我正在尝试使用Keras顺序模型训练过的权重来预测值，但是，我需要尽可能轻。使用这些权重的数学步骤是什么？
我对机器学习模型及其背后的数学有一个大致的了解，但是我似乎对Keras如何在Dense模型中节省或使用其权重有些了解。我有一个顺序模型，在输入层之后有3层，结构如下：
Layer (type) Output Shape Param Activation
=============================================================================
dense_1 (Dense) (None, 32) 128 selu
_____________________________________________________________________________
dense_2 (Dense) (None, 16) 528 selu
_____________________________________________________________________________
dense_3 (Dense) (None, 1) 17 None
=============================================================================
在HDF5文件中，密集_1出现如下；
GROUP "dense_1" {
DATASET "bias:0" {
DATATYPE H5T_IEEE_F32LE
DATASPACE SIMPLE { ( 32 ) / ( 32 ) }
DATA {
(0): -1.94451, 1.01336, -0.331715, -0.113848, 1.62048,
(5): -0.048518, -0.354262, 0.0446166, -0.589025, 0.52462,
(10): -0.65492, -1.31858, 0.655622, 0.105934, -0.211289,
(15): -0.707541, 0.136989, -0.780716, 0.848544, -0.243017,
(20): 0.573721, -0.486272, -0.341983, -1.13598, 0.319228,
(25): -0.251834, -2.36574, -0.162145, -1.39944, -0.121933,
(30): -0.31354, -2.03999
}
}
DATASET "kernel:0" {
DATATYPE H5T_IEEE_F32LE
DATASPACE SIMPLE { ( 3, 32 ) / ( 3, 32 ) }
DATA {
(0,0): -0.0363489, -0.710178, 0.235509, -1.05998, -0.219359,
(0,5): 0.409596, -0.979901, -0.956889, -0.760354, -0.666067,
(0,10): -0.984245, -0.0354558, -0.349002, 0.00105414,
(0,14): 0.116752, -1.15233, 0.0275432, 0.215353, -0.678382,
(0,19): -1.30734, -0.607154, 0.339789, -1.15283, 0.264189,
(0,24): -0.6365, -1.06603, 0.626251, 0.511415, 0.211209,
(0,29): 0.570231, -1.12093, 0.0427848,
(1,0): 0.499443, 0.919749, 0.211407, -0.650205, 0.0994867,
(1,5): -0.193579, -0.927313, -0.663942, -0.971027, 0.646805,
(1,10): -0.891204, 0.432123, 0.459246, 0.0648564, -0.338207,
(1,15): -0.926523, -0.0511654, 0.18302, 0.676123, -0.394053,
(1,20): 0.520645, 0.289941, -0.760597, -0.309942, 0.477148,
(1,25): -0.769285, -0.885901, 0.370343, -0.111789, -0.117417,
(1,30): -0.534311, 0.466348,
(2,0): -0.635124, 0.0613677, 0.10305, -0.635547, -0.00577374,
(2,5): 0.0560383, -0.496999, -1.10678, -1.1172, 0.686428,
(2,10): -1.04004, 0.569042, -0.285685, 1.24021, -0.602061,
(2,15): 0.628718, 0.213643, -0.552893, 0.648553, -0.864449,
(2,20): 1.21918, 0.185123, -0.415593, -0.187327, 1.11334,
(2,25): -0.0186234, -0.192697, 0.3651, -0.964619, 0.201066,
(2,30): -1.03416, 0.514029
}
}
}
我对机器学习模型的理解使我相信，要计算第一层中节点0的输出，我将执行以下操作： bias（0）*（（Input0 *内核（0,0））+（Input1 *内核（1,0））+ Input2 *内核（2,0））
import h5py
import pandas as pd
import numpy as np
import sys
T, D, Y = 9, 5, -0.3#sys.argv[1:4]
def selu(vals):
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
for i in range(len(vals)):
if vals[i] < 0:
vals[i] = np.e ** vals[i]
return scale * vals
file = h5py.File('file.h5', 'r') #load h5 file
layers = list(file.keys()) #get keys
model = model[layers[0]] #get model
layers = list(model.keys()) #get layer keys
biases, kernals = [], []
for layer in layers:
layer_weights = model[layer][layer]
sub = list(layer_weights.keys())
bias = layer_weights[sub[0]][:]
kernal = layer_weights[sub[1]][:]
biases.append(bias)
kernals.append(kernal)
_in = [T, D, Y]
for i in range(len(kernals)):
# print("Layer {}".format(i))
_sum = np.zeros(len(biases[i]))
for j in range(len(kernals[i])):
_cross = np.float64(_in[j]) * np.float64(kernals[i][j])
_sum += _cross
if i < len(kernals)-1:
_vals = selu(_sum)
else:
_vals = _sum
_out = _vals * biases[i]
_in = _out
print(_out)
如果有人可以解释或指出我的发展道路，那么价值将通过这样的模型来体现，我将非常感激。