利用 VGG（实战） 实现花的分类
1.处理数据
def creat_tf(imgpath):
classes = os.listdir(imgpath)
writer = tf.compat.v1.python_io.TFRecordWriter("train.tfrecords")
for index, name in enumerate(classes):
class_path = imgpath + name
if os.path.isdir(class_path):
for img_name in os.listdir(class_path):
img_path = class_path + '/' + img_name
img = Image.open(img_path)
# you can improve, not resize
img = img.resize((224, 224))
img_raw = img.tobytes()
example = tf.train.Example(features=tf.train.Features(feature={
'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[int(name)])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
}))
writer.write(example.SerializeToString())
writer.close()
2. 构建神经网络
data_dict = np.load('vgg16.npy', allow_pickle=True, encoding='latin1').item()
def print_layer(t):
print(t.op.name, ' ', t.get_shape().as_list(), 'n')
def conv(x, out_channel, name, finetune=False):
in_channel = x.get_shape()[-1].value
with tf.name_scope(name) as scope:
if finetune:
weight = tf.constant(data_dict[name][0], name="weights")
bias = tf.constant(data_dict[name][1], name="bias")
print("finetune")
else:
weight = tf.Variable(tf.truncated_normal([3, 3, in_channel, out_channel], stddev=0.1), name="weights")
bias = tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[out_channel]), trainable=True, name="bias")
print("truncated normal")
conv = tf.nn.conv2d(x, weight, [1, 1, 1, 1], padding='SAME')
activation = tf.nn.relu(conv + bias, name=scope)
print_layer(activation)
return activation
def maxpool(x, name):
activation = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID', name=name)
print(activation)
return activation
def fc(x, out_channel, name, finetune=False):
in_channel = x.get_shape()[-1].value
with tf.name_scope(name) as scope:
if finetune:
weight = tf.constant(data_dict[name][0], name="weights")
bias = tf.constant(data_dict[name][1], name="bias")
print("finetune")
else:
weight = tf.Variable(tf.random.truncated_normal([in_channel, out_channel], stddev=0.1), name="weights")
bias = tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[out_channel]), trainable=True, name="bias")
print("truncated normal")
# activation = tf.nn.relu_layer(x, weight, bias, name=name)
# print_layer(activation)
# return activation
net = tf.add(tf.matmul(x, weight), bias)
return net
def VGG16(images, _dropout, n_classes):
# conv1
t_start = time.time()
conv1_1 = conv(images, 32, 'conv1_1', finetune=True)
t_end = time.time()
print("*********************************************")
print("t_end-t_start",t_end-t_start)
print("**********************************************")
conv1_2 = conv(conv1_1, 32, 'conv1_2', finetune=True)
pool1 = maxpool(conv1_2, 'pool1')
# conv2
conv2_1 = conv(pool1, 64, 'conv2_1', finetune=True)
conv2_2 = conv(conv2_1, 64, 'conv2_2', finetune=True)
pool2 = maxpool(conv2_2, 'pool2')
# conv3
conv3_1 = conv(pool2, 256, 'conv3_1', finetune=True)
conv3_2 = conv(conv3_1, 256, 'conv3_2', finetune=True)
conv3_3 = conv(conv3_2, 256, 'conv3_3', finetune=True)
pool3 = maxpool(conv3_3, 'pool3')
# conv4
conv4_1 = conv(pool3, 512, 'conv4_1', finetune=True)
conv4_2 = conv(conv4_1, 512, 'conv4_2', finetune=True)
conv4_3 = conv(conv4_2, 512, 'conv4_3', finetune=True)
pool4 = maxpool(conv4_3, 'pool4')
# conv5
conv5_1 = conv(pool4, 512, 'conv5_1', finetune=True)
conv5_2 = conv(conv5_1, 512, 'conv5_2', finetune=True)
conv5_3 = conv(conv5_2, 512, 'conv5_3', finetune=True)
pool5 = maxpool(conv5_3, 'pool5')
# fully connected layer
flatten = tf.reshape(pool5, [-1, 7 * 7 * 512])
fc_6 = fc(flatten, 4096, 'fc_6', finetune=False)
fc_6 = tf.nn.relu(fc_6)
dropout1 = tf.nn.dropout(fc_6, _dropout)
fc_7 = fc(dropout1, 4096, 'fc_7', finetune=False)
fc_7 = tf.nn.relu(fc_7)
dropout2 = tf.nn.dropout(fc_7, _dropout)
fc_8 = fc(dropout2, n_classes, 'fc_8', finetune=False)
return fc_8
3 训练网络
def read_and_decode(filename):
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'img_raw': tf.FixedLenFeature([], tf.string),
})
img = tf.decode_raw(features['img_raw'], tf.uint8)
img = tf.reshape(img, [224, 224, 3])
img = tf.cast(img, tf.float32)
label = tf.cast(features['label'], tf.int64)
return img, label
def train():
x = tf.placeholder(tf.float32, shape=[None, 224, 224, 3], name='input')
y = tf.placeholder(tf.int64, shape=[None, n_classes], name='label')
keep_prob = tf.placeholder(tf.float32)
output = VGG16(x, keep_prob, n_classes)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=y))
train_step = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(loss)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(output, 1), tf.argmax(y, 1)), tf.float32))
images, labels = read_and_decode('./train.tfrecords')
img_batch, label_batch = tf.train.shuffle_batch([images, labels],
batch_size=batch_size,
capacity=512,
min_after_dequeue=200)
label_batch = tf.one_hot(label_batch, n_classes, 1, 0)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
plt_i = []
plot_loss = []
plot_acc = []
fig = plt.figure()
with tf.Session() as sess:
sess.run(init)
#train_writer = tf.summary.FileWriter("D:/py_codes/VGG16-master/VGG16-master/model",sess.graph)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(200):
batch_x, batch_y = sess.run([img_batch, label_batch])
_, loss_val = sess.run([train_step, loss], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
if i % 40 == 0:
train_acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
print("%s: Step [%d] Loss: %f, training accuracy: %g" % (datetime.now(), i, loss_val, train_acc))
# train_writer.add_summary(train_acc,loss_val, i)
plot_loss.append(loss_val)
plot_acc.append(train_acc)
plt_i.append(i)
if (i + 1) == max_steps:
saver.save(sess, './model/model.ckpt', global_step=i)
#train_writer.close()
coord.request_stop()
coord.join(threads)
plt.title("loss")
plt.plot(plot_loss, c='r')
plt.show()
4 测试
def try_a_try(path):
x = tf.placeholder(tf.float32, shape=[None, 224, 224, 3], name='input')
keep_prob = tf.placeholder(tf.float32)
output = VGG16(x, keep_prob, 17)
score = tf.nn.softmax(output)
f_cls = tf.argmax(score, 1)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, './model/model.ckpt-999')
for i in os.listdir(path):
imgpath = os.path.join(path, i)
im = cv2.imread(imgpath)
im = cv2.resize(im, (224, 224))
im = np.expand_dims(im, axis=0)
pred, _score = sess.run([f_cls, score], feed_dict={x: im, keep_prob: 1.0})
prob = round(np.max(_score))
print("{} flowers class is {}, score: {} ".format(i, int(pred), prob))
sess.close()
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