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学习文献资料：《TensorFlow 官方文档中文版 - v1.2》摘录

训练 TensorFlow 神经网络模型:

learning@learning-virtual-machine:~/tensorflow/tensorflow/models/image/mnist$ python convolutional.pySuccessfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.Extracting data/train-images-idx3-ubyte.gzExtracting data/train-labels-idx1-ubyte.gzExtracting data/t10k-images-idx3-ubyte.gzExtracting data/t10k-labels-idx1-ubyte.gzInitialized!Step 0 (epoch 0.00), 23.9 msMinibatch loss: 12.053, learning rate: 0.010000Minibatch error: 90.6%Validation error: 84.6%Step 100 (epoch 0.12), 2406.9 msMinibatch loss: 3.306, learning rate: 0.010000Minibatch error: 6.2%Validation error: 7.1%

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Step 8100 (epoch 9.43), 1906.4 msMinibatch loss: 1.626, learning rate: 0.006302Minibatch error: 0.0%Validation error: 0.8%Step 8200 (epoch 9.54), 1771.1 msMinibatch loss: 1.625, learning rate: 0.006302Minibatch error: 0.0%Validation error: 0.8%Step 8300 (epoch 9.66), 1783.0 msMinibatch loss: 1.612, learning rate: 0.006302Minibatch error: 0.0%Validation error: 0.8%Step 8400 (epoch 9.77), 2009.2 msMinibatch loss: 1.595, learning rate: 0.006302Minibatch error: 0.0%Validation error: 0.7%Step 8500 (epoch 9.89), 1988.8 msMinibatch loss: 1.595, learning rate: 0.006302Minibatch error: 0.0%Validation error: 0.8%Test error: 0.8%learning@learning-virtual-machine:~/tensorflow/tensorflow/models/image/mnist$

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使用TensorFlow

learning@learning-virtual-machine:~/tensorflow/tensorflow/models/image/imagenet$ python classify_image.py>> Downloading inception-2015-12-05.tgz 100.0%Succesfully downloaded inception-2015-12-05.tgz 88931400 bytes.

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W tensorflow/core/framework/op_def_util.cc:320] Op is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().W tensorflow/core/framework/op_def_util.cc:320] Op is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().W tensorflow/core/framework/op_def_util.cc:320] Op is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.89233)indri, indris, Indri indri, Indri brevicaudatus (score = 0.00859)lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00264)custard apple (score = 0.00141)earthstar (score = 0.00107)learning@learning-virtual-machine:~/tensorflow/tensorflow/models/image/imagenet$ TensorFlow

