基于TensorFlow實作的CNN神經網路 花卉識別系統Demo
- Demo展示
- 登錄與注冊
- 主頁面
- 模型訓練
- 識別
- 神經網路
- 訓練
- Demo下載
Demo展示
登錄與注冊
主頁面
模型訓練
識別
神經網路
定義CNN網路結構
卷積神經網路，卷積加池化2，全連接2，softmax分類
關鍵代碼：
# 定義函式infence，定義CNN網路結構
# 卷積神經網路，卷積加池化*2，全連接*2，softmax分類
# 卷積層1
def inference(images, batch_size, n_classes):
with tf.variable_scope('conv1') as scope:
weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], stddev=1.0, dtype=tf.float32),
name='weights', dtype=tf.float32)
biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[64]),
name='biases', dtype=tf.float32)
conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(pre_activation, name=scope.name)
# 池化層1
# 3x3最大池化，步長strides為2，池化后執行lrn()操作，區域回應歸一化，對訓練有利，
with tf.variable_scope('pooling1_lrn') as scope:
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')
norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
# 卷積層2
# 16個3x3的卷積核（16通道），padding=’SAME’，表示padding后卷積的圖與原圖尺寸一致，激活函式relu()
with tf.variable_scope('conv2') as scope:
weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 16], stddev=0.1, dtype=tf.float32),
name='weights', dtype=tf.float32)
biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[16]),
name='biases', dtype=tf.float32)
conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(pre_activation, name='conv2')
# 池化層2
# 3x3最大池化，步長strides為2，池化后執行lrn()操作，
# pool2 and norm2
with tf.variable_scope('pooling2_lrn') as scope:
norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')
# 全連接層3
# 128個神經元，將之前pool層的輸出reshape成一行，激活函式relu()
with tf.variable_scope('local3') as scope:
reshape = tf.reshape(pool2, shape=[batch_size, -1])
dim = reshape.get_shape()[1].value
weights = tf.Variable(tf.truncated_normal(shape=[dim, 128], stddev=0.005, dtype=tf.float32),
name='weights', dtype=tf.float32)
biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),
name='biases', dtype=tf.float32)
local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
# 全連接層4
# 128個神經元，激活函式relu()
with tf.variable_scope('local4') as scope:
weights = tf.Variable(tf.truncated_normal(shape=[128, 128], stddev=0.005, dtype=tf.float32),
name='weights', dtype=tf.float32)
biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),
name='biases', dtype=tf.float32)
local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')
# dropout層
# with tf.variable_scope('dropout') as scope:
# drop_out = tf.nn.dropout(local4, 0.8)
# Softmax回歸層
# 將前面的FC層輸出，做一個線性回歸，計算出每一類的得分
with tf.variable_scope('softmax_linear') as scope:
weights = tf.Variable(tf.truncated_normal(shape=[128, n_classes], stddev=0.005, dtype=tf.float32),
name='softmax_linear', dtype=tf.float32)
biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[n_classes]),
name='biases', dtype=tf.float32)
softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')
return softmax_linear
# -----------------------------------------------------------------------------
# loss計算
# 傳入引數：logits，網路計算輸出值，labels，真實值，在這里是0或者1
# 回傳引數：loss，損失值
def losses(logits, labels):
with tf.variable_scope('loss') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='xentropy_per_example')
loss = tf.reduce_mean(cross_entropy, name='loss')
tf.summary.scalar(scope.name + '/loss', loss)
return loss
# --------------------------------------------------------------------------
# loss損失值優化
# 輸入引數：loss，learning_rate，學習速率，
# 回傳引數：train_op，訓練op，這個引數要輸入sess.run中讓模型去訓練，
def trainning(loss, learning_rate):
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = optimizer.minimize(loss, global_step=global_step)
return train_op
# -----------------------------------------------------------------------
# 評價/準確率計算
# 輸入引數：logits，網路計算值，labels，標簽，也就是真實值，在這里是0或者1，
# 回傳引數：accuracy，當前step的平均準確率，也就是在這些batch中多少張圖片被正確分類了，
def evaluation(logits, labels):
with tf.variable_scope('accuracy') as scope:
correct = tf.nn.in_top_k(logits, labels, 1)
correct = tf.cast(correct, tf.float16)
accuracy = tf.reduce_mean(correct)
tf.summary.scalar(scope.name + '/accuracy', accuracy)
return accuracy
訓練
關鍵代碼：
class Train:
path, train_dir, logs_train_dir = None, None, None
def __init__(self):
self.path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
self.train_dir = self.path + '/input_data' # 訓練樣本的讀入路徑
self.logs_train_dir = self.path + '/save' # logs存盤路徑
def train(self, BATCH_SIZE=20, MAX_STEP=1000, learning_rate=0.0001):
# 變數宣告
N_CLASSES = 4 # 四種花型別
IMG_W = 64 # resize影像，太大的話訓練時間久
IMG_H = 64
CAPACITY = 200
# 獲取批次batch
# train, train_label = input_data.get_files(train_dir)
train, train_label, val, val_label = input_data.get_files(self.train_dir, 0.3)
# 訓練資料及標簽
train_batch, train_label_batch = input_data.get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
# 測驗資料及標簽
val_batch, val_label_batch = input_data.get_batch(val, val_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
# 訓練操作定義
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)
# 測驗操作定義
test_logits = model.inference(val_batch, BATCH_SIZE, N_CLASSES)
test_loss = model.losses(test_logits, val_label_batch)
test_acc = model.evaluation(test_logits, val_label_batch)
# 這個是log匯總記錄
summary_op = tf.summary.merge_all()
# 產生一個會話
sess = tf.Session()
# 產生一個writer來寫log檔案
train_writer = tf.summary.FileWriter(self.logs_train_dir, sess.graph)
# 產生一個saver來存盤訓練好的模型
saver = tf.train.Saver()
# 所有節點初始化
sess.run(tf.initialize_all_variables())
# 佇列監控
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# 進行batch的訓練
try:
print('批次為：{}，步數為：{}，學習率為：{}'.format(BATCH_SIZE, MAX_STEP, learning_rate))
# 執行MAX_STEP步的訓練，一步一個batch
for step in np.arange(MAX_STEP + 1):
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
# 每隔50步列印一次當前的loss以及acc，同時記錄log，寫入writer
if step % 10 == 0:
print('步數：%d, loss：%.2f, 訓練準確率：%.2f%%' % (step, tra_loss, tra_acc * 100.0))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
# 每隔100步，保存一次訓練好的模型
if (step) == MAX_STEP:
checkpoint_path = os.path.join(self.logs_train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('到達訓練上限，訓練完成')
finally:
coord.request_stop()
if __name__ == '__main__':
Train().train()
Demo下載
大家喜歡的話，希望可以star一下，謝謝，
https://github.com/JJJiangYH/Flower-Distinguish
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標籤：python
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