粤车牌abcdef的排列顺序应该如何，以满足百度优化标准？，

# image = convert2gray(image)

# batch_x = image.flatten() / 255 # (image.flatten()-128)/128 mean为0

batch_x = image.flatten()

batch_y = text2vec(text)

return batch_x, batch_y

def text2vec(text): text_len = len(text) if text_len > MAX_NP: raise ValueError('验证码最长4个字符') vector = np.zeros(MAX_NP * CHAR_SET_LEN) 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: # 说明是中文字 k = dicthz # raise ValueError('No Map') return k for i, c in enumerate(text): #print text idx = i * CHAR_SET_LEN + char2pos(c) # print("idx" + str(idx)) #print i,CHAR_SET_LEN,char2pos(c),idx vector = 1 return vector# 向量转回文本def vec2text(vec): # char_pos = vec.nonzero()<0> char_pos = vec 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') zm = chr(char_code) elif char_idx < 36: char_code = char_idx - 10 + ord('A') zm = chr(char_code) elif char_idx < 62: char_code = char_idx - 36 + ord('a') zm = chr(char_code) elif char_idx == 62: char_code = ord('_') zm = chr(char_code) else: zm = "N" # 说明是中文 for key in dicthz: if char_idx == dicthz: zm = key # raise ValueError('error') text.append(zm) return "".join(text)

X = tf.placeholder(tf.float32, ) #None代表不限条数的输入Y = tf.placeholder(tf.float32, )keep_prob = tf.placeholder(tf.float32) # dropoutdef variable_summaries(var, name): """Attach a lot of summaries to a Tensor.""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean/' + name, mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean))) tf.summary.scalar('sttdev/' + name, stddev) tf.summary.scalar('max/' + name, tf.reduce_max(var)) tf.summary.scalar('min/' + name, tf.reduce_min(var)) tf.summary.histogram(name, var)def detect_np_cnn(X, keep_prob, w_alpha=0.01, b_alpha=0.1): with tf.name_scope('image_input'): x = tf.reshape(X, shape=<-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1>) tf.summary.image('image_input', x, 3)

with tf.name_scope('input_cnn_filter1'): with tf.name_scope('input_weight1'): w_c1 = tf.Variable(tf.truncated_normal(<3, 3, 1, 32>, stddev=0.1)) variable_summaries(w_c1, 'input_cnn_filter1/input_weight1') with tf.name_scope('input_biases1'): b_c1 = tf.Variable(tf.constant(0.1, shape=<32>)) variable_summaries(b_c1, 'input_cnn_filter1/input_biases1') conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=<1, 1, 1, 1>, padding='SAME'), b_c1)) tf.summary.histogram('input_cnn_filter1', conv1) 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)# print(conv1.get_shape())with tf.name_scope('input_cnn_filter2'): with tf.name_scope('input_weight2'): w_c2 = tf.Variable(tf.truncated_normal(<3, 3, 32, 64>, stddev=0.1)) variable_summaries(w_c2, 'input_cnn_filter2/input_weight2') with tf.name_scope('input_biases2'): b_c2 = tf.Variable(tf.constant(0.1, shape=<64>)) variable_summaries(b_c2, 'input_cnn_filter2/input_biases2')conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=<1, 1, 1, 1>, padding='SAME'), b_c2))tf.summary.histogram('input_cnn_filter2', conv2)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)# print(conv2.get_shape())with tf.name_scope('input_cnn_filter3'): with tf.name_scope('input_weight3'): w_c3 = tf.Variable(tf.truncated_normal(<3, 3, 64, 64>, stddev=0.1)) variable_summaries(w_c3, 'input_cnn_filter3/input_weight3') with tf.name_scope('input_biases3'): b_c3 = tf.Variable(tf.constant(0.1, shape=<64>)) variable_summaries(b_c3, 'input_cnn_filter3/input_biases3')conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=<1, 1, 1, 1>, padding='SAME'), b_c3))tf.summary.histogram('input_cnn_filter1', conv3)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)# print(conv3.get_shape())# Fully connected layerwith tf.name_scope('input_fully_layer'): with tf.name_scope('input_fully_weight'): w_d = tf.Variable(tf.truncated_normal(<5 * 13 * 64, 1024>, stddev=0.1)) variable_summaries(w_d, 'input_fully_layer/input_fully_weight') with tf.name_scope('input_fully_biases'): b_d = tf.Variable(tf.constant(0.1, shape=<1024>)) variable_summaries(b_d, 'input_fully_layer/input_fully_biases')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))tf.summary.histogram('input_fully_layer', dense)dense = tf.nn.dropout(dense, keep_prob)with tf.name_scope('output'): with tf.name_scope('output_w'): w_out = tf.Variable(tf.truncated_normal(<1024, MAX_NP * CHAR_SET_LEN>, stddev=0.1)) variable_summaries(w_out, 'output/output_w') with tf.name_scope('output_b'): b_out = tf.Variable(tf.constant(0.1, shape=)) variable_summaries(b_out, 'output/output_b')out = tf.add(tf.matmul(dense, w_out), b_out)tf.summary.histogram('output', out)# out = tf.nn.softmax(out)return out

