tf.reduce_mean( input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None ) 定义于:tensorflow/python/ops/math_ops.py 请参考:Math > Reduction 计算一个张量不同维度上的平均值。(不推荐使用的参数) 某些参数已被弃用。它们将在未来版本中被删除。更新说明:不推荐使用...
loss = tf.reduce_mean(tf.losses.mean_absolute_percentage_error(label, predict)) return loss 1. 2. 3. 4. 5. MSLE 均方对数误差 (mean_squared_logarithmic_error) # Mean Squared Logarithmic Error (MSLE) def getMsleLoss(predict, label): loss = tf.reduce_mean(tf.losses.mean_squared_logarit...
import tensorflow as tf # 假设我们有一些预测值和真实值 y_pred = tf.constant([2.5, 0.0, 2, 8]) y_true = tf.constant([3, -0.5, 2, 7]) # 计算损失 loss = tf.keras.losses.MSE(y_true, y_pred) print(tf.reduce_mean(loss).numpy()) 三、平均绝对误差 (Mean Absolute Error, MAE) ...
tf.reduce_mean(x,0) > <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1.5, 1.5], dtype=float32)> tf.reduce_mean(x,1) > <tf.Tensor: shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)> tf.reduce_mean(x,-1) > <tf.Tensor: shape=(2,), dtype=float...
losses=tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(ys,num_classes),#将input转化为one-hot类型数据输出 logits=logits)# 平均损失 mean_loss=tf.reduce_mean(losses)# 定义优化器 学习效率设置为0.0001optimizer=tf.train.
one_hot_labels0=tf.one_hot(indices=tf.cast(y0, tf.int32), depth=CHAR_SET_LEN) # loss0=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits0, labels=one_hot_labels0)) # total_loss=loss0 # optimizer=tf.train.AdamOptimizer(learning_rate=lr).minimize(total_loss) ...
returntf_contrib.layers.batch_norm(x,decay=0.9,epsilon=1e-05,center=True,scale=True,scope=scope)defflatten(x):returntf.layers.flatten(x)deflrelu(x,alpha=0.2):returntf.nn.leaky_relu(x,alpha)defrelu(x):returntf.nn.relu(x)defglobal_avg_pooling(x):gap=tf.reduce_mean(x,axis=[1,2],...
tf.reduce_mean()函数用于计算张量tensor沿着指定的数轴(tensor的某一维度)上的平均值,主要用作降维或者计算tensor(图像)的平均值。 tf.reduce_mean( input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None ) 参数: input_tensor: 输入的待降维的tensor ...
loss=tf.reduce_mean(tf.square(y-y_data)) #因为有误差,所以建立一个神经网络,用神经网络优化误差,也就是误差优化器,减少误差 optimizer=tf.train.GradientDescentOptimizer(0.5)#0.5是learing rate,一般来说小于1 train=optimizer.minimize(loss) init=tf.initialize_all_variables()#初始化神经网络 ...
# 初始化权重和偏置项w = tf.Variable(0.0)b = tf.Variable(0.0) # 定义线性回归模型deflinear_regression(x):returnw * x + b # 定义损失函数defloss_fn(y_true, y_pred):returntf.reduce_mean(tf.square(y_true - y_pred)) # 设置优化器optimizer = tf.optimizers.S...