MLP_Mnist_Data_TensorBoard.py

__autor__ = 'arachis'
__date__ = '2018/4/6'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

'''
    win下运行tensorboard:
    tensorboard --logdir=D:/code/deep_learning_test/tensorboard/logs/mnist_with_summaries
    浏览器输入localhost:6006
'''

max_steps = 1000
learning_rate = 0.001
dropout = 0.9
data_dir = '../cnn/MNIST_data'
log_dir = 'logs/mnist_with_summaries'
# Import data
mnist = input_data.read_data_sets(data_dir, one_hot=True)
sess = tf.InteractiveSession()
# Create a multilayer model.
# Input placeholders
with tf.name_scope('input'):
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
with tf.name_scope('input_reshape'):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', image_shaped_input, 10)
    # We can't initialize these variables to 0 - the network will get stuck.


def weight_variable(shape):
    """
    Create a weight variable with appropriate initialization.
    :param shape: list,tensor各个维度的大小
    :return: TF Variable
    """
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    """
    Create a bias variable with appropriate initialization.
    :param shape: list,tensor各个维度的大小
    :return: TF Variable
    """
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def variable_summaries(var):
    """
    Attach a lot of summaries to a Tensor (for TensorBoard visualization).
    :param var: TF Variable
    :return:None
    """
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min', tf.reduce_min(var))
        tf.summary.histogram('histogram', var)


def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
    """
    making a simple neural net layer.
    It also sets up name scoping so that the resultant graph is easy to read,
    and adds a number of summary ops.
    :param input_tensor:输入数据
    :param input_dim:输入数据的一维特征总数
    :param output_dim:输出类别总数
    :param layer_name:网络层名字
    :param act:激活函数
    :return:TF tensor
    """
    # Adding a name scope ensures logical grouping of the layers in the graph.
    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)
        with tf.name_scope('biases'):
            biases = bias_variable([output_dim])
            variable_summaries(biases)
        with tf.name_scope('Wx_plus_b'):
            preactivate = tf.matmul(input_tensor, weights) + biases
            tf.summary.histogram('pre_activations', preactivate)
        activations = act(preactivate, name='activation')
        tf.summary.histogram('activations', activations)
        return activations


hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    tf.summary.scalar('dropout_keep_probability', keep_prob)
    dropped = tf.nn.dropout(hidden1, keep_prob)
    # Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
with tf.name_scope('cross_entropy'):
    # The raw formulation of cross-entropy,
    #
    # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
    #                               reduction_indices=[1]))
    #
    # can be numerically unstable.
    #
    # So here we use tf.nn.softmax_cross_entropy_with_logits on the
    # raw outputs of the nn_layer above, and then average across
    # the batch.
    diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
    with tf.name_scope('total'):
        cross_entropy = tf.reduce_mean(diff)
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(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.summary.scalar('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(log_dir + '/test')
tf.global_variables_initializer().run()


# Train the model, and also write summaries.
# Every 10th step, measure test-set accuracy, and write test summaries
# All other steps, run train_step on training data, & add training summaries
def feed_dict(train):
    """
    Make a TensorFlow feed_dict: maps data onto Tensor placeholders.
    :param train:是否是训练集
    :return:feed_dict,{x: xs, y_: ys, keep_prob: k}
    """
    if train:
        xs, ys = mnist.train.next_batch(100)
        k = dropout
    else:
        xs, ys = mnist.test.images, mnist.test.labels
        k = 1.0
    return {x: xs, y_: ys, keep_prob: k}


saver = tf.train.Saver()
for i in range(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
        if i % 100 == 99:  # Record execution stats
            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
            run_metadata = tf.RunMetadata()
            summary, _ = sess.run([merged, train_step],
                                  feed_dict=feed_dict(True),
                                  options=run_options,
                                  run_metadata=run_metadata)
            train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
            train_writer.add_summary(summary, i)
            saver.save(sess, log_dir + "/model.ckpt", i)
            print('Adding run metadata for', i)
        else:  # Record a summary
            summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
            train_writer.add_summary(summary, i)
train_writer.close()
test_writer.close()