CNN猫狗识别

train.py

import dataset

import tensorflow as tf

import time

from datetime import timedelta

import math

import random

import numpy as np

from numpy.random import seed

seed(10)

from tensorflow import set_random_seed

set_random_seed(20)

batch_size = 32

classes = ['dogs','cats']

num_classes = len(classes)

validation_size = 0.2

img_size = 64

num_channels = 3

train_path='D:/hh/twst/test/training_data'

data = dataset.read_train_sets(train_path, img_size, classes, validation_size=validation_size)

print("Complete reading input data. Will Now print a snippet of it")

print("Number of files in Training-set:tt{}".format(len(data.train.labels)))

print("Number of files in Validation-set:t{}".format(len(data.valid.labels)))

session = tf.Session()

x = tf.placeholder(tf.float32, shape=[None, img_size,img_size,num_channels], name='x')

y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')

y_true_cls = tf.argmax(y_true, dimension=1)

filter_size_conv1 = 3

num_filters_conv1 = 32

filter_size_conv2 = 3

num_filters_conv2 = 32

filter_size_conv3 = 3

num_filters_conv3 = 64

fc_layer_size = 1024

def create_weights(shape):

return tf.Variable(tf.truncated_normal(shape, stddev=0.05))

def create_biases(size):

return tf.Variable(tf.constant(0.05, shape=[size]))

def create_convolutional_layer(input,

num_input_channels,

conv_filter_size,

num_filters):

weights = create_weights(shape=[conv_filter_size, conv_filter_size, num_input_channels, num_filters])

biases = create_biases(num_filters)

layer = tf.nn.conv2d(input=input,

filter=weights,

strides=[1, 1, 1, 1],

padding='SAME')

layer += biases

layer = tf.nn.relu(layer)