tensorflow 13《TensorFlow実戦Google深さ学習フレームワーク》ノート-06-02 mnist LeNet 5ボリュームニューラルネットワークcode
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01 LeNet 5ボリュームニューラルネットワーク前方伝播
# 《TensorFlow Google 》06
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:LeNet5_infernece.py # LeNet5
import tensorflow as tf
# 1.
INPUT_NODE = 784
OUTPUT_NODE = 10
IMAGE_SIZE = 28
NUM_CHANNELS = 1
NUM_LABELS = 10
CONV1_DEEP = 32
CONV1_SIZE = 5
CONV2_DEEP = 64
CONV2_SIZE = 5
FC_SIZE = 512
# 2.
def inference(input_tensor, train, regularizer):
with tf.variable_scope('layer1-conv1'):
conv1_weights = tf.get_variable(
"weight", [CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv1_biases = tf.get_variable("bias", [CONV1_DEEP], initializer=tf.constant_initializer(0.0))
conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
with tf.name_scope("layer2-pool1"):
pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="SAME")
with tf.variable_scope("layer3-conv2"):
conv2_weights = tf.get_variable(
"weight", [CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv2_biases = tf.get_variable("bias", [CONV2_DEEP], initializer=tf.constant_initializer(0.0))
conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
with tf.name_scope("layer4-pool2"):
pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
pool_shape = pool2.get_shape().as_list()
nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
reshaped = tf.reshape(pool2, [pool_shape[0], nodes])
with tf.variable_scope('layer5-fc1'):
fc1_weights = tf.get_variable("weight", [nodes, FC_SIZE],
initializer=tf.truncated_normal_initializer(stddev=0.1))
if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
fc1_biases = tf.get_variable("bias", [FC_SIZE], initializer=tf.constant_initializer(0.1))
fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
if train: fc1 = tf.nn.dropout(fc1, 0.5)
with tf.variable_scope('layer6-fc2'):
fc2_weights = tf.get_variable("weight", [FC_SIZE, NUM_LABELS],
initializer=tf.truncated_normal_initializer(stddev=0.1))
if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
fc2_biases = tf.get_variable("bias", [NUM_LABELS], initializer=tf.constant_initializer(0.1))
logit = tf.matmul(fc1, fc2_weights) + fc2_biases
return logit02 LeNet 5ボリュームニューラルネットワークトレーニング
# 《TensorFlow Google 》06
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:LeNet5_train.py # LeNet5
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import LeNet5_infernece
import os
import numpy as np
# 1.
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 55000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "LeNet5_model/" # LeNet5_model
MODEL_NAME = "LeNet5_model"
# 2.
def train(mnist):
# 4 placeholder
x = tf.placeholder(tf.float32, [
BATCH_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.NUM_CHANNELS],
name='x-input')
y_ = tf.placeholder(tf.float32, [None, LeNet5_infernece.OUTPUT_NODE], name='y-input')
regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
y = LeNet5_infernece.inference(x, True, regularizer)
global_step = tf.Variable(0, trainable=False)
# 、 、 。
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
variables_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,
staircase=True)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
with tf.control_dependencies([train_step, variables_averages_op]):
train_op = tf.no_op(name='train')
# TensorFlow 。
saver = tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
for i in range(TRAINING_STEPS):
xs, ys = mnist.train.next_batch(BATCH_SIZE)
reshaped_xs = np.reshape(xs, (
BATCH_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.NUM_CHANNELS))
_, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys})
if i % 1000 == 0:
print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)
# 3.
def main(argv=None):
mnist = input_data.read_data_sets("../../../datasets/MNIST_data", one_hot=True)
train(mnist)
if __name__ == '__main__':
main()
'''
...
After 49001 training step(s), loss on training batch is 0.589334.
After 50001 training step(s), loss on training batch is 0.601423.
After 51001 training step(s), loss on training batch is 0.639142.
After 52001 training step(s), loss on training batch is 0.610477.
After 53001 training step(s), loss on training batch is 0.58531.
After 54001 training step(s), loss on training batch is 0.626083.
'''03 LeNet 5ボリュームニューラルネットワークテスト
# 《TensorFlow Google 》06
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:LeNet5_eval.py #
import time
import math
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import LeNet5_infernece
import LeNet5_train
def evaluate(mnist):
with tf.Graph().as_default() as g:
# 4 placeholder
x = tf.placeholder(tf.float32, [
mnist.test.num_examples,
#LeNet5_train.BATCH_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.NUM_CHANNELS],
name='x-input')
y_ = tf.placeholder(tf.float32, [None, LeNet5_infernece.OUTPUT_NODE], name='y-input')
validate_feed = {x: mnist.test.images, y_: mnist.test.labels}
global_step = tf.Variable(0, trainable=False)
regularizer = tf.contrib.layers.l2_regularizer(LeNet5_train.REGULARIZATION_RATE)
y = LeNet5_infernece.inference(x, False, regularizer)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
variable_averages = tf.train.ExponentialMovingAverage(LeNet5_train.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
#n = math.ceil(mnist.test.num_examples / LeNet5_train.BATCH_SIZE)
n = math.ceil(mnist.test.num_examples / mnist.test.num_examples)
for i in range(n):
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(LeNet5_train.MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
xs, ys = mnist.test.next_batch(mnist.test.num_examples)
#xs, ys = mnist.test.next_batch(LeNet5_train.BATCH_SIZE)
reshaped_xs = np.reshape(xs, (
mnist.test.num_examples,
#LeNet5_train.BATCH_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.IMAGE_SIZE,
LeNet5_infernece.NUM_CHANNELS))
accuracy_score = sess.run(accuracy, feed_dict={x:reshaped_xs, y_:ys})
print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score))
else:
print('No checkpoint file found')
return
#
def main(argv=None):
mnist = input_data.read_data_sets("../../../datasets/MNIST_data", one_hot=True)
evaluate(mnist)
if __name__ == '__main__':
main()
'''
After 54001 training step(s), test accuracy = 0.9915
'''