Tensorflow 学习
源码来自 https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
# coding: utf8 # 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, 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 and linear 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_import from __future__ import division from __future__ import print_function import gzip import os import sys import time import numpy from six.moves import urllib from six.moves import xrange import tensorflow as tf # 数据源 SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' # 工作目录，存放下载的数据 WORK_DIRECTORY = 'data' # MNIST 数据集特征： # 图像尺寸 28x28 IMAGE_SIZE = 28 # 黑白图像 NUM_CHANNELS = 1 # 像素值0~255 PIXEL_DEPTH = 255 # 标签分10个类别 NUM_LABELS = 10 # 验证集共 5000 个样本 VALIDATION_SIZE = 5000 # 随机数种子，可设为 None 表示真的随机 SEED = 66478 # 批处理大小为64 BATCH_SIZE = 64 # 数据全集一共过10遍网络 NUM_EPOCHS = 10 # 验证集批处理大小也是64 EVAL_BATCH_SIZE = 64 # 验证时间间隔，每训练100个批处理，做一次评估 EVAL_FREQUENCY = 100 tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.") FLAGS = tf.app.flags.FLAGS # 如果下载过了数据，就不再重复下载 def maybe_download(filename): 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: #参考代码中为f.Szie()，报错 size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath # 抽取数据，变为 4维张量[图像索引，y, x, c] # 去均值、做归一化，范围变到[-0.5, 0.5] def extract_data(filename, num_images): 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 data # 抽取图像标签,int64类型 def extract_labels(filename, num_images): 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 labels # 假数据，用于功能自测，数据维度与mnist一致。 def fake_data(num_images): 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, labels # 计算分类错误率 def error_rate(predictions, labels): return 100.0 - ( 100.0 * numpy.sum(numpy.argmax(predictions, 1) == labels) / predictions.shape[0]) # 主函数 def main(argv=None): # 自测 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: # 下载数据 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') # 载入数据到numpy 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) # 产生评测集 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] # 训练样本和标签将从这里送入网络。 # 每训练迭代步，占位符节点将被送入一个批处理数据 # 训练数据节点 train_data_node = tf.placeholder( tf.float32, shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)) # 训练标签节点 train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,)) # 评测数据节点 eval_data = tf.placeholder( tf.float32, shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)) # 下面这些变量是网络的可训练权值 # conv1 权值维度为 32 x channels x 5 x 5, 32 为特征图数目 conv1_weights = tf.Variable( tf.truncated_normal([5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32. stddev=0.1, seed=SEED)) # conv1 偏置 conv1_biases = tf.Variable(tf.zeros([32])) # conv2 权值维度为 64 x 32 x 5 x 5 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])) # 全连接层 fc1 权值，神经元数目为512 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 权值，维度与标签类数目一致 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])) # 两个网络：训练网络和评测网络 # 它们共享权值 # 实现 LeNet-5 模型，该函数输入为数据，输出为fc2的响应 # 第二个参数区分训练网络还是评测网络 def model(data, train=False): # 二维卷积，使用“不变形”补零（即输出特征图与输入尺寸一致）。 conv = tf.nn.conv2d(data, conv1_weights, strides=[1, 1, 1, 1], padding='SAME') # 加偏置、过激活函数一块完成 relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases)) # 最大值下采样 pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # 第二个卷积层 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') # 特征图变形为2维矩阵，便于送入全连接层 pool_shape = pool.get_shape().as_list() reshape = tf.reshape( pool, [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]]) # 全连接层，注意“+”运算自动广播偏置 hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases) # 训练阶段，增加 50% dropout；而评测阶段无需该操作 if train: hidden = tf.nn.dropout(hidden, 0.5, seed=SEED) return tf.matmul(hidden, fc2_weights) + fc2_biases # 训练阶段计算： 对数+交叉熵 损失函数 logits = model(train_data_node, True) loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits, train_labels_node)) # 全连接层参数进行 L2 正则化 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)) # 将正则项加入损失函数 loss += 5e-4 * regularizers # 优化器： 设置一个变量，每个批处理递增，控制学习速率衰减 batch = tf.Variable(0) # 指数衰减，每经过train_size (此处为一个epoch)，学习率变为原来的0.95 # 默认staircase=False，此时返回一个不断变化的学习率 # learning_rate *decay_rate ^ (global_step / decay_steps) # 默认staircase=True时返回平滑的学习率 learning_rate = tf.train.exponential_decay( 0.01, # 基本学习速率， learning_rate batch * BATCH_SIZE, # 当前批处理在数据全集中的位置， global step 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) # 用softmax 计算训练批处理的预测概率 train_prediction = tf.nn.softmax(logits) # 用 softmax 计算评测批处理的预测概率 eval_prediction = tf.nn.softmax(model(eval_data)) # 使用小batch兼顾速度和收敛，降低对性能的要求 def eval_in_batches(data, sess): 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 # 创建一个本地会话来运行训练 start_time = time.time() with tf.Session() as sess: # 初始化 tf.initialize_all_variables().run() print('Initialized!') # 循环 for step in xrange(int(num_epochs * train_size) // BATCH_SIZE): # 在epoch之间，采用随机序列更好（此处未使用） offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE) batch_data = train_data[offset:(offset + BATCH_SIZE), ...] batch_labels = train_labels[offset:(offset + BATCH_SIZE)] # feed_dict存储变量字典，用于接受minibatch数据 feed_dict = {train_data_node: batch_data, train_labels_node: batch_labels} # 运行图，返回节点 _, 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() # 打印结果 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,) # 程序入口点 if __name__ == '__main__': tf.app.run() """ Running self-test. Initialized! Step 0 (epoch 0.00), 30.1 ms Minibatch loss: 9.898, learning rate: 0.010000 Minibatch error: 93.8% Validation error: 0.0% Test error: 0.0% test_error 0.0 """ """ Step 0 (epoch 0.00), 14.0 ms Minibatch loss: 12.149, learning rate: 0.010000 Minibatch error: 90.6% Validation error: 84.1% . . . . . . Step 8500 (epoch 9.89), 78.1 ms Minibatch loss: 1.631, learning rate: 0.006302 Minibatch error: 1.6% Validation error: 0.9% Test error: 0.8% """