实时的显示相关数据的图
import tensorflow as tf import datetime def preporocess(x,y): x = tf.cast(x,dtype=tf.float32) / 255 x = tf.reshape(x,(-1,28 *28)) # 铺平 x = tf.squeeze(x,axis=0) # print('里面x.shape:',x.shape) y = tf.cast(y,dtype=tf.int32) return x,y def main(): # 加载手写数字数据 mnist = tf.keras.datasets.mnist (train_x, train_y), (test_x, test_y) = mnist.load_data() # 处理数据 # 训练数据 db = tf.data.Dataset.from_tensor_slices((train_x,train_y)) # 将x,y分成一一对应的元组 db = db.map(preporocess) # 执行预处理函数 db = db.shuffle(60000).batch(2000) # 打乱加分组 # 测试数据 db_test = tf.data.Dataset.from_tensor_slices((test_x,test_y)) db_test = db_test.map(preporocess) db_test = db_test.shuffle(10000).batch(10000) # 设置超参 iter_num = 2000 # 迭代次数 lr = 0.01 # 学习率 # 定义模型器和优化器 model = tf.keras.Sequential([ tf.keras.layers.Dense(256,activation='relu'), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(10) ]) # model.build(input_shape=[None,28*28]) # 事先查看网络结构 # model.summary() # 优化器 # optimizer = tf.keras.optimizers.SGD(learning_rate=lr) optimizer = tf.keras.optimizers.Adam(learning_rate=lr) # 迭代训练 db_iter = iter(db) for i in range(iter_num): for step,(x,y) in enumerate(db): with tf.GradientTape() as tape: logits = model(x) y_onehot = tf.one_hot(y,depth=10) # loss = tf.reduce_mean(tf.losses.MSE(y_onehot,logits)) # 差平方损失 loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot,logits,from_logits=True)) # 交叉熵损失 grads = tape.gradient(loss,model.trainable_variables) # 梯度 grads,_ = tf.clip_by_global_norm(grads,15) # 梯度限幅 optimizer.apply_gradients(zip(grads,model.trainable_variables)) # 更新参数 if step % 10 == 0: # 将数据写入log文件 with summary_writer.as_default(): tf.summary.scalar('loss', float(loss), step=step) pass # print('i:{} , step:{} , loss:{} '.format(i,step,loss)) # 计算测试集准确率 for (x,y) in db_test: logits = model(x) out = tf.nn.softmax(logits,axis=1) pre = tf.argmax(out,axis=1) pre = tf.cast(pre,dtype=tf.int32) print(pre.shape,y.shape) acc = tf.equal(pre,y) acc = tf.cast(acc,dtype=tf.int32) acc = tf.reduce_mean(tf.cast(acc,dtype=tf.float32)) print('i:{}'.format(i)) print('acc:{}'.format(acc)) # ************************** 将数据写入log文件 *********************************** with summary_writer.as_default(): tf.summary.scalar('acc', float(acc), step=i) if __name__ == '__main__': # ***************************** tensorboard文件处理 ******************************* current_time = datetime.datetime.now().strftime('%Y%m%d-%H%M%S') # 当前时间 # print('当前时间：',current_time) log_dir = 'tb_data/logs/' + current_time # 以当前时间作为log文件名 summary_writer = tf.summary.create_file_writer(log_dir) # 创建log文件 main()
