我在尝试加载模型时陷入困境。我已经对其进行了两个时期的训练，获得了两个保存的检查点，每个时期之后一个，但我在尝试加载模型时遇到了困难。
这是我在尝试使用加载模型时遇到的错误
model = load_model('my_chunkedmodel_vertical_epoch_01.h5')
Traceback (most recent call last):
File "/tmp/ipykernel_682950/3467434784.py", line 1, in <module>
model = load_model('my_chunkedmodel_vertical_epoch_01.h5')
File "/home/ceausexu/anaconda3/envs/treisapte/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ceausexu/anaconda3/envs/treisapte/lib/python3.7/site-packages/ncps/tf/ltc_cell.py", line 347, in from_config
return cls(wiring=wiring, **config)
File "/home/ceausexu/anaconda3/envs/treisapte/lib/python3.7/site-packages/ncps/tf/ltc_cell.py", line 64, in __init__
super().__init__(**kwargs)
TypeError: ('Keyword argument not understood:', 'adjacency_matrix')"
这是到目前为止的脚本：
import numpy as np
import gc
import time
import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objects as go
from sklearn.preprocessing import MinMaxScaler
from keras import backend as K
from keras.callbacks import ModelCheckpoint
from keras.models import Model
from keras.layers import Input, LSTM, Conv1D, Dense, Dropout, Flatten, concatenate
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
from keras.preprocessing.sequence import TimeseriesGenerator
from keras.callbacks import ReduceLROnPlateau
from ncps.tf import LTC # or from ncps.tf import CfC based on what you want
from ncps.wirings import AutoNCP # for custom wiring
# Custom DoubleInputTimeseriesGenerator for hybrid models
class DoubleInputTimeseriesGenerator(TimeseriesGenerator):
def __getitem__(self, index):
X, y = super().__getitem__(index)
print(f"Shape of y in Data Generator: {y.shape}") # Debug line
return [X, X], y # Return X twice for the two inputs
##checkpoint save after epoch
checkpoint_filepath = "my_chunkedmodel_vertical_epoch_{epoch:02d}.h5"
checkpoint_callback = ModelCheckpoint(
filepath=checkpoint_filepath,
save_best_only=False, # Set to True if you only want to save the model if it's "better" (based on validation loss)
monitor='loss', # What metric to monitor (you can change this to val_loss if you have validation data)
verbose=1, # Verbosity mode, 1 means it will print the epoch number when saving
save_weights_only=False, # If True, then only the model's weights will be saved, else the full model is saved
mode='auto', # The decision to overwrite the current save file is made based on the monitored quantity
save_freq='epoch' # Save the model after each epoch
)
# Wiring configuration
wiring = AutoNCP(50, 5) # 50 neurons, 5 outputs
# Model architecture
ncp_input = Input(shape=(None, 6)) # 6 features
x = LTC(wiring)(ncp_input) # using wiring, no need to specify neurons here
x = Dropout(0.2)(x)
x = Dense(5)(x) # 5 outputs
# Create the model
model = Model(inputs=ncp_input, outputs=x)
# Compile the model
model.compile(
optimizer=Adam(learning_rate=0.001),
loss='mean_squared_error',
metrics=['mean_absolute_error']
)
# Load the entire training dataset
dataset_train = pd.read_csv('train_set.csv')
training_set = dataset_train.iloc[:, [1,2,3,4,5,8]].values
# Load the evaluation dataset
dataset_eval = pd.read_csv('evaluation_set.csv')
evaluation_set = dataset_eval.iloc[:, [1,2,3,4,5,8]].values
# Feature scaling
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)
evaluation_set_scaled = sc.transform(evaluation_set)
# Define a separate scaler for 'Open' price
sc_open = MinMaxScaler(feature_range = (0, 1))
training_set_open_scaled = sc_open.fit_transform(training_set[:, 0].reshape(-1, 1))
evaluation_set_open_scaled = sc_open.transform(evaluation_set[:, 0].reshape(-1, 1))
# Define Params for the generator
lookback = 300
batch_size = 10
# Use standard TimeseriesGenerator for training and evaluation
train_gen = TimeseriesGenerator(training_set_scaled, training_set_open_scaled, length=lookback, batch_size=batch_size)
eval_gen = TimeseriesGenerator(evaluation_set_scaled, evaluation_set_open_scaled, length=lookback, batch_size=batch_size)
# Learning rate reducer
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=6, min_lr=0.0001)
# Train the modele
model.fit(train_gen, epochs=9, callbacks=[checkpoint_callback, reduce_lr], validation_data=eval_gen)
我认为，这只是一个加载问题，所以我尝试从保存的 h5 检查点文件中提取 LTC 层和密集层的唯一权重（由于数据集大小，每个周期需要一周时间） 。欢迎其他解决方法或解决方案！
以下是迄今为止的修改，实现了 LTC 层内形状的提取和匹配：
import h5py
# Open the .h5 file
filename = 'my_chunkedmodel_vertical_epoch_01.h5'
with h5py.File(filename, 'r') as f:
# Access the 'model_weights' group
model_weights_group = f['model_weights']
# Access the 'dense' and 'ltc' groups and list the keys in each
dense_group = model_weights_group['dense/dense']
ltc_cell_group = model_weights_group['ltc/ltc/ltc_cell']
# Print the keys in the 'dense' group
print("Keys in 'dense' group: ", list(dense_group.keys()))
# Print the keys in the 'ltc_cell' group
print("Keys in 'ltc_cell' group: ", list(ltc_cell_group.keys()))
# Extract and save the weights from the 'dense' group
dense_kernel_weights = dense_group['kernel:0'][()]
dense_bias_weights = dense_group['bias:0'][()]
np.save('dense_kernel_weights.npy', dense_kernel_weights)
np.save('dense_bias_weights.npy', dense_bias_weights)
# Extract and save the weights and parameters from the 'ltc_cell' group
for key in ltc_cell_group.keys():
weight = ltc_cell_group[key][()]
np.save(f'ltc_cell_{key}.npy', weight)
print("Weights from the 'dense' group and 'ltc_cell' group have been saved to numpy files.")
# Print current shapes of the weights in the LTC layer to match the order when loading
ltc_layer = model.layers[1].cell
current_weights = ltc_layer.get_weights()
for weight in current_weights:
print(weight.shape)
# For the LTC layer
ltc_cell_weights = [
np.load('ltc_cell_cm:0.npy'),
np.load('ltc_cell_gleak:0.npy'),
np.load('ltc_cell_vleak:0.npy'),
np.load('ltc_cell_erev:0.npy'),
np.load('ltc_cell_mu:0.npy'),
np.load('ltc_cell_sigma:0.npy'),
np.load('ltc_cell_w:0.npy'),
np.load('ltc_cell_sensory_erev:0.npy'),
np.load('ltc_cell_sensory_mu:0.npy'),
np.load('ltc_cell_sensory_sigma:0.npy'),
np.load('ltc_cell_sensory_w:0.npy'),
np.load('ltc_cell_input_b:0.npy'),
np.load('ltc_cell_input_w:0.npy'),
np.load('ltc_cell_output_b:0.npy'),
np.load('ltc_cell_output_w:0.npy'),
]
model.layers[1].cell.set_weights(ltc_cell_weights)
# For the dense layer
dense_kernel_weights = np.load('dense_kernel_weights.npy')
dense_bias_weights = np.load('dense_bias_weights.npy')
model.layers[3].set_weights([dense_kernel_weights, dense_bias_weights])