'''
查找流量图的关键边，输出关键边的数量即可
'''


from typing import List
from collections import deque

class FortdFulkerson:
    # 输入图中所有边列表[(from1, to1, weight1), (form2, to2, weight2), ......]
    # 边可以有重边和自环边
    # souece_node 和 end_node 分别为源点和汇点
    # 所有节点从1开始连续编号, max_node_num是最大节点数，max_edge_nums是最大边数
    def __init__(self, edges, source_node, end_node, max_node_num, max_edge_num):
        self.edges = edges[::]
        self.source_node = source_node
        self.end_node = end_node
        self.max_edge_num = max_edge_num
        self.max_node_num = max_node_num

    # 获取关键路径的标号列表，标号从0开始编号
    def getKeyEdges(self):
        e = [-1] * (self.max_edge_num * 2 + 1)  # e[idx]表示编号为idx残量图边的终点, idx//2 就是残量图边对应的原图边的编号
        f = [-1] * (self.max_edge_num * 2 + 1)  # f[idx]表示编号为idx的残量图边的流量
        ne = [-1] * (self.max_edge_num * 2 + 1)  # ne[idx]表示根编号为idx的边同一个起点的下一条边的编号
        h = [-1] * (self.max_node_num + 1)  # h[a]表示节点a为起点的所有边的链表头对应的边的编号
        dis = [-1] * (self.max_node_num + 1)  # dis[a]表示点a到源点的距离，用于记录分层图信息
        cur = [-1] * (self.max_node_num + 1)  # cur[a]表示节点a在dfs搜索中第一次开始搜索的边的下标，也称当前弧，用于优化dfs速度
        orig_flow = [0] * (self.max_edge_num + 1)  # 原图中有向边的流量

        idx = 0
        for a, b, w in self.edges:
            e[idx], f[idx], ne[idx], h[a] = b, w, h[a], idx
            idx += 1
            e[idx], f[idx], ne[idx], h[b] = a, 0, h[b], idx
            idx += 1

        # bfs搜索有没有增广路
        def bfs() -> bool:
            for i in range(self.max_node_num + 1):
                dis[i] = -1

            que = deque()
            que.append(self.source_node)
            dis[self.source_node] = 0
            cur[self.source_node] = h[self.source_node]

            while len(que) > 0:
                cur_node = que.popleft()
                idx = h[cur_node]
                while idx != -1:
                    next_node = e[idx]
                    if dis[next_node] == -1 and f[idx] > 0:
                        dis[next_node] = dis[cur_node] + 1
                        cur[next_node] = h[next_node]
                        if next_node == self.end_node:
                            return True

                        que.append(next_node)

                    idx = ne[idx]

            return False

        # dfs查找增广路, 返回当前残量图上node节点能流入汇点的不超过limit的最大流量有多事少
        def dfs(node, limit) -> int:
            if node == self.end_node:
                return limit

            flow = 0
            idx = cur[node]  # 从节点的当前弧开始搜索下一个点
            while idx != -1 and flow < limit:
                # 当前弧优化，记录每一个节点最后走的一条边，只要limit还没有减成0，已经搜过的边的终点
                # 能够汇入汇点的流量就已经全部用完了，另外一条路径到同一个点时候没必要重复搜索已经不会
                # 再提供流量贡献的邻接点
                cur[node] = idx

                next_node = e[idx]
                if dis[next_node] == dis[node] + 1 and f[idx] > 0:
                    t = dfs(next_node, min(f[idx], limit - flow))
                    if t == 0:
                        # 已经无法提供流量的废点闪删除掉，不再参与搜索
                        dis[next_node] = -1

                    # 更新残量图边的流量
                    f[idx], f[idx ^ 1], flow = f[idx] - t, f[idx ^ 1] + t, flow + t

                    # 更新原图边的流量
                    if self.edges[idx >> 1][0] == node:
                        orig_flow[idx >> 1] += t
                    else:
                        orig_flow[idx >> 1] -= t

                idx = ne[idx]

            return flow


        while bfs():
            # 只要还有增广路，就dfs把增广路都找到，把增广路上的流量加到可行流上
            dfs(self.source_node, 0x7fffffff)

        # 筛选满流的边
        candi_e = []
        for i in range(len(self.edges)):
            if self.edges[i][2] == orig_flow[i]:
                candi_e.append(i)

        s_reachable = set() # S能够到的点
        t_reachable = set() # 能够到T的点

        # 从S和T分别正向和反向搜索能够到的点
        def travel(cur, visit):
            visit[cur] = 1
            s_reachable.add(cur)

            idx = h[cur]
            while idx != -1:
                next_node = e[idx]
                if visit[next_node] == 0 and f[idx] > 0:
                    travel(next_node, visit)

                idx = ne[idx]

        def rev_travle(cur, visit):
            visit[cur] = 1
            t_reachable.add(cur)

            idx = h[cur]
            while idx != -1:
                next_node = e[idx]
                if visit[next_node] == 0 and f[idx] != self.edges[idx>>1][2]:
                    # 方向边没有满流，等价于正向边还有流量可以用，next_node就可以沿着正向边到cur
                    rev_travle(next_node, visit)

                idx = ne[idx]

        travel(self.source_node, [0] * (self.max_node_num + 1))
        rev_travle(self.end_node, [0] * (self.max_node_num + 1))

        ans = []
        for edge_idx in candi_e:
            a, b, _ = self.edges[edge_idx]
            if a in s_reachable and b in t_reachable:
                ans.append(edge_idx)
        return ans


n, m = map(int, input().split())
e = []
for _ in range(m):
    a, b, w = map(int, input().split())
    a, b = a + 1, b + 1
    e.append((a, b, w))

algo = FortdFulkerson(e, 1, n, max_node_num=n, max_edge_num=len(e))
print(len(algo.getKeyEdges()))