补充运行说明，微调结构

1.验证码/tensorflow_cnn/README.md

@@ -7,7 +7,11 @@
 
 
 直接运行 ：
-python TensorFlow_cnn.py
+python TensorFlow_cnn_train.py
+则开始训练，当前成功率设定为50%则停止，
+训练完毕确保训练后模型在同一文件夹下则运行
+python TensorFlow_cnn_test_model.py
+实时生成新验证码并且进行预测
 
 当前程序设定一个成功率0.5或者0.7
 

1.验证码/tensorflow_cnn/tensorflow_cnn_test_model.py

@@ -0,0 +1,224 @@
+#coding:utf-8
+from gen_captcha import gen_captcha_text_and_image
+from gen_captcha import number
+from gen_captcha import alphabet
+from gen_captcha import ALPHABET
+
+import numpy as np
+import tensorflow as tf
+
+text, image = gen_captcha_text_and_image()
+print("验证码图像channel:", image.shape)  # (60, 160, 3)
+# 图像大小
+IMAGE_HEIGHT = 60
+IMAGE_WIDTH = 160
+MAX_CAPTCHA = len(text)
+print("验证码文本最长字符数", MAX_CAPTCHA)   # 验证码最长4字符; 我全部固定为4,可以不固定. 如果验证码长度小于4，用'_'补齐
+
+# 把彩色图像转为灰度图像（色彩对识别验证码没有什么用）
+def convert2gray(img):
+	if len(img.shape) > 2:
+		gray = np.mean(img, -1)
+		# 上面的转法较快，正规转法如下
+		# r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
+		# gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
+		return gray
+	else:
+		return img
+
+"""
+cnn在图像大小是2的倍数时性能最高, 如果你用的图像大小不是2的倍数，可以在图像边缘补无用像素。
+np.pad(image【,((2,3),(2,2)), 'constant', constant_values=(255,))  # 在图像上补2行，下补3行，左补2行，右补2行
+"""
+
+# 文本转向量
+char_set = number + alphabet + ALPHABET + ['_']  # 如果验证码长度小于4, '_'用来补齐
+CHAR_SET_LEN = len(char_set)
+def text2vec(text):
+	text_len = len(text)
+	if text_len > MAX_CAPTCHA:
+		raise ValueError('验证码最长4个字符')
+
+	vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN) #生成一个默认为0的向量
+	def char2pos(c):
+		if c =='_':
+			k = 62
+			return k
+		k = ord(c)-48
+		if k > 9:
+			k = ord(c) - 55
+			if k > 35:
+				k = ord(c) - 61
+				if k > 61:
+					raise ValueError('No Map')
+		return k
+	for i, c in enumerate(text):
+		idx = i * CHAR_SET_LEN + char2pos(c)
+		vector[idx] = 1
+	return vector
+# 向量转回文本
+def vec2text(vec):
+	char_pos = vec.nonzero()[0]
+	text=[]
+	for i, c in enumerate(char_pos):
+		char_at_pos = i #c/63
+		char_idx = c % CHAR_SET_LEN
+		if char_idx < 10:
+			char_code = char_idx + ord('0')
+		elif char_idx <36:
+			char_code = char_idx - 10 + ord('A')
+		elif char_idx < 62:
+			char_code = char_idx-  36 + ord('a')
+		elif char_idx == 62:
+			char_code = ord('_')
+		else:
+			raise ValueError('error')
+		text.append(chr(char_code))
+	return "".join(text)
+
+"""
+#向量（大小MAX_CAPTCHA*CHAR_SET_LEN）用0,1编码 每63个编码一个字符，这样顺利有，字符也有
+vec = text2vec("F5Sd")
+text = vec2text(vec)
+print(text)  # F5Sd
+vec = text2vec("SFd5")
+text = vec2text(vec)
+print(text)  # SFd5
+"""
+
+# 生成一个训练batchv  一个批次为 默认128 张图片 转换为向量
+def get_next_batch(batch_size=128):
+	batch_x = np.zeros([batch_size, IMAGE_HEIGHT*IMAGE_WIDTH])
+	batch_y = np.zeros([batch_size, MAX_CAPTCHA*CHAR_SET_LEN])
+
+	# 有时生成图像大小不是(60, 160, 3)  反复获取验证码直到该验证码符合标准格式。
+	def wrap_gen_captcha_text_and_image():
+		while True:
+			text, image = gen_captcha_text_and_image()
+			if image.shape == (60, 160, 3):
+				return text, image
+
+	for i in range(batch_size):
+		#获取图片，并灰度转换
+		text, image = wrap_gen_captcha_text_and_image()
+		image = convert2gray(image)
+
+		# flatten 图片一维化 以及对应的文字内容也一维化，形成一个128行每行一个图片及对应文本
+		batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128  mean为0
+		batch_y[i,:] = text2vec(text)
+
+	return batch_x, batch_y
+
+####################################################################
+
+# 申请三个占位符
+X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT*IMAGE_WIDTH])
+Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA*CHAR_SET_LEN])
+keep_prob = tf.placeholder(tf.float32) # dropout
+
+# 定义CNN
+def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
+	x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
