nn.batch_normalization(layer, batch_mean, batch_variance, beta, gamma, epsilon) def batch_norm_inference(): return tf.nn.batch_normalization(layer, pop_mean, pop_variance, beta, gamma, epsilon) batch_normalized_
with tf.name_scope("conv"):#batch norm之后在激活,所以这里不设定激活函数conv1 = tf.layers.conv2d(X_reshaped, filters=conv1_fmaps, kernel_size=conv1_ksize, strides=conv1_stride, padding=conv1_pad, activation=None, name="conv1")#进行batch norm之后,再激活batch_norm1 =tf.nn.selu(my_ba...
with tf.name_scope("conv"):#batch norm之后在激活,所以这里不设定激活函数conv1 = tf.layers.conv2d(X_reshaped, filters=conv1_fmaps, kernel_size=conv1_ksize, strides=conv1_stride, padding=conv1_pad, activation=None, name="conv1")#进行batch norm之后,再激活batch_norm1 =tf.nn.selu(my_ba...
affine=True,name=None):withtf.variable_scope(name,default_name='BatchNorm2d'):params_shape=tf.s...
检查数据流（数据增强是否合理）监控梯度范数（torch.nn.utils.clip_grad_norm_）学习率动态调整（Reduce...
Batch Normalization: 使用 tf.layers 高级函数来构建神经网络 参考文献 吴恩达 deeplearningai 课程[1] 课程笔记[2] Udacity 课程[3] 批量标准化在构建深度神经网络时最为有用。为了证明这一点,我们将创建一个具有20个卷积层的卷积神经网络,然后是一个完全连接的层。我们将使用它来对MNIST数据集中的手写数字进行...
def batch_norm(x, train, eps=1e-05, decay=0.9, affine=True, name=None): with tf.variable_scope(name, default_name='BatchNorm2d'): params_shape = x.get_shape().as_list() params_shape = params_shape[-1:] moving_mean = tf.get_variable('mean', shape=params_shape, ...
# torch.nn.MaxPool2d(kernel_size, stride, padding) # input 維度 [3, 128, 128] # 卷积网络 self.cnn = nn.Sequential( nn.Conv2d(3, 64, 3, 1, 1), # [64, 128, 128] nn.BatchNorm2d(64), # 归一化 nn.ReLU(), nn.MaxPool2d(2, 2, 0), # [64, 64, 64] ...
Tensorflow系列:tf.contrib.layers.batch_norm tf.contrib.layers.batch_norm( inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, param_regularizers=None, updates_collections=tf.GraphKeys.UPDATE_OPS,...
注意,我们正在使用具有卷积层,BatchNormalization和MaxPooling 层的顺序模型。 倒数第二层使结构变平,最后一层使用 softmax 激活,因此我们预测的类将显示为具有最高激活的输出神经元: model = Sequential() model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), inp...