基于Tensorflow使用CNN进行图像分类

其他地方看见这个代码，他本身有一些问题，经过修改已经可以运行，对于新手可以参考，练练手。
数据集下载地址：http://download.tensorflow.org/example_images/flower_photos.tgz
五类花。
如果要使用自己的数据集修改path即可。
如果报错说分母出现0,则是数据集数量太小，比例划分后少的地方可能分不到图片。可以通过一些旋转，裁剪等方法扩大数据集。或者修改比例划分系数ratio。

# -*- coding: utf-8 -*-

from skimage import io, transform
import glob
import os
import tensorflow as tf
import numpy as np
import time
#Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 报错忽略
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]='2' # 只显示 warning 和 Error

#图片位置
path = 'F:\\flower_photos\\'

# 将所有的图片resize成100*100
w = 100
h = 100
c = 3

# 读取图片
def read_img(path):
cate = [path +'/'+ x for x in os.listdir(path) if os.path.isdir(path +'/'+ x)]
imgs = []
labels = []
for idx, folder in enumerate(cate):
for im in glob.glob(folder + '/*.jpg'):
print('reading the images:%s' % (im))
img = io.imread(im)
img = transform.resize(img, (w, h))
imgs.append(img)
labels.append(idx)
return np.asarray(imgs, np.float32), np.asarray(labels, np.int32)

data, label = read_img(path)

# 打乱顺序
num_example = data.shape[0]
arr = np.arange(num_example)
np.random.shuffle(arr)
data = data[arr]
label = label[arr]

# 将所有数据分为训练集和验证集
ratio = 0.8
s = np.int(num_example * ratio)
x_train = data[:s]
y_train = label[:s]
x_val = data[s:]
y_val = label[s:]

# -----------------构建网络----------------------
# 占位符
x = tf.placeholder(tf.float32, shape=[None, w, h, c], name='x')
y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_')

# 第一个卷积层（100——>50)
conv1 = tf.layers.conv2d(
inputs=x,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

# 第二个卷积层(50->25)
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

# 第三个卷积层(25->12)
conv3 = tf.layers.conv2d(
inputs=pool2,
filters=128,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)

# 第四个卷积层(12->6)
conv4 = tf.layers.conv2d(
inputs=pool3,
filters=128,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool4 = tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2)

re1 = tf.reshape(pool4, [-1, 6 * 6 * 128])

# 全连接层
dense1 = tf.layers.dense(inputs=re1,
units=1024,
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
dense2 = tf.layers.dense(inputs=dense1,
units=512,
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
logits = tf.layers.dense(inputs=dense2,
units=5,
activation=None,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
# ---------------------------网络结束---------------------------

loss = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 定义一个函数，按批次取数据
def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
if shuffle:
excerpt = indices[start_idx:start_idx + batch_size]
else:
excerpt = slice(start_idx, start_idx + batch_size)
yield inputs[excerpt], targets[excerpt]

# 训练和测试数据，可将n_epoch设置更大一些

n_epoch = 10
batch_size = 64
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
for epoch in range(n_epoch):
start_time = time.time()

# training
train_loss, train_acc, n_batch = 0, 0, 0
for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
_, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})
train_loss += err
train_acc += ac
n_batch += 1
print("   train loss: %f" % (train_loss / n_batch))
print("   train acc: %f" % (train_acc / n_batch))

# validation
val_loss, val_acc, n_batch = 0, 0, 0
for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
err, ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a})
val_loss += err
val_acc += ac
n_batch += 1
print("   validation loss: %f" % (val_loss / n_batch))
print("   validation acc: %f" % (val_acc / n_batch))

sess.close()