+
+	#w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) #
+	#w_c2_alpha = np.sqrt(2.0/(3*3*32))
+	#w_c3_alpha = np.sqrt(2.0/(3*3*64))
+	#w_d1_alpha = np.sqrt(2.0/(8*32*64))
+	#out_alpha = np.sqrt(2.0/1024)
+
+	# 3 conv layer # 3 个 转换层
+	w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 1, 32]))
+	b_c1 = tf.Variable(b_alpha*tf.random_normal([32]))
+	conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
+	conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+	conv1 = tf.nn.dropout(conv1, keep_prob)
+
+	w_c2 = tf.Variable(w_alpha*tf.random_normal([3, 3, 32, 64]))
+	b_c2 = tf.Variable(b_alpha*tf.random_normal([64]))
+	conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
+	conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+	conv2 = tf.nn.dropout(conv2, keep_prob)
+
+	w_c3 = tf.Variable(w_alpha*tf.random_normal([3, 3, 64, 64]))
+	b_c3 = tf.Variable(b_alpha*tf.random_normal([64]))
+	conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
+	conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+	conv3 = tf.nn.dropout(conv3, keep_prob)
+
+	# Fully connected layer  # 最后连接层
+	w_d = tf.Variable(w_alpha*tf.random_normal([8*20*64, 1024]))
+	b_d = tf.Variable(b_alpha*tf.random_normal([1024]))
+	dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
+	dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
+	dense = tf.nn.dropout(dense, keep_prob)
+
+	# 输出层
+	w_out = tf.Variable(w_alpha*tf.random_normal([1024, MAX_CAPTCHA*CHAR_SET_LEN]))
+	b_out = tf.Variable(b_alpha*tf.random_normal([MAX_CAPTCHA*CHAR_SET_LEN]))
+	out = tf.add(tf.matmul(dense, w_out), b_out)
+	#out = tf.nn.softmax(out)
+	return out
+
+# 训练
+def train_crack_captcha_cnn():
+	output = crack_captcha_cnn()
+	# loss 损失数值
+	# loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))
+	loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
+        # 最后一层用来分类的softmax和sigmoid有什么不同？
+	# optimizer 为了加快训练 learning_rate 应该开始大，然后慢慢衰
+	optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
+
+	predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
+	max_idx_p = tf.argmax(predict, 2)
+	max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
+	correct_pred = tf.equal(max_idx_p, max_idx_l)
+	accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
+
+	saver = tf.train.Saver()
+	with tf.Session() as sess:
+		sess.run(tf.global_variables_initializer())
+
+		step = 0
+		while True:
+			batch_x, batch_y = get_next_batch(64)
+			_, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
+			print(step, loss_)
+
+			# 每100 step计算一次准确率
+			if step % 100 == 0:
+				batch_x_test, batch_y_test = get_next_batch(100)
+				acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
+				print(step, acc)
+				# 如果准确率大于50%,保存模型,完成训练
+				if acc > 0.5:
+					saver.save(sess, "crack_capcha.model", global_step=step)
+					break
+			step += 1
+
+def crack_captcha(captcha_image):
+	output = crack_captcha_cnn()
+
+	saver = tf.train.Saver()
+	with tf.Session() as sess:
+		saver.restore(sess, tf.train.latest_checkpoint('.'))
+
+		predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
+		text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
+
+		text = text_list[0].tolist()
+		vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
+		i = 0
+		for n in text:
+				vector[i*CHAR_SET_LEN + n] = 1
+				i += 1
+		return vec2text(vector)
+
+if __name__ == '__main__':
+
+	text, image = gen_captcha_text_and_image()
+	image = convert2gray(image) #生成一张新图
+	image = image.flatten() / 255 # 将图片一维化
+	predict_text = crack_captcha(image) #导入模型识别
+	print("正确: {}  预测: {}".format(text, predict_text))
+	#train_crack_captcha_cnn()