深度學習筆記——TensorFlow學習筆記(三)使用TensorFlow實現的神經網路進行MNIST手寫體數字識別
阿新 • • 發佈:2019-01-16
本文是TensorFlow學習的第三部分,參考的是《TensorFlow實戰Google深度學習框架》一書,這部分講述的是使用TensorFlow實現的神經網路進行MNIST手寫體數字識別一個例項。
這個例項將第二部分講述的啟用函式、損失函式、優化演算法、正則化等都運用上了。同時,使用TensorFlow中利用變數名稱來建立/獲取變數的機制將前向傳播的過程抽象出來,使得訓練和測試時不需要關心神經網路的結構或是引數;還使用了TensorFlow儲存模型的方法將模型持久化(儲存),以及載入模型進行預測。
總結來說,將神經網路的訓練、測試和使用拆分成了不同的程式,並且將神經網路的前向傳播過程抽象成了一個獨立的庫函式,通過這種方式可以將訓練過程和測試。使用過程解耦合,從而使得整個過程更加靈活。
具體程式碼如下:
神經網路前向傳播過程程式碼(mnist_inference.py):
#coding: utf-8 import tensorflow as tf #define the variables of nerual network INPUT_NODE = 784 OUTPUT_NODE = 10 LAYER1_NODE = 500 #通過tf.get_variable函式來獲取變數,在訓練時會建立這些變數,在測試時會通過儲存的模型載入這些變數的取值。因為可以在變數載入時將滑動平均變數重新命名,所以可以直接通過同樣的名字在訓練時使用變數自身,而在測試時使用變數的滑動平均值。這個函式也會將變數的正則化損失加入損失集合。 def get_weight_variable(shape, regularizer): weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1)) if regularizer != None: tf.add_to_collection('losses', regularizer(weights)) return weights #define the forward network def inference(input_tensor, regularizer): with tf.variable_scope('layer1'):#宣告第一層神經網路的變數並完成前向傳播過程 weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer) biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0)) layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases) with tf.variable_scope('layer2'):#宣告第二層神經網路的變數並完成前向傳播過程 weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer) biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0)) layer2 = tf.matmul(layer1, weights) + biases return layer2
訓練過程的程式碼(minist_train.py):
#coding: utf-8 import os import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import mnist_inference BATCH_SIZE = 100 LEARNING_RATE_BASE = 0.8 LEARNING_RATE_DECAY = 0.99 REGULARAZTION_RATE = 0.0001 TRAINING_STEPS = 30000 MOVING_AVERAGE_DECAY = 0.99 MODEL_SAVE_PATH = "/home/mpk/TensorFlow_learning/MNIST_MLP/model/"#模型儲存的路徑 MODEL_NAME = "model.ckpt" def train(mnist): x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input') y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input') regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE) #直接使用mnist_inference.py中定義的前向傳播過程 y = mnist_inference.inference(x, regularizer) global_step = tf.Variable(0, trainable=False) #定義損失函式、指數衰減學習率、滑動平均操作以及訓練過程 variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) variable_averages_op = variable_averages.apply(tf.trainable_variables()) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1)) cross_entropy_mean = tf.reduce_mean(cross_entropy) loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses')) learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY) train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) with tf.control_dependencies([train_step, variable_averages_op]): train_op = tf.no_op(name='train') #初始化TensorFlow持久化類 saver = tf.train.Saver() with tf.Session() as sess: tf.initialize_all_variables().run() #訓練過程 for i in range(TRAINING_STEPS): xs, ys = mnist.train.next_batch(BATCH_SIZE) _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys}) #每1000輪儲存一次模型 if i % 1000 == 0: print("After %d training step(s), loss on training batch is %g." % (step, loss_value)) print os.path.join(MODEL_SAVE_PATH, MODEL_NAME) saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step) def main(argv=None): mnist = input_data.read_data_sets("/tmp/data", one_hot=True) train(mnist) if __name__ == '__main__': tf.app.run()
訓練結果如下圖所示:
測試過程(mnist_eval.py):
#coding: utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference
import minist_train
#every 10 sec load the newest model
EVAL_INTERVAL_SECS = 10
def evaluate(mnist):
with tf.Graph().as_default() as g:
x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')
validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
#直接呼叫封裝好的函式來計算前向傳播的結果
y = mnist_inference.inference(x, None)
#計算正確率
correcgt_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correcgt_prediction, tf.float32))
#通過變數重新命名的方式載入模型
variable_averages = tf.train.ExponentialMovingAverage(minist_train.MOVING_AVERAGE_DECAY)
variable_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variable_to_restore)
#每隔10秒呼叫一次計算正確率的過程以檢測訓練過程中正確率的變化
while True:
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(minist_train.MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
#load the model
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
accuracy_score = sess.run(accuracy, feed_dict=validate_feed)
print("After %s training steps, validation accuracy = %g" % (global_step, accuracy_score))
else:
print('No checkpoint file found')
return
time.sleep(EVAL_INTERVAL_SECS)
def main(argv=None):
mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
evaluate(mnist)
if __name__ == '__main__':
tf.app.run()
測試結果如下圖:
程式碼也可以在我的GitHub獲取:點選開啟連結