tensorflow从0开始（4）——解读mnist程序

前言

由于图像的问题学习机器学习，选择TensorFlow，但似乎直接从ImageNet的例子出发，却发现怎么都找不到头（python也不会，机器学习也不懂），但根据我以往的经验，遇到这种情况，又没有明眼人指路时，就瞎碰（没错，就是瞎碰），从一点开始，遇到什么看什么，看相关的各种知识，最好能反复两遍以上，然后揪住其中一个例子，联系之前看过的东西，加深一遍理解。 
当然如果只是把现成的东西拿来用，就不用这么复杂了，简单了解下就行。但若要变通运用，上面就是我的方法。

背景介绍

TensorFlow官网上的几个例子教程，简单看起来都不难（个人是个机器学习零基础，数学也不怎么样的人，所以它似乎真的不难），只要根据上面提供的链接，读些相关paper就好，下面是官网教程的链接。 
https://www.tensorflow.org/versions/r0.9/tutorials/index.html 
本想从ImageNet开始，但是它没教模型怎么构建，直接给了个模型文件，加载进去的。所以不得回过头从最简单的例子开始。就是这个mnist（手写体识别）教程。

mnist

这是个什么东西，大家自行google。 
Tensorflow的官网给到两个例子，简单的例子，通过一般的机器学习算法实现。涉及到的概念在上一篇帖子提到了，大家可以自己看下：

• logistic regression
• ReLU activation激活函数
• dropout
• Softmax regression
• cross entropy交叉熵
• hypothesis function
• cost function/loss function
• gradient descent 梯度下降

个人觉得不完全理解也没关系，好事多磨，总有个过程，我也不理解。。。。。

还有个关于CNN的例子，这是我的目的。

mnist 神经网络实现

据官网说这个的识别率更高，我相信了，因为这本来就是我的目的，下面是代码，我机器上的存放路径 
~/libsource/tensorflow/tensorflow/models/image/mnist：

