为什么80%的码农都做不了架构师？>>> 
利用卷积神经网络识别mnist手写数字，python程序来源于：
TensorFlow卷积神经网络(CNN)示例 - 高级API
Convolutional Neural Network Example - tf.layers API
CNN网络结构图示
MNIST数据库
More info: http://yann.lecun.com/exdb/mnist/
In [38]:
from __future__ import division, print_function, absolute_import
# Import MNIST data，MNIST数据集导入
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("file://G:/pandas/data", one_hot=False)
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. WARNING:tensorflow:From d:\ide\python\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version. Instructions for updating: Please use tf.data to implement this functionality. Extracting file://G:/pandas/data\train-images-idx3-ubyte.gz Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. WARNING:tensorflow:From d:\ide\python\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version. Instructions for updating: Please use tf.data to implement this functionality. Extracting file://G:/pandas/data\train-labels-idx1-ubyte.gz Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Extracting file://G:/pandas/data\t10k-images-idx3-ubyte.gz Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting file://G:/pandas/data\t10k-labels-idx1-ubyte.gz WARNING:tensorflow:From d:\ide\python\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version. Instructions for updating: Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
In [39]:
# Training Parameters，超参数 learning_rate = 0.001 #学习率 num_steps = 2000 # 训练步数 batch_size = 128 # 训练数据批的大小 # Network Parameters，网络参数 num_input = 784 # MNIST数据输入 (img shape: 28*28) num_classes = 10 # MNIST所有类别 (0-9 digits) dropout = 0.75 # Dropout, probability to keep units，保留神经元相应的概率
In [40]:
# Create the neural network，创建深度神经网络
def conv_net(x_dict, n_classes, dropout, reuse, is_training):
# Define a scope for reusing the variables，确定命名空间
with tf.variable_scope('ConvNet', reuse=reuse):
# TF Estimator类型的输入为像素
x = x_dict['images']
# MNIST数据输入格式为一位向量，包含784个特征 (28*28像素)
# 用reshape函数改变形状以匹配图像的尺寸 [高 x 宽 x 通道数]
# 输入张量的尺度为四维: [(每一)批数据的数目, 高，宽，通道数]
x = tf.reshape(x, shape=[-1, 28, 28, 1])
# 卷积层，32个卷积核，尺寸为5x5
conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)
# 最大池化层，步长为2，无需学习任何参量
conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
# 卷积层，32个卷积核，尺寸为5x5
conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)
# 最大池化层，步长为2，无需学习任何参量
conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
# 展开特征为一维向量，以输入全连接层
fc1 = tf.contrib.layers.flatten(conv2)
# 全连接层
fc1 = tf.layers.dense(fc1, 1024)
# 应用Dropout (训练时打开，测试时关闭)
fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
# 输出层，预测类别
out = tf.layers.dense(fc1, n_classes)
return out
In [41]:
# 确定模型功能 (参照TF Estimator模版)
def model_fn(features, labels, mode):
# 构建神经网络
# 因为dropout在训练与测试时的特性不一，我们此处为训练和测试过程创建两个独立但共享权值的计算图
logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)
logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)
# 预测
pred_classes = tf.argmax(logits_test, axis=1)
pred_probas = tf.nn.softmax(logits_test)
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)
# 确定误差函数与优化器
loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())
# 评估模型精确度
acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)
# TF Estimators需要返回EstimatorSpec
estim_specs = tf.estimator.EstimatorSpec(
mode=mode,
predictions=pred_classes,
loss=loss_op,
train_op=train_op,
eval_metric_ops={'accuracy': acc_op})
return estim_specs
In [43]:
# 构建Estimator model = tf.estimator.Estimator(model_fn)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\XUESON~1\AppData\Local\Temp\tmpdaj442mr
INFO:tensorflow:Using config: {'_model_dir': 'C:\\Users\\XUESON~1\\AppData\\Local\\Temp\\tmpdaj442mr', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
In [44]:
