python - 如何理解 Keras 中简单神经网络 Python 代码的密集层参数

Question

import numpy as np
    from keras.models import Sequential
    from keras.layers.core import Dense, Activation

    # X has shape (num_rows, num_cols), where the training data are stored
    # as row vectors
    X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)

    # y must have an output vector for each input vector
    y = np.array([[0], [0], [0], [1]], dtype=np.float32)

    # Create the Sequential model
    model = Sequential()

    # 1st Layer - Add an input layer of 32 nodes with the same input shape as
    # the training samples in X
    model.add(Dense(32, input_dim=X.shape[1]))

    # Add a softmax activation layer
    model.add(Activation('softmax'))

    # 2nd Layer - Add a fully connected output layer
    model.add(Dense(1))

    # Add a sigmoid activation layer
    model.add(Activation('sigmoid'))

我是 Keras 新手，正在尝试理解它。

model.add(Dense(32, input_dim=X.shape[1])) 其中 32 表示每个训练实例有 32 个输入变量，其尺寸由input_dim给出。但在输入 X 向量中，

array([[0., 0.],
       [0., 1.],
       [1., 0.],
       [1., 1.]], dtype=float32)

有 4 个训练实例。看起来每个示例都只有两个输入变量。那么这与密集层定义中的“32”是如何对应的呢？这个网络是什么样的？

Answer 1

如果你尝试

model.summary()

您将得到上一个问题的答案。

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 32)                96        
_________________________________________________________________
activation_1 (Activation)    (None, 32)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 33        
_________________________________________________________________
activation_2 (Activation)    (None, 1)                 0         
=================================================================
Total params: 129
Trainable params: 129
Non-trainable params: 0
_________________________________________________________________

网络输入是2个节点(变量)，它们与dense_1层(32个节点)连接。总共 32*2 权重 + 32 个偏差为您提供 96 个参数。希望这会有所帮助。