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at##     http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# =============================================================================="""Simple, end-to-end, LeNet-5-like convolutional MNIST model example.This should achieve a test error of 0.7%. Please keep this model as simple andlinear as possible, it is meant as a tutorial for simple convolutional models.Run with --self_test on the command line to execute a short self-test."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport gzipimport osimport sysimport timeimport numpyfrom six.moves import urllibfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tfSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'WORK_DIRECTORY = 'data'IMAGE_SIZE = 28NUM_CHANNELS = 1PIXEL_DEPTH = 255NUM_LABELS = 10VALIDATION_SIZE = 5000  # Size of the validation set.SEED = 66478  # Set to None for random seed.BATCH_SIZE = 64NUM_EPOCHS = 10EVAL_BATCH_SIZE = 64EVAL_FREQUENCY = 100  # Number of steps between evaluations.tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.")FLAGS = tf.app.flags.FLAGSdef maybe_download(filename):  """Download the data from Yann's website, unless it's already here."""  if not tf.gfile.Exists(WORK_DIRECTORY):    tf.gfile.MakeDirs(WORK_DIRECTORY)  filepath = os.path.join(WORK_DIRECTORY, filename)  if not tf.gfile.Exists(filepath):    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)    with tf.gfile.GFile(filepath) as f:      size = f.Size()    print('Successfully downloaded', filename, size, 'bytes.')  return filepathdef extract_data(filename, num_images):  """Extract the images into a 4D tensor [image index, y, x, channels].  Values are rescaled from [0, 255] down to [-0.5, 0.5].  """  print('Extracting', filename)  with gzip.open(filename) as bytestream:    bytestream.read(16)    buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)    data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)    data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH    data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)    return datadef extract_labels(filename, num_images):  """Extract the labels into a vector of int64 label IDs."""  print('Extracting', filename)  with gzip.open(filename) as bytestream:    bytestream.read(8)    buf = bytestream.read(1 * num_images)    labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)  return labelsdef fake_data(num_images):  """Generate a fake dataset that matches the dimensions of MNIST."""  data = numpy.ndarray(      shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),      dtype=numpy.float32)  labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)  for image in xrange(num_images):    label = image % 2    data[image, :, :, 0] = label - 0.5    labels[image] = label  return data, labelsdef error_rate(predictions, labels):  """Return the error rate based on dense predictions and sparse labels."""  return 100.0 - (      100.0 *      numpy.sum(numpy.argmax(predictions, 1) == labels) /      predictions.shape[0])def main(argv=None):  # pylint: disable=unused-argument  if FLAGS.self_test:    print('Running self-test.')    train_data, train_labels = fake_data(256)    validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)    test_data, test_labels = fake_data(EVAL_BATCH_SIZE)    num_epochs = 1  else:    # Get the data.    train_data_filename = maybe_download('train-images-idx3-ubyte.gz')    train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')    test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')    test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')    # Extract it into numpy arrays.    train_data = extract_data(train_data_filename, 60000)    train_labels = extract_labels(train_labels_filename, 60000)    test_data = extract_data(test_data_filename, 10000)    test_labels = extract_labels(test_labels_filename, 10000)    # Generate a validation set.    validation_data = train_data[:VALIDATION_SIZE, ...]    validation_labels = train_labels[:VALIDATION_SIZE]    train_data = train_data[VALIDATION_SIZE:, ...]    train_labels = train_labels[VALIDATION_SIZE:]    num_epochs = NUM_EPOCHS  train_size = train_labels.shape[0]  # This is where training samples and labels are fed to the graph.  # These placeholder nodes will be fed a batch of training data at each  # training step using the {feed_dict} argument to the Run() call below.  train_data_node = tf.placeholder(      tf.float32,      shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))  train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))  eval_data = tf.placeholder(      tf.float32,      shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))  # The variables below hold all the trainable weights. They are passed an  # initial value which will be assigned when we call:  # {tf.initialize_all_variables().run()}  conv1_weights = tf.Variable(      tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.                          stddev=0.1,                          seed=SEED))  conv1_biases = tf.Variable(tf.zeros([32]))  conv2_weights = tf.Variable(      tf.truncated_normal([5, 5, 32, 64],                          stddev=0.1,                          seed=SEED))  conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))  fc1_weights = tf.Variable(  # fully connected, depth 512.      tf.truncated_normal(          [IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],          stddev=0.1,          seed=SEED))  fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))  fc2_weights = tf.Variable(      tf.truncated_normal([512, NUM_LABELS],                          stddev=0.1,                          seed=SEED))  fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))  # We will replicate the model structure for the training subgraph, as well  # as the evaluation subgraphs, while sharing the trainable parameters.  def model(data, train=False):    """The Model definition."""    # 2D convolution, with 'SAME' padding (i.e. the output feature map has    # the same size as the input). Note that {strides} is a 4D array whose    # shape matches the data layout: [image index, y, x, depth].    