# 确定训练输入函数
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.train.images}, y=mnist.train.labels,
batch_size=batch_size, num_epochs=None, shuffle=True)
# 开始训练模型
model.train(input_fn, steps=num_steps)
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 1 into C:\Users\XUESON~1\AppData\Local\Temp\tmpdaj442mr\model.ckpt. INFO:tensorflow:loss = 2.333311, step = 1 INFO:tensorflow:global_step/sec: 4.39831 INFO:tensorflow:loss = 0.17404273, step = 101 (22.736 sec) INFO:tensorflow:global_step/sec: 3.77397 INFO:tensorflow:loss = 0.11679428, step = 201 (26.497 sec) INFO:tensorflow:global_step/sec: 4.31603 INFO:tensorflow:loss = 0.13766995, step = 301 (23.169 sec) INFO:tensorflow:global_step/sec: 4.15033 INFO:tensorflow:loss = 0.10576604, step = 401 (24.094 sec) INFO:tensorflow:global_step/sec: 3.94188 INFO:tensorflow:loss = 0.021022122, step = 501 (25.415 sec) INFO:tensorflow:global_step/sec: 3.85337 INFO:tensorflow:loss = 0.014713779, step = 601 (25.905 sec) INFO:tensorflow:global_step/sec: 4.46202 INFO:tensorflow:loss = 0.10044412, step = 701 (22.410 sec) INFO:tensorflow:global_step/sec: 4.40926 INFO:tensorflow:loss = 0.022443363, step = 801 (22.680 sec) INFO:tensorflow:global_step/sec: 4.38373 INFO:tensorflow:loss = 0.011833555, step = 901 (22.812 sec) INFO:tensorflow:global_step/sec: 4.67343 INFO:tensorflow:loss = 0.015882235, step = 1001 (21.413 sec) INFO:tensorflow:global_step/sec: 3.90732 INFO:tensorflow:loss = 0.07513487, step = 1101 (25.593 sec) INFO:tensorflow:global_step/sec: 3.46564 INFO:tensorflow:loss = 0.03496183, step = 1201 (28.842 sec) INFO:tensorflow:global_step/sec: 3.71127 INFO:tensorflow:loss = 0.0319542, step = 1301 (26.958 sec) INFO:tensorflow:global_step/sec: 3.80743 INFO:tensorflow:loss = 0.04076298, step = 1401 (26.252 sec) INFO:tensorflow:global_step/sec: 3.63793 INFO:tensorflow:loss = 0.072247826, step = 1501 (27.501 sec) INFO:tensorflow:global_step/sec: 4.26565 INFO:tensorflow:loss = 0.016876796, step = 1601 (23.443 sec) INFO:tensorflow:global_step/sec: 4.18978 INFO:tensorflow:loss = 0.10762453, step = 1701 (23.855 sec) INFO:tensorflow:global_step/sec: 3.6478 INFO:tensorflow:loss = 0.05514864, step = 1801 (27.411 sec) INFO:tensorflow:global_step/sec: 4.18436 INFO:tensorflow:loss = 0.027144967, step = 1901 (23.899 sec) INFO:tensorflow:Saving checkpoints for 2000 into C:\Users\XUESON~1\AppData\Local\Temp\tmpdaj442mr\model.ckpt. INFO:tensorflow:Loss for final step: 0.010040723.
Out[44]:
In [45]:
# 评判模型
# 确定评判用输入函数
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.test.images}, y=mnist.test.labels,
batch_size=batch_size, shuffle=False)
model.evaluate(input_fn)
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2018-05-17-01:36:50 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\XUESON~1\AppData\Local\Temp\tmpdaj442mr\model.ckpt-2000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Finished evaluation at 2018-05-17-01:36:57 INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.992, global_step = 2000, loss = 0.03843919
Out[45]:
{'accuracy': 0.992, 'global_step': 2000, 'loss': 0.03843919}
In [46]:
# 预测单个图像
n_images = 4
# 从数据集得到测试图像
test_images = mnist.test.images[:n_images]
# 准备输入数据
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': test_images}, shuffle=False)
# 用训练好的模型预测图片类别
preds = list(model.predict(input_fn))
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from C:\Users\XUESON~1\AppData\Local\Temp\tmpdaj442mr\model.ckpt-2000 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op.
输入图片
In [49]:
for i in range(n_images):
plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
print("Model prediction:", preds[i])
输出：
Model prediction: 7
Model prediction: 2
Model prediction: 1
Model prediction: 0