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python - CNN 为所有输入数据预测相同的类别

问题描述

我正在尝试在 Keras 中重新创建一个 CNN 来对点云数据进行分类。本文描述了 CNN 。

网络设计 图像

这是我目前的实现：

inputs = Input(shape=(None, 3))

x = Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)
x = BatchNormalization()(x)
x = Conv1D(filters=64, kernel_size=1, activation='relu')(x)
x = BatchNormalization()(x)

y = Conv1D(filters=64, kernel_size=1, activation='relu')(x)
y = BatchNormalization()(y)
y = Conv1D(filters=128, kernel_size=1, activation='relu')(y)
y = BatchNormalization()(y)
y = Conv1D(filters=2048, kernel_size=1, activation='relu')(y)
y = MaxPooling1D(1)(y)

z = keras.layers.concatenate([x, y], axis=2)
z = Conv1D(filters=512, kernel_size=1, activation='relu')(z)
z = BatchNormalization()(z)
z = Conv1D(filters=512, kernel_size=1, activation='relu')(z)
z = BatchNormalization()(z)
z = Conv1D(filters=512, kernel_size=1, activation='relu')(z)
z = BatchNormalization()(z)
z = Dense(9, activation='softmax')(z)

model = Model(inputs=inputs, outputs=z)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

问题是网络为所有输入数据预测相同的类别。这可能是由于我的网络实现错误、过拟合或训练数据不足造成的。有人可以在我的实施中发现错误吗？

Yousefhussien, M., Kelbe, DJ, Ientilucci, EJ, & Salvaggio, C. (2017)。用于 3D 点云语义标记的全卷积网络。arXiv 预印本 arXiv:1710.01408。

标签： pythontensorflowneural-networkkerasconv-neural-network

解决方案

网络是固定的，因为它现在提供了预期的预测。谢谢您的帮助！

根据答案，我更改了以下内容：

  • 激活的顺序和批量标准化。
  • 最后一层从密集层到卷积层。

我还将training=True参数添加到批量标准化层

正确实现的代码：

inputs = Input(shape=(None, 3))

x = Conv1D(filters=64, kernel_size=1, input_shape=(None, 4))(inputs)
x = BatchNormalization()(x, training=True)
x = Activation('relu')(x)
x = Conv1D(filters=64, kernel_size=1, use_bias=False)(x)
x = BatchNormalization()(x, training=True)
x = Activation('relu')(x)

y = Conv1D(filters=64, kernel_size=1)(x)
y = BatchNormalization()(y, training=True)
y = Activation('relu')(y)
y = Conv1D(filters=128, kernel_size=1)(y)
y = BatchNormalization()(y, training=True)
y = Activation('relu')(y)
y = Conv1D(filters=2048, kernel_size=1)(y)
y = BatchNormalization()(y, training=True)
y = Activation('relu')(y)
y = MaxPooling1D(1)(y)

z = keras.layers.concatenate([x, y], axis=2)
z = Conv1D(filters=512, kernel_size=1)(z)
z = BatchNormalization()(z, training=True)
z = Activation('relu')(z)
z = Conv1D(filters=512, kernel_size=1)(z)
z = BatchNormalization()(z, training=True)
z = Activation('relu')(z)
z = Conv1D(filters=512, kernel_size=1)(z)
z = BatchNormalization()(z, training=True)
z = Activation('relu')(z)
z = Conv1D(filters=2, kernel_size=1, activation='softmax')(z)

model = Model(inputs=inputs, outputs=z)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

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