conv = tf.nn.conv2d(data,                        conv1_weights,                        strides=[1, 1, 1, 1],                        padding='SAME')    # Bias and rectified linear non-linearity.    relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))    # Max pooling. The kernel size spec {ksize} also follows the layout of    # the data. Here we have a pooling window of 2, and a stride of 2.    pool = tf.nn.max_pool(relu,                          ksize=[1, 2, 2, 1],                          strides=[1, 2, 2, 1],                          padding='SAME')    conv = tf.nn.conv2d(pool,                        conv2_weights,                        strides=[1, 1, 1, 1],                        padding='SAME')    relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))    pool = tf.nn.max_pool(relu,                          ksize=[1, 2, 2, 1],                          strides=[1, 2, 2, 1],                          padding='SAME')    # Reshape the feature map cuboid into a 2D matrix to feed it to the    # fully connected layers.    pool_shape = pool.get_shape().as_list()    reshape = tf.reshape(        pool,        [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])    # Fully connected layer. Note that the '+' operation automatically    # broadcasts the biases.    hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)    # Add a 50% dropout during training only. Dropout also scales    # activations such that no rescaling is needed at evaluation time.    if train:      hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)    return tf.matmul(hidden, fc2_weights) + fc2_biases  # Training computation: logits + cross-entropy loss.  logits = model(train_data_node, True)  loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(      logits, train_labels_node))  # L2 regularization for the fully connected parameters.  regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +                  tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))  # Add the regularization term to the loss.  loss += 5e-4 * regularizers  # Optimizer: set up a variable that's incremented once per batch and  # controls the learning rate decay.  batch = tf.Variable(0)  # Decay once per epoch, using an exponential schedule starting at 0.01.  learning_rate = tf.train.exponential_decay(      0.01,                # Base learning rate.      batch * BATCH_SIZE,  # Current index into the dataset.      train_size,          # Decay step.      0.95,                # Decay rate.      staircase=True)  # Use simple momentum for the optimization.  optimizer = tf.train.MomentumOptimizer(learning_rate,                                         0.9).minimize(loss,                                                       global_step=batch)  # Predictions for the current training minibatch.  train_prediction = tf.nn.softmax(logits)  # Predictions for the test and validation, which we'll compute less often.  eval_prediction = tf.nn.softmax(model(eval_data))  # Small utility function to evaluate a dataset by feeding batches of data to  # {eval_data} and pulling the results from {eval_predictions}.  # Saves memory and enables this to run on smaller GPUs.  def eval_in_batches(data, sess):    """Get all predictions for a dataset by running it in small batches."""    size = data.shape[0]    if size < EVAL_BATCH_SIZE:      raise ValueError("batch size for evals larger than dataset: %d" % size)    predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)    for begin in xrange(0, size, EVAL_BATCH_SIZE):      end = begin + EVAL_BATCH_SIZE      if end <= size:        predictions[begin:end, :] = sess.run(            eval_prediction,            feed_dict={eval_data: data[begin:end, ...]})      else:        batch_predictions = sess.run(            eval_prediction,            feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})        predictions[begin:, :] = batch_predictions[begin - size:, :]    return predictions  # Create a local session to run the training.  start_time = time.time()  with tf.Session() as sess:    # Run all the initializers to prepare the trainable parameters.    tf.initialize_all_variables().run()    print('Initialized!')    # Loop through training steps.    for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):      # Compute the offset of the current minibatch in the data.      # Note that we could use better randomization across epochs.      offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)      batch_data = train_data[offset:(offset + BATCH_SIZE), ...]      batch_labels = train_labels[offset:(offset + BATCH_SIZE)]      # This dictionary maps the batch data (as a numpy array) to the      # node in the graph it should be fed to.      feed_dict = {train_data_node: batch_data,                   train_labels_node: batch_labels}      # Run the graph and fetch some of the nodes.      _, l, lr, predictions = sess.run(          [optimizer, loss, learning_rate, train_prediction],          feed_dict=feed_dict)      if step % EVAL_FREQUENCY == 0:        elapsed_time = time.time() - start_time        start_time = time.time()        print('Step %d (epoch %.2f), %.1f ms' %              (step, float(step) * BATCH_SIZE / train_size,               1000 * elapsed_time / EVAL_FREQUENCY))        print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))        print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))        print('Validation error: %.1f%%' % error_rate(            eval_in_batches(validation_data, sess), validation_labels))        sys.stdout.flush()    # Finally print the result!    test_error = error_rate(eval_in_batches(test_data, sess), test_labels)    print('Test error: %.1f%%' % test_error)    if FLAGS.self_test:      print('test_error', test_error)      assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (          test_error,)if __name__ == '__main__':  tf.app.run()其tensorflow图如下图所示：

解读

• 由于没写过python，所以一开始我就困惑了，这货为什么有两个main函数入口：

if __name__ == '__main__':def main(argv=None):  # pylint: disable=unused-argument

解释见下面两篇帖子： 
http://stackoverflow.com/questions/419163/what-does-if-name-main-do 
http://stackoverflow.com/questions/4041238/why-use-def-main

• 待续。。。。