# Copyright 2015 Google Inc. 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 image classification with Inception.Run image classification with Inception trained on ImageNet 2012 Challenge dataset.This program creates a graph from a saved GraphDef protocol buffer,and runs inference on an input JPEG image. It outputs human readablestrings of the top 5 predictions along with their probabilities.Change the --image_file argument to any jpg image to compute aclassification of that image.Please see the tutorial and website for a detailed description of howto use this script to perform image recognition.https://tensorflow.org/tutorials/image_recognition/"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport os.pathimport reimport sysimport tarfileimport numpy as npfrom six.moves import urllibimport tensorflow as tfFLAGS = tf.app.flags.FLAGS# classify_image_graph_def.pb:#   Binary representation of the GraphDef protocol buffer.# imagenet_synset_to_human_label_map.txt:#   Map from synset ID to a human readable string.# imagenet_2012_challenge_label_map_proto.pbtxt:#   Text representation of a protocol buffer mapping a label to synset ID.tf.app.flags.DEFINE_string(    'model_dir', '/tmp/imagenet',    """Path to classify_image_graph_def.pb, """    """imagenet_synset_to_human_label_map.txt, and """    """imagenet_2012_challenge_label_map_proto.pbtxt.""")tf.app.flags.DEFINE_string('image_file', '',                           """Absolute path to image file.""")tf.app.flags.DEFINE_integer('num_top_predictions', 5,                            """Display this many predictions.""")# pylint: disable=line-too-longDATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'# pylint: enable=line-too-longclass NodeLookup(object):  """Converts integer node ID's to human readable labels."""  def __init__(self,               label_lookup_path=None,               uid_lookup_path=None):    if not label_lookup_path:      label_lookup_path = os.path.join(          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')    if not uid_lookup_path:      uid_lookup_path = os.path.join(          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)  def load(self, label_lookup_path, uid_lookup_path):    """Loads a human readable English name for each softmax node.    Args:      label_lookup_path: string UID to integer node ID.      uid_lookup_path: string UID to human-readable string.    Returns:      dict from integer node ID to human-readable string.    """    if not tf.gfile.Exists(uid_lookup_path):      tf.logging.fatal('File does not exist %s', uid_lookup_path)    if not tf.gfile.Exists(label_lookup_path):      tf.logging.fatal('File does not exist %s', label_lookup_path)    # Loads mapping from string UID to human-readable string    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()    uid_to_human = {}    p = re.compile(r'[n\d]*[ \S,]*')    for line in proto_as_ascii_lines:      parsed_items = p.findall(line)      uid = parsed_items[0]      human_string = parsed_items[2]      uid_to_human[uid] = human_string    # Loads mapping from string UID to integer node ID.    node_id_to_uid = {}    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()    for line in proto_as_ascii:      if line.startswith('  target_class:'):        target_class = int(line.split(': ')[1])      if line.startswith('  target_class_string:'):        target_class_string = line.split(': ')[1]        node_id_to_uid[target_class] = target_class_string[1:-2]    # Loads the final mapping of integer node ID to human-readable string    node_id_to_name = {}    for key, val in node_id_to_uid.items():      if val not in uid_to_human:        tf.logging.fatal('Failed to locate: %s', val)      name = uid_to_human[val]      node_id_to_name[key] = name    return node_id_to_name  def id_to_string(self, node_id):    if node_id not in self.node_lookup:      return ''    return self.node_lookup[node_id]def create_graph():  """Creates a graph from saved GraphDef file and returns a saver."""  # Creates graph from saved graph_def.pb.  with tf.gfile.FastGFile(os.path.join(      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:    graph_def = tf.GraphDef()    graph_def.ParseFromString(f.read())    _ = tf.import_graph_def(graph_def, name='')def run_inference_on_image(image):  """Runs inference on an image.  Args:    image: Image file name.  Returns:    Nothing  """  if not tf.gfile.Exists(image):    tf.logging.fatal('File does not exist %s', image)  image_data = tf.gfile.FastGFile(image, 'rb').read()  # Creates graph from saved GraphDef.  create_graph()  with tf.Session() as sess:    # Some useful tensors:    # 'softmax:0': A tensor containing the normalized prediction across    #   1000 labels.    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048    #   float description of the image.    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG    #   encoding of the image.    # Runs the softmax tensor by feeding the image_data as input to the graph.    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')    predictions = sess.run(softmax_tensor,                           {
  'DecodeJpeg/contents:0': image_data})    predictions = np.squeeze(predictions)    # Creates node ID --> English string lookup.    node_lookup = NodeLookup()    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]    for node_id in top_k:      human_string = node_lookup.id_to_string(node_id)      score = predictions[node_id]      print('%s (score = %.5f)' % (human_string, score))def maybe_download_and_extract():  """Download and extract model tar file."""  dest_directory = FLAGS.model_dir  if not os.path.exists(dest_directory):    os.makedirs(dest_directory)  filename = DATA_URL.split('/')[-1]  filepath = os.path.join(dest_directory, filename)  if not os.path.exists(filepath):    def _progress(count, block_size, total_size):      sys.stdout.write('\r>> Downloading %s %.1f%%' % (          filename, float(count * block_size) / float(total_size) * 100.0))      sys.stdout.flush()    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)    print()    statinfo = os.stat(filepath)    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')  tarfile.open(filepath, 'r:gz').extractall(dest_directory)def main(_):  maybe_download_and_extract()  image = (FLAGS.image_file if FLAGS.image_file else           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))  run_inference_on_image(image)if __name__ == '__main__':  tf.app.run()