pattern_recognition

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("MNIST_data", one_hot=False)

import tensorflow as tf import matplotlib.pyplot as plt import numpy as np

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 = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25

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']

Import MNIST data，MNIST数据集导入

from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data", one_hot=False)

import tensorflow as tf import matplotlib.pyplot as plt import numpy as np

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 = (1-p)，保留神经元相应的概率为(1-p)=(1-0.75)=0.25

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，64个卷积核，尺寸为3x3
    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

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

构建Estimator

model = tf.estimator.Estimator(model_fn)

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)

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)

预测单个图像

n_images = 6

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))

可视化显示

for i in range(n_images): plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') plt.show() print("Model prediction:", preds[i])

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))

可视化显示

for i in range(n_images): plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') plt.show() print("Model prediction:", preds[i])

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))

可视化显示

for i in range(n_images): plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray') plt.show() print("Model prediction:", preds[i])