropped1, 500, 300, 'layer2')
with tf.name_scope('dropout2'): tf.scalar_summary('dropout_keep_probability2', keep_prob) dropped2 = tf.nn.dropout(hidden2, keep_prob)
y = nn_layer(dropped2, 300, 10, 'layer3', act=tf.nn.softmax)
with tf.name_scope('cross_entropy'): diff = y_ * tf.log(y) with tf.name_scope('total'): cross_entropy = -tf.reduce_mean(diff) tf.scalar_summary('cross entropy', cross_entropy)
with tf.name_scope('train'): train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize( cross_entropy)
with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) with tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.scalar_summary('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default) merged = tf.merge_all_summaries() train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train',sess.graph) test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/test') tf.initialize_all_variables().run()
# for i in range(FLAGS.max_steps): if i % 10 == 0: # Record summaries and test-set accuracy summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False)) test_writer.add_summary(summary, i) print('Accuracy at step %s: %s' % (i, acc)) else: # Record train set summaries, and train summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True)) train_writer.add_summary(summary, i)

def main(_): if tf.gfile.Exists(FLAGS.summaries_dir): tf.gfile.DeleteRecursively(FLAGS.summaries_dir) tf.gfile.MakeDirs(FLAGS.summaries_dir) train()

if __name__ == '__main__': tf.app.run()

函数可以用在其他地方，打开目录定位/tmp/mnist_logs图表中可以有两条曲线，一个是是训练一个是测试





注意：如果保存数据过于频繁，会显著增加运行时间！毕竟硬盘读取的速度太慢，即使是SSD也不必要全部保存数据（程序速度能快一点是一点），一般的做法是每个100---1000步保存一下数据供图表显示即可。





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