traindata, testdata = getSplitData(PATH)def train_detect_np_cnn(max_step=200): X = tf.placeholder(tf.float32, ) Y = tf.placeholder(tf.float32, ) keep_prob = tf.placeholder(tf.float32) # dropout output = detect_np_cnn(X, keep_prob) # loss # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=Y)) with tf.name_scope('loss'): loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y)) tf.summary.scalar('loss', loss) # 可视化观看常量 # 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 with tf.name_scope('train'): optimizer = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(loss) predict = tf.reshape(output, <-1, MAX_NP, CHAR_SET_LEN>) max_idx_p = tf.argmax(predict, 2) YY = tf.reshape(Y, <-1, MAX_NP, CHAR_SET_LEN>) max_idx_l = tf.argmax(YY, 2) correct_pred = tf.equal(max_idx_p, max_idx_l) with tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) tf.summary.scalar('accuracy', accuracy) # 可视化观看常量 saver = tf.train.Saver() with tf.Session() as sess: # 合并到Summary中 merged = tf.summary.merge_all() # 选定可视化存储目录 writer = tf.summary.FileWriter("/Users/adminjackfy/downloads/trainlibs/chepai/", sess.graph) test_writer = tf.summary.FileWriter("/Users/adminjackfy/downloads/trainlibs/chepai/test/", sess.graph) sess.run(tf.global_variables_initializer()) step = 0 while True: batch_x, batch_y = get_next_batch(sampleTrain(128, traindata)) _, lossSize = sess.run(, feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.8}) # writer.add_summary(summary, step) if step % 5 == 0: print("step is:" + str(step), u"损失函数大小为" + str(lossSize)) batch_x_test, batch_y_test = get_next_batch(testdata) summary, acc = sess.run(, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.}) writer.add_summary(summary, step) # print(sess.run(max_idx_p, feed_dict={X:batch_x_test, Y: batch_y_test, keep_prob: 1.})) # print(sess.run(max_idx_l, feed_dict={X:batch_x_test, Y: batch_y_test, keep_prob: 1.})) print("step is:" + str(step), "acc is :" + str(acc)) if step == max_step: saver.save(sess, "./model/crack_capcha.model") break step += 1 # 训练

def predict(testdata): X = tf.placeholder(tf.float32, ) keep_prob = tf.placeholder(tf.float32) output = detect_np_cnn(X, keep_prob) saver = tf.train.Saver() with tf.Session() as sess: # sess.run(tf.global_variables_initializer()) saver.restore(sess, tf.train.latest_checkpoint('./model/')) batch_size = len(testdata) count = 0 for i in range(batch_size): text, image = getImageAndName(testdata) # image = convert2gray(image) # captcha_image = image.flatten() / 255 captcha_image = image.flatten() predict = tf.argmax(tf.reshape(output, <-1, MAX_NP, CHAR_SET_LEN>), 2) text_list = sess.run(predict, feed_dict={X: , keep_prob: 1}) predict_text = text_list<0>.tolist() predict_value = vec2text(predict_text) flag = text == predict_value if flag: count += 1 print("真实值: {}, 预测值: {}, 是否相等: {}".format(text, predict_value, flag)) print('\n识别结果: {}/{}={}'.format(count, batch_size, count / batch_size))

if __name__ == '__main__': train_detect_np_cnn(max_step=10000) #训练10000次 #predict(testdata)

采用CPU模式训练一次要一天多，使用GPU模式大几个小时。Accu可以上到90%。