我试图在训练后从 Keras 模型中获取每个批次/时期的权重。为此，我使用回调使模型在训练期间保存权重。然而，在模型训练之后，看起来我只从最后一个时期获得权重。如何获得模型生成的所有权重？这是一个简单的例子：
import numpy as np
import tensorflow as tf
from tensorflow import keras
from keras import layers
# Generate data
start, stop = 1,100
cnt = stop - start + 1
xs = np.linspace(start, stop, num = cnt)
b,k = 1,2
ys = np.array([k*x + b for x in xs])
# Simple model with one feature and one unit for regression task
model = keras.Sequential([
layers.Dense(units=1, input_shape=[1], activation='relu')
])
model.compile(loss='mae', optimizer='adam')
batch_size = int(cnt / 5)
epochs = 80
接下来是回调，以某种频率保存 Keras 模型权重。根据 Keras 文档：
save_freq：“纪元”或整数。使用 'epoch' 时，回调应在每个 epoch 后保存模型。使用整数时，回调应在这么多批次结束时保存模型。
checkpoint_filepath = './checkpoint.hdf5'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
save_weights_only=True,
save_freq ='epoch', # 1 for every batch
save_best_only=False
)
# Train model
history = model.fit(xs, ys, batch_size=batch_size, epochs=epochs,
callbacks=[model_checkpoint_callback])
我使用两种不同的方法来获得权重。第一的：
w, b = model.weights
print("Weights: \n {} \n Bias: \n {}".format(w,b))
Weights:
<tf.Variable 'dense/kernel:0' shape=(1, 1) dtype=float32, numpy=array([[-0.1450262]], dtype=float32)>
Bias:
<tf.Variable 'dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>
这导致一个权重和一个偏差，而不是模型在每个批次/时期生成的所有权重。
第二种直接从 h5 文件中获取权重的方法：
# Functions to read weights from h5 file
import h5py
def getH5Keys(fileName):
keys = []
with h5py.File(fileName, mode='r') as f:
for key in f:
keys.append(key)
return keys
def isGroup(obj):
if isinstance(obj, h5py.Group):
return True
else:
return False
def isDataset(obj):
if isinstance(obj, h5py.Dataset):
return True
else:
return False
def getDataSetsFromGroup(datasets, obj):
if isGroup(obj):
for key in obj:
x = obj[key]
getDataSetsFromGroup(datasets, x)
else:
datasets.append(obj)
def getWeightsForLayer(layerName, fileName):
weights = []
with h5py.File(fileName, mode='r') as f:
for key in f:
if layerName in key:
obj = f[key]
datasets = []
getDataSetsFromGroup(datasets, obj)
for dataset in datasets:
w = np.array(dataset)
weights.append(w)
return weights
此方法为一个权重和一个偏差返回相同的奇异值：
layers = getH5Keys(checkpoint_filepath)
firstLayer = layers[0]
print(layers) # ['dense']
weights = getWeightsForLayer(firstLayer, checkpoint_filepath)
for w in weights:
print(w.shape)
print(weights)
输出：
(1,)
(1, 1)
[array([0.], dtype=float32), array([[-0.1450262]], dtype=float32)]
同样，我只得到一个权重和一个偏差。如何获取模型为每个批次/时期生成的所有权重？
更新
10xAI为我工作的答案。但是，在我的情况下，我有一个具有一个单元的网络级别，因此我以不同的方式访问权重和偏差：
weights_dict = {}
weight_callback = tf.keras.callbacks.LambdaCallback \
( on_epoch_end=lambda epoch, logs: weights_dict.update({epoch:model.get_weights()}))
# Train model
history = model.fit(xs, ys, batch_size=batch_size, epochs=epochs,
callbacks=[weight_callback])
print(weights_dict[0])
Output: [array([[1.5375139]], dtype=float32), array([0.00499998], dtype=float32)]
print("*** Epoch: ", epoch, "\nWeight: ", weights_dict[0][0][0], " bias: ", weights_dict[1][0])
Output: *** Epoch: 79
Weight: [1.5375139] bias: [[1.